Therefore, the use of the h-index seems justifed, as it addresses the issue of outliers (i.e. highly popular tweets) as well as the lifespan of accounts (immature, yet highly popular accounts) and provides a weighted rank of signifcant contributions. To put it simply, the h-index algorithm fnds an "ideal point" between the number of contributions and their relative popularity, which for Twitter can be considered as the sum of "likes" and "retweets" for each users' post (*hirsch method*(*Favorites* + *Retweets*)). All leaders were ranked according to the obtained indices and the resultant list was compared with the ranked list of leaders, obtained through the index method proposed by this research. Spearman's rank correlation coeffcient (*ρ*) was utilized to establish whether both methods were concordant. It demonstrated a high correlation coeffcient of 0.69 between the proposed infuence index (2) and the modifed h-index (3). Notably, this coeffcient was calculated when the h-index did not refer to the centrality parameter of each leader account. Including the centrality indicator increased the correlation coeffcient to 0.80.

As a ranking algorithm, the h-index provides a useful method for establishing the most infuential contributors and can be used for ranking leaders. It also confrms the validity of the proposed time-invariant infuence rank (2). As any other methods, the h-index for Twitter is not without defciencies and potentially may not be used for samples where leaders are highly popular and produce a large quantity of tweets. As the Twitter REST API limits access to 3200 most recent posts, the h-index will not be able to produce an index higher than the quantity of posts. Yet in the case of current measurements of Russian Twitter, this issue was not a problem, as the most popular user—Alexei Navalny—scored only 902 points on the scale. Furthermore, both indices (2 and 3) are consistent and consonant with common wisdom that the actual disposition of actors and organizations within the political arena should be correlated with their political infuence.

## 30.4 Beyond the Score: Cross Validation of Detected Patterns

#### *30.4.1 Further Validating "Echo Chambers"*

Focusing on intra- and cross-community conversation, we observed homophilous conversation patterns between the various communities as users tended to share content with like-minded individuals inside their own community, most especially between the pro-government and Opposition groups. Nominal homophily however can be misleading as users in small communities are more likely to converse across community lines simply because their community is small, and users in large communities are unlikely to converse outside their group due to its relative proportion. We adopt a method developed by Currarini et al. (2007) to validate the nominal homophily observed. Specifcally, nominal homophily, or the proportion of conversations a community has within itself (*Hi*) is compared to its relative size within the network (*wi*).

$$\mathbf{(4)}H\_i = \mathbf{w\_i; (\mathbf{5})}H\_i > \mathbf{w\_i; (\mathbf{6})}H\_i < \mathbf{w\_i}$$

Baseline homophily (4) occurs when the proportion of user conversations within a community equals the relative size of the community, indicating that on aggregate, users in that community show no special preference or bias for their own friends. Inbreeding homophily (5) indicates that users are biased and converse more often within their own group than is expected on the basis of its relative demographic size. Finally, if a community shows heterophilous patterns (6), the number of conversations within the group will be less than the relative size of the group.9 To enable comparisons between communities of various sizes and different conversation types on Twitter, we standardize homophily indicators for (7) baseline homophily, (8) inbreeding homophily, and (9) heterophily.

$$\mathbb{P}\left(\mathcal{T}\right)\frac{H\_{\iota}}{\omega\_{\iota}} = 1; \left(8\right)\frac{H\_{\iota}}{\omega\_{\iota}} > 1; \left(9\right)\frac{H\_{\iota}}{\omega\_{\iota}} < 1$$

Investigating standardized homophily indicators ( *H w i i* ), we fnd that each community demonstrated strong in breeding homophily (Table 30.4). Interestingly, the non-systemic opposition is more homophilous than the progovernment community. A few communities, such as communities 3 and 4, while quite small, demonstrated excessively high standardized homophily indicators.

**Table 30.4** Relative community sizes and standardized homophily indicators

### *30.4.2 Makeup of Two Main Political Communities and Their Reactions to Political Events*

Individually assessing the pro-government community, we observe that it was by far the largest political group, always actively participating in all events. The community displayed strong pro-Putin, pro-government, anti-western (including anti-US [United States], anti-Europe and anti-Ukraine), and, in some instances even nationalist sentiment. While sometimes a little critical of the regime, the users in this community (community 0 or purple in Fig. 30.1 above) generally disseminated information in line with a patriotic narrative and demonstrated two patterns of Twitter use. If the informational event was not negative to Russia or the government, such as the two-year anniversary of the accession of Crimea as part of the Russian Federation, then reactions were usually event-specifc and generally positive. However, if the informational event was inherently negative to the government, reaction was usually split between a certain proportion of anti-government content, and neutral or positive progovernment reaction. In certain cases, a pattern is evident whereby positive content was coordinated around specifc keywords that were trending negatively in order to coopt the term and spin it positively, distracting the conversation to unrelated pro-government content.

Reactions to Prime Minister Medvedev's comment of "there is no money, but you hang in there" to Crimean pensioners, posted to YouTube on May 23, 2016, demonstrate these two patterns well (Fig. 30.5). While some users derided the Prime Minister's comments, factual and neutral reactions were quite prevalent. A large amount of disseminated content focused on unrelated positive topics to distract and mitigate the initial negative reaction inside the community. Two stories were widely mentioned on May 23 and 24 focusing on specifc keywords. The frst focused on the word "money" by distributing a story on the Prime Minister promising to fnd money for museums in Crimea. The second focused on the terms "economy" and "investment," disseminating content about the release of a government plan, approved by the Prime Minister, aiming to increase domestic demand for the products of Russian chemical and petrochemical industries. Other reactions also included the factual reporting of the Prime Minister's comments or presenting the information in a neutral fashion, with tweets such as "Medvedev admitted that there is no money to index pensions."10 Interestingly, such neutral posts usually did not include links and were composed of just text.

The solid line indicates the number of tweets on an hourly basis (right axis) in the pro-government community, and the dashed line indicates the

**Fig. 30.5** Pro-government community reaction to Medvedev's comment to pensioners in Crimea

proportion of conversation (i.e. of the tweets and retweets made during that hour) that had to do with Medvedev's comment (left axis). Tweets containing "money," "hang in there," "pensioners," "have a good day" ("*deneg*," "*den'gi*," "*deržites*ʹ," "*pensii*," "*pensij*," "*nastroenie*") were used to calculate the proportion. Specifcally, lemmatized words in each tweet were checked against the lemmas of desired keywords. Tweets that contained keywords known to be used in the counter strategy were excluded.

The second of the two main political groups, the Opposition community (community 2, marked teal in Fig. 30.1 above), was on average 3.5 times smaller. Its users displayed negative, ironic, and critical assessments of the Russian government and also disseminated Ukrainian-friendly, pro-US, and pro-Western content. While users in this community also shared content on liberal values, such as opposition to authoritarian government or support for democracy, a majority focused on vilifying the government, with users spreading negative memes or ridiculing government strategies or statements. Indeed, particularly virile ridicule and even contempt of the government followed Medvedev's comments in Crimea. The community is also made up of a sizable proportion of Russian-speaking Ukrainians, which seemed to infuence how the group reacts to informational events. Indeed the Savchenko affair and the Eurovision contest, both highly interrelated with Ukrainian politics, are the largest samples of captured Opposition group users, with the former being the largest by number of tweets and the latter, the largest in quantity of engaged users.

Similar to the pro-government community, content shared within the Opposition group followed a dual pattern. If the informational event was neutral or negative to the government, and hence in line with community expectation, users either discussed the topic in a neutral fashion or spread content negative to the government. However, when the informational event ran counter to community expectations, then reaction was split as users reacted in

**Fig. 30.6** Opposition community reaction of disbelief to Belykh's guilt

different ways. The three-pronged reaction following the arrest of the liberal governor of the Kirov Oblast, Nikita Belykh, well demonstrates this trend. Factual and neutral tweeting was predominant; however, genuine shock and disbelief, often including statements that Belykh was set up, was also prevalent (see Fig. 30.6). Finally, a third opinion expressed by the community was that of anti-Belykh statements, believing that he betrayed the liberal movement by becoming a systemic politician.

Represents data from June 25 to June 28. The solid line indicates the number of tweets per hour (right axis) and the dashed line indicates the proportion of conversation believing Belykh was set up (left axis). Tweets containing lemmatized words including "setup," "don't believe" and "provocation" ("*podstava*," "*podstavili*," "*podstavit'*," "*podstav*," "*ne verû*," "*poverit*," "*provokaciâ*," "*provocirovali*") were used to calculate the proportion.

Comparing the standardized rate of tweets per hour between the two main communities, we see that the pro-government group reacted very differently than the Opposition group to several events, most notably during the Medvedev event (see Medvedev Chart in Annex C).11 The initial spike of tweeting activity in mid-day on May 24 lagged by a few hours the initial and larger reaction by the Opposition group, indicating that the pro-government group was less likely to immediately react to the negative information. The secondary and larger spike of activity in the latter half of the day is particularly interesting, given its size and the content shared during it was mostly not about Medvedev's comment at all. Comparing the (nominal) number of tweets every two hours with the proportion of the tweets that have to do with Medvedev's comments, we see that the conversation shifted to discussing other topics during this second spike (see Fig. 30.5). Following this, the proportion of unrelated content on Medvedev continued to dominate discussions within the community, with the proportion mentioning the pensioner comment mostly remaining in the 20–30% range. Moreover, given that the keyword used to collect this sample was "Medvedev," we hypothesize that this pattern of distraction specifcally had to do with positively portraying the Prime Minister.

#### *30.4.3 Finding Bots Within Russian Twitter*

SNA is undeservedly neglected in the context of the mainstream topic of "bot" and "troll" impact on Russian "online" political discussions. Recent research points that the proportion of content created on Russian Twitter has a 50% probability of being produced by "bots" (Stukal et al. 2017). As stated elsewhere (Murthy et al. 2016), bots can have an impact on simple indicators such as follower counts or hashtag boosting. This may impact users who follow others indiscriminantly and network metrics as a whole, which assess all tweets in the network without taking into account the structure of social networks (Ferrara 2018). As ordinary users tend not to follow "bots" and segregate themselves into isolated "echo-chambers," bots are likely to be segregated to isolated communities that have little infuence on real politically engaged users.

To evaluate the potential impact and ensure the validity of our fndings, we apply a three-method strategy to measure the prevalence of bots within the identifed network structure. First, we evaluate the proportion of duplicate and highly similar content created in each community, as bots are known to repeat (not retweet) identical tweets (Lawrence 2015). As tweets can have very minor purposeful variation, applied to them by bot designers, such as adding an extra hashtag or changing the beginning of the tweet, we compare the similarity of tweets by excluding tweet extremities. Secondly, to validate the results of the frst method, we qualitatively assess a sub-sample of tweets shared in suspect communities. Finally, we apply the popular *Bot or Not* method, also known as botometer, to score the likelihood of each user in our samples to be a "bot" (Davis et al. 2016), a feature-based method that evaluates a set of behaviors of a Twitter account and assigns it a score (probability) of being a "bot" (Ferrara 2018). A tried and relatively accurate approach (Yang et al. 2019), it is appropriate for cross-validating other methods utilized.

We fnd that in the two main political communities, the proportion of duplicate and similar tweets varied by event; however, the pro-government community demonstrated a much larger proportion of both in all events (Table 30.5). For instance, in the Medvedev sample, 43% of tweets are very similar in the progovernment community, many spreading the positive new story of government support of Russia's petrochemical industry on May 24. The low proportion of duplicate content in the opposition community is intriguing, as is the relatively high proportion of similar content. A possible explanation could be that the opposition is a more dynamic group of users who follow more sophisticated bots that utilize more complex natural language or image methods to spread content. We propose this fascinating puzzle as a question for further research in the area.

Outside the main political communities, qualitative and duplicate/similarity analysis revealed that many visually segregated communities were made up to a very large degree of "bots," or at least accounts sharing very similar content

**Table 30.5** Duplication and similarity of content in two main communities (% identical; % similar)

(Table 30.6). The more unsophisticated groups posted identical or very similar content, including as high as 83% of all tweets for an event (community 7, dark orange). Others showed more complex approaches, such as tweeting news headlines or factual statements, with or without a corresponding link in the tweet. Interestingly, when links were present, they often pointed to Yandex News or even more commonly to heterodox news or blogging sites. While the information captured in the samples of the six events for these communities were often political, the public timelines of the "bot" accounts often included completely unrelated content, such as for commercial purposes and advertising. We assume that these communities of "bots" are owned by marketing or consulting frms and tweet specifc content based on the requirements of their clients, without any particular impact on actual political discussions.

Evaluating the average automation probability of users in each community, as reported by botometer, reinforces our fndings (Table 30.7). Communities of real users had on average low probability of being automated, with a relatively small proportion of users removed or suspended. By the same token, communities, previously identifed as highly likely as being "botnets" or having a large prevalence of "bots" (Communities 3, 7, 8 and others) had much higher average probability of being automated. Moreover, a large proportion of accounts in these communities have since been suspended by Twitter. Recently the company expanded its activities to diminish the impact of bots and trolls by suspending multiple accounts.12 The results of these actions reinforced our fndings, as the proportion of suspended accounts in the communities we identifed as botnets was much higher than in real user communities. Indeed, entire

**Table 30.7** Botometer (Bot or Not) results (average universal probability of automation; proportion of accounts no longer present two years after samples were originally collected)

"botnets" have all but been suspended by Twitter in the two years since the samples were originally collected (see Table 30.7). On the other hand, some remain relatively untouched. From all above indicators, we conclude that "bots," while undeniably highly numerous and often verbose on Russian Twitter, are often segregated to isolated mini communities that have little impact on real politically engaged users. Real political communities do likely possess a certain proportion of bots, however, as identifed in the literature (Kollanyi 2016; Murthy et al. 2016), these bots are likely to be complex and highly sophisticated, making their study challenging but their potential impact on shifting real conversations greater.

## 30.5 Conclusion

Data on public and political engagement of Russian citizens, active political discussions and debates, and even protest coordination activities, are readily available to researchers studying Russian politics. This chapter illustrates how SNA can be instrumental and ineluctable in evaluating key research hypotheses utilizing such data. Using six resonant political discussions collected from Twitter over the summer of 2016, we validate multiple political and sociological theories important for Russian studies. Firstly, we fnd that Russian online society is divided among the same ideological lines as the public sphere, representing two distinct and consistent communities of users, one supporting the Kremlin, and a "non-systemic" opposition that opposes it. Secondly, we validate the presence of "echo-chambers" in Russian social networks, identifying polarization between the two main political groups. Thirdly, we observe that "infuencers" on Russian Twitter are not generally traditional political elites, but "interesting" and highly informative users such as that of famous pundits, parody accounts, or news sources. Furthermore, given regular users' isolation into self-created separate ideological communities as well as further minicommunities, we comment that the expected impact of information control strategies by the government are likely quite limited.

We obtain our results through the application of a thorough and holistic approach in evaluating the network structure, focusing on three levels of analysis. Macroscopic methods, such as community detection and network visualization, supports the evaluation of the "overall" picture of online Russian political society. Mesoscopic methods validate the detected structures and provide insight into the sub-structure of each detected community. Finally, microscopic methods identify the "infuential" users who are able to widely disseminate content and impact political discussions.

We fnd that SNA is also an economical method to detect "bots" in social networks and evaluate their impact on real political users. Currently a hotly debated topic both internationally (Murthy et al. 2016; McKelvey and Dubois 2017; Ferrara 2018) and in Russia (Stukal et al. 2017; Lawrence 2015), our fndings demonstrate that the impact of "bots" on Russian social networks is likely quite negligible. We fnd that while numerous and often verbose, "bots" are mostly isolated to mini-communities far removed from real politically engaged users, and as such are unlikely to impact real political discussions. We conclude by noting that the effciency of SNA in extracting real and valid structures in Russian social networks makes it a prerequisite and fundament for the application of further advanced methods, such as topic or sentiment analysis, when studying Russian politics (for more on sentiment analysis, see Chap. 28).

## Annex A: Polarization of Communities

Polarization of communities is taken from an aggregate of all six events. Note that as over 1000 groups were detected, only the most numerically relevant are shown. Percentages represent proportion of the total for each community (the "All" category).

**Table 30.8** Polarization of communities: retweets

**Table 30.9** Polarization of communities: mentions

**Table 30.10** Polarization of communities: replies

## Annex B: Microscopic

**Fig. 30.7** First and second quantile breakdown of account types by political communities

**Fig. 30.8** Change in infuence of leaders' accounts when centrality compliments the impact of their distributed content

## Annex C: Tweet Patterns for the Two Main Communities, Tweets per Hour by Event

**Fig. 30.9** Tweet patterns for the two main communities, tweets per hour by event

## Notes

issues, and a further 17 were of news media. In 2017, the pattern was similar with 15 and 16 respectively. Prime Minister Dmitry Medvedev has remained the most followed politician and always either tops or is at the top of the list. Although the trend has shifted towards popularization of non-political accounts, as of 2019, the nominal politicization of Twitter still remains quite high with 28 politically relevant accounts among the top 100. In comparison, German, French, UK, and American politicians' positions are less representative in the top 100, as these segments are largely dominated by the non-political (celebrities, media and sport personalities) opinion leaders.

## References

an Echo Chamber? *Psychological Science* 26 (10): 1531–1542. https://doi. org/10.1177/0956797615594620.

———. 2018. Twitter and the Russian Street: Memes, Networks and Mobilization. *Center for New Media and Society Working Paper* 1.

**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/ by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

## The State of the Art: Surveying Digital Russian Art History

## *Reeta E. Kangas*

## 31.1 Introduction

Digital methodologies can be used to complement more traditional approaches to art history. Yet, mainly due to the diffculties of analyzing visual material with computer-assisted methods, digital art history and visual analysis are areas that have arguably developed slower than other branches of digital humanities (see Drucker 2013, 5; Klinke 2016, 16; Lozano 2017, 2). With regard to training in digital methods, the efforts in the humanities are rather scattered and digital training in art history is especially lacking (Zorich 2013, 16). However, the feld of digital humanities could beneft immensely from the traditionally strong expertise of art historians in curating and creating cultural data (Schelbert 2017, 5). Indeed, digital art history is not quite as new a feld as is often thought. Some even argue that art history is not behind other disciplines in the development of the digital (Zweig 2015, 40–41). Nonetheless, despite the recent advances in image recognition, digital methods have not been as widely adopted in the analysis of the visual as in some other felds of humanities, making it easy to overlook the efforts that do exist.

Though Soviet and Russian visual materials have been studied quite extensively, they have not been analyzed much within the framework of digital art history. This is partly because many of the widely used digital art repositories contain only Western European and Renaissance art, leaving other art in a marginalized role (see, e.g., Münster et al. 2018, 380). However, scholars are now

R. E. Kangas (\*)

University of Turku, Turku, Finland e-mail<EMAIL_ADDRESS>
© The Author(s) 2021 569 D. Gritsenko et al. (eds.), *The Palgrave Handbook of Digital Russia Studies*, https://doi.org/10.1007/978-3-030-42855-6\_31

debating the advantages of creating and making accessible digitized Russian visual material in collections and archives in- and outside of Russia (see Kizhner et al. 2018; Bridgers and Blood 2010; Kain 2018). For example, Biryukova et al. (2017) discuss how virtual cultural storages and virtual museums can be used to preserve the Russian cultural heritage. Other researchers have analyzed the possibilities and problems associated with making 3D models of cultural heritage objects and Russian architectural monuments, such as churches and monasteries, and presenting them online (see e.g. Borodkin et al. 2015; Zherebyatiev and Ionova 2014). Indeed, as Biryukova et al. (2017, 157) show, many of Russia's most popular virtual museums contain churches and monasteries reconstructed in a virtual space. Some researchers, like Anna Sanina (2019), Olha Korniienko (2014), and S. Polovinets and E. Baranova (2018), have even applied digital methods to the study of Russian and Soviet satirical visual material. However, the majority of research using digital methods is based on content analyses, which in turn have relied on the coding of the images by the researcher or research assistants. To the best of my knowledge, no machine learning or computer vision methods have been used in visual studies of Russian and Soviet material.

In this chapter, I chalk out some options and possibilities to expand and apply new digital research methods and visual analyses, in order to complement the more traditional approaches to Russian and Soviet art history. As an example of a feld of art historical research that may beneft from such digital methods, I use my own research on Soviet political cartoons published during the "Great Patriotic War," as the years of war between the Soviet Union and Nazi Germany, 1941–1945, of the Second World War are known in Russian. During these years, the offcial party newspaper *Pravda* published 185 political cartoons, bearing nine different artists' signatures. In the course of my research, I manually collected these political cartoons and assembled an Excel spreadsheet with detailed annotations that essentially functioned as a database and allowed me to conduct a qualitative analysis on them.

To interpret a political cartoon, it is necessary to understand the contextual, textual, and visual features and information contained in them. This requires the researcher to have a certain amount of background knowledge. I employ Ernst Gombrich's (2002, 142–154) ideas, according to which a communicating image consists of three components—context (the environment within which the cartoon exists), caption (the verbal elements of the image), and code (the visual language the artist uses). This chapter thus ultimately discusses how digital methods could facilitate a Gombrichian analysis of a communicative image, such as a Soviet political cartoon.

Before getting into the use of digital methods to enhance the research of visual material, it is frst necessary to give a brief overview of the situation in Russia regarding the digitization of material, copyright laws, and open access. Next, I look at recent developments in digital methods for art history and their potential application to Russian and Soviet art, especially with regard to visualizing data and the use of machine-learning algorithms to help analyze databases of relevant textual and visual material. One of the benefts of using such algorithms is that they could facilitate piecing together the cultural context for an art historical research project. However, as I discuss in the following section, the vast amount of cultural knowledge that is required for a machine to properly understand the representations within an image is where machinelearning algorithms reach their limits. As is laid out in the fnal section, these limits could be overcome by combining the new digital methods with traditional art historical research methods. Ideally, larger research projects, featuring both Information Technologies (IT) professionals and trained art historians, would enable us to create more useful art historical databases that would allow for a more effective use of the new digital methods while also combining the strengths of both digital and traditional art historical research.

## 31.2 Digitization, Copyright Laws, and Open Access

It is a common trend that archival material and cultural artifacts are being digitized at an increasing rate. However, the level of digitization is not universal, and its organizational forms differ. Certain cultures, mainly Western European and North American ones, are making bigger investments in their digitization projects and are thus overrepresented, while others remain in a marginalized position (Rodríguez Ortega 2013, 131). Some countries, like Russia, have government involvement driving the digitization, while in others it remains the task of individual organizations.

It is often diffcult for art historians to fnd relevant, available, open-access, and good quality visual material in digital repositories (Münster et al. 2018, 369–371; ibid., 380). And with digital archives that are not exclusively devoted to Russian data, it is occasionally diffcult to search for Russian material, because not all archives attach keywords such as "Russia" or "Slavic" to their documents and objects (cf. Bridgers and Blood 2010, 78; for more, see Chap. 20). These problems with accessing digital databases often lead to researchers creating their own personal collections (Münster et al. 2018, 371). Thus, Russian art history remains very much a question of the researcher knowing where to look for accessible and relevant material, and in many cases still traveling to the destination to retrieve it.

Online resources of Russian digitized art are rather limited. However, some resources do exist. For instance, some Russian art museums have now made parts of their collections available online, and some have even created virtual tours of their museums (see, e.g., Virtual Visit, the State Hermitage Museum). A number of museums and galleries, including the State Russian Museum, are also collaborating with the Google Arts and Culture project to digitize and put parts of their collections online (see Virtual Russian Museum). In addition to such classic art resources, there are also newspaper, journal, and photography repositories that may be of interest to art historians. For instance, a collaboration of the Russian search engine Yandex with museums and private collectors has resulted in a large online photo archive (see *Istoriâ Rossii v fotografâh*).

The National Library of Finland has also recently subscribed to East View's digital collections (http://www.eastview.com), sidelining their microflm collections. However, the East View interface offers only a text-based search option, which makes looking for images in the newspaper diffcult. Furthermore, compared to the library's microflms, the digital archive's image quality is worse and some issues of *Pravda* that were available on microflm are missing from the digital archive. Nonetheless, these digital copies of *Pravda* provide an easier option for studying the textual environment—the Gombrichian caption—within which the image exists. But while digital repositories like East View make accessing digitized material easier for those who have online access, they do not provide the services for free (for more, see Chap. 20). They offer researchers material that would otherwise require them travelling to archives to retrieve, but they do not make the information openly available to everyone. Furthermore, the digitization of textual sources is generally much more common than that of, for example, art objects.

An ongoing Ministry of Culture led program aims to have all the Russian Federation Museum Collections cataloged, digitized, and available online at https://goskatalog.ru by 2026 (see *Gosudarstvennyj katalog Muzejnogo fonda Rossijskoj Federacii*). That is, it aims to make available metadata and pictures of all the items in the public museums. The participation of private museums in this project is voluntary (Kizhner et al. 2018, 351–352). In 2018—eight years before the project was supposed to be completed—only 14% of the objects were digitized and 9%, that is 7,034,904 objects, were included in the database (ibid., 352–354). By May 2019, the number of objects in the online catalogue was 11,017,513, which means that the digitization and cataloging process advanced by about four million objects within the past year or so.

This digitization project by the Russian Ministry of Culture, however, does not currently grant complete open online access to the cultural heritage objects. For example, in St. Petersburg the number of digitized items is higher than on average in Russia, and even higher than digitization on average in European museums, but the number of items available online is lower than the average in Russia (Kizhner et al. 2018, 356–358). Furthermore, the quality of the photographs is not necessarily an aspect to which much attention has been paid. This becomes evident when scrolling through the various images of the catalog. A more thorough "digital museumifcation," that is, a proper transformation of the object into digital form with full metadata, would be needed to make the objects in the catalog more useable to the researcher (cf. Biryukova et al. 2017, 153). This could be achieved by using crawlers or appropriate scripts. If the Russian Ministry of Culture's project's aim is achieved by 2026, and if the Russian policies allow for open licenses on cultural heritage objects, this would provide substantially easier access to the Russian cultural heritage to a wider audience, including international researchers (Kizhner et al. 2018, 363). It remains to be seen where the digitization project will lead and what it will in the end provide to the researchers of Russia.

Copyright laws largely limit what digitized material remains closed and what becomes public, which infuences the research and other projects connected to cultural objects (Arditi 2018, 54; Roued-Cunliffe 2018, 288). Internationally, copyright laws largely accept the so-called fair use policy of images, which means that they can be used without obtaining permission from the copyright holder in certain cases, such as in research papers where the image is the direct subject of the analysis rather than merely an illustration. However, the Russian laws are more restrictive. Here, the state legislation supports the so-called "permissions culture," which works counter to the fair use policy. Accordingly, museums can retain the rights to all their material even in a case that would be regarded as fair use in a research publication. In practice, this varies from museum to museum and the researcher needs to fgure out the museum's practices. For instance, the State Hermitage allows their material to be used, for example, for educational purposes, in conference presentations, and in PhD theses. But permission is required to use images for commercial purposes or in research publications, or to publish conference slides online (Kizhner et al. 2018, 359). The fact that visual material is by nature copyright heavy, when compared, for example, to text sources, hinders the work of individual researchers as well as the building of digital repositories that would beneft the feld more broadly.

The complexities of the copyright laws and the "permission culture" that prevail in Russia make it unfeasible for an individual researcher to make their personal databases of primary material open to other researchers. While nothing prevents me from publishing my metadata, the fear of litigation or of being denied further access to the archival material has kept me from making my collection of *Pravda* political cartoons accessible to the public. Instead, I have the database stored on personal devices and the images saved in accessible formats, such as PNG and TIFF. Indeed, when thinking about the storage of data, it is necessary to consider whether the data can be made open and who could beneft from it. For data to be openly accessible, it is necessary to use fle formats that are possible to use with a variety of non-commercial programs and are likely to stay in use for a long period of time (Roued-Cunliffe 2018, 292). Such formats include, but are not limited to, JSON, XML, and IIIF. With regard to Russian images, a more widespread use of the annotation ready IIIF format by the heritage institutions and in the Russian museum catalogs would provide researchers better access and more possibilities to present the images with stable Uniform Resource Identifers (URIs).

## 31.3 New Digital Approaches to Visual Analysis and Art History

Digital methods provide new approaches to art history, such as the visualization and display of data and research results, the digitization and digital rendering of art, and most recently the use of convolutional neural networks (CNNs) for simple and even more complex recognition tasks. The increasing computational power we have at our disposal now enables rather more complex visualizations than the traditional bar chart or pie chart. For instance, one can create complex visualizations that consist of a large quantity of individual images that, when combined, provide an overall picture (Schelbert 2017, 4). New visualization techniques also allow for more dynamic "moving" charts in electronic form. The use of such visualizations in digital visual research has been criticized for its lack of accompanying interpretation (see Bishop 2017, paragraph 9). However, when approached with care, new modes of visualization can be very powerful at revealing tendencies that might otherwise be missed. Thus, with the *Pravda* political cartoons, one could create visualizations to exemplify their structure, their connections to historical events, their intertextuality, and other aspects, in the spirit of Gombrich's notion of contextualizing an image. One could, for example, place the cartoons on a map of Europe, showing where each one takes place, or do a cross-referencing of countries and animal characters to see the signifcance of animal symbolism in the cartoons.

In addition to such visualizations of data, digital methods also offer other options for representing research fndings. Digitized art and the digital rendering of art artifacts enable the researchers to bring the art to a wider audience. For example, the University of Nottingham's project *Windows on War: Soviet Posters 1943*–*1945* (see Windows on War), which was conducted by a multidisciplinary team, allows the visitor to look at the images while at the same time reading about culturally specifc information and the historical contextualization of the images. In a sense, this makes the communication of art history independent of both location and time, allowing people to immediately access art from around the world and even to view a virtual restructuration of an already destroyed artwork, such as an old building (Kellaway 2013, 95–96; Borodkin et al. 2015, 5–7). Furthermore, contemporary digital online spaces offer us the possibility to reconstruct old exhibitions, of which we have photographic evidence, such as the "godless corners" of the early twentieth-century Soviet Union (Kain 2018, 219). Thus, the use of digital methods is not limited to the actual process of conducting the research or disseminating the results within the academic environment; they can be employed in researchers' popular outreach efforts as well.

One of the diffculties of digital humanities is to turn the primary material into useful data: to quantify a body of material that is not traditionally handled in such a way and to combine this quantifcation with humanities methods and theories of enquiry (Otty and Thomson 2016, 135). Manually annotating images is perhaps still the most common way of approaching the problem of turning an image into a format that is possible to analyze with the use of a computer. Here, researchers annotate the images with appropriate keywords, that is, metadata, which are then used as a basis to build a database and conduct a computer-assisted analysis to fnd underlying tendencies of the material (see, e.g., Korniienko 2014; Sanina 2019). I followed this procedure when I made my database of the 185 *Pravda* political cartoons. My metadata included, when relevant, information on the cartoon's date of publication, page, position on the page, artist, title, captions, text in image, quote, poet, poem, characters, countries represented, symbols, combinations of a symbol and a country/person, combinations of attributes and a person/country, and the roles of the characters. This allowed me to analyze the cartoons on the basis of the assigned keywords and to make cross-references between them. Such studies rely on the human to do the coding, instead of employing machine vision, which to this date is not yet at a level where most researchers would completely rely on it or know how to use it for the principal annotation of the primary visual material.

The possibility that a machine could take over such basic art historical analyses would help immensely with metadata extraction and other rather mechanical work. The extraction of this metadata, in turn, would directly facilitate the analysis of Gombrich's caption and code—text, quotes, and title being part of the caption and characters, symbols, and attributes part of the code. Naturally, using machines to do this would enable researchers to process much larger datasets. And furthermore, a machine would assign keywords more consistently than a group of coders, who are each assigning keywords based on their varying interpretations (cf. Rose 2007, 60–61; Bell 2001, 22). By combining this computer analysis of a vast body of imagery with an art historian's analysis of specifc images from that same body, one could also create a two-sided database. The frst side would comprise simple computer-assigned characteristics of large amounts of images, while the second would consist of the art historian's keywords and would address the more semantic notions of the image (Dressen 2017, 8). This would allow the researcher to conduct a qualitative analysis with specifc images as examples, while the bulk of the images serve as a contextualizing device.

The computer vision technology that would facilitate such analyses is in a process of constant development. For some time now, computers have been able to reliably detect the colors and textures of an artwork, which does not help us to make any semantic interpretations but does facilitate more precise quantitative analyses of colors and shadings used by various artists as well as to identify artworks and attribute them to artists (Manovich 2015, 22; Schelbert 2017, 5). According to Emily L. Spratt, the image analysis capabilities of machines is now approaching the second of Erwin Panofsky's three levels of art historical analysis. That is, they can not only identify basic elements within the image, such as animals or people, but also detect conventional cultural representations, such as religious motifs (Spratt 2017, 12). In the case of the Union of Soviet Socialist Republics (USSR), these could include ideological motifs, such as depictions of revolutionary events, or certain types of characters, such as the archetypal worker or peasant. Applying such a tried and true art historical theory as Panofsky's to these new developments is, of course, not straightforward. But the fact that the capabilities of computer image analysis are now being directly compared to the image analysis skills of people is, on its own, very telling.

At present, a number of research groups are working to push the limits of what computer vision can do with comics (cf. Laubrock and Dubray 2019; Laubrock and Dunst 2019; Young-Min 2019). Any such research is, of course, heavily dependent on the availability of appropriate training sets. For instance, the Digital Comic Museum (DCM) hosts a set of nearly 200,000 pages of American comics published before 1959, segmented into panels and text bubbles by machine, and transcribed using optical character recognition (OCR), which can be downloaded at https://github.com/miyyer/comics (see Digital Comic Museum). Due to the imperfections of having the segmentation done by a machine, Nguyen et al. (2018) have also produced a subset of 772 pages from the DCM that has been fully annotated by humans. With the help of this training set, among others, researchers have achieved good results in identifying various elements of a comic, such as speech bubbles, panels, and captions. And they are now moving on to more advanced recognition tasks, such as getting machines to recognize recurrent characters, image-text relations, and simple narrative structures (Laubrock and Dunst 2019, 11–20; ibid. 28). It is conceivable, that similar datasets could be created of recent Russian comics. But unfortunately, many other areas of art history do not yet beneft from such vast and high-quality datasets, and as discussed in the following section, Russian art history is no exception to this rule.

## 31.4 The Current Limits of Machine Learning

Perhaps the biggest challenge of using machine learning for analyzing visual imagery is that it requires very large datasets to train the algorithms. For large corpora of visual material with repetitive elements, such as medieval manuscripts, it has already proven to be especially feasible to use computer vision to annotate the images, saving the researchers countless working hours (Bell et al. 2013, 27). As with the medieval manuscripts, if it were possible to construct a suffciently large training set, it now seems entirely possible that a machine could be trained to help analyze political cartoons. For instance, a machine could learn to identify certain characters by distinguishing the exaggerated physical attributes that make a caricature look like its target, such as the moustache and hair of Hitler or the big mouth and short stature of Goebbels. However, care would have to be taken to include enough features so that the computer would not, for example, mistake Chaplin for Hitler on the basis of his moustache. Furthermore, conventional facial recognition currently works by identifying the dimensions of the face. So, for this to work, either the facial recognition software would have to be expanded or a separate recognition algorithm would have to be developed to identify exaggerated and satirized physical features (for more on machine learning, see Chap. 26).

The question in this specifc case is whether the Soviet political cartoons, or visual propaganda more generally, are repetitive enough that training computers to do the annotation would be feasible. While features like Hitler's moustache certainly repeat, each cartoonist has their own style and the themes and topics vary. The only way to fnd out for sure would be to gather a suffciently large dataset and try it out. Saurav Jha et al. (2018) point out that the training sets that are currently available are too small to train a neural network to recognize cartoon characters. Hence, the training sets need to be supplemented with the inclusion of photographs of the people who appear in the cartoons. However, the inclusion of large amounts of photographic material decreases the feature recognition of the cartoons. Additionally, the more exaggerated the features of the character, the more the machine has trouble identifying the face. Going even further, one wonders whether a machine could learn to detect satire and ridicule. Or make the connection and fnd the similarities and differences between a caricatured and a photographed Hitler. If a computer could effectively learn to examine the Gombrichian code of an image, it would enable the faster analysis of large visual corpora of propaganda imagery and, possibly, provide us with a more complete picture of the ways in which visual propaganda functions.

In addition to the size of the training set, the quality of its images and their similarity to the material that is to be investigated are also crucial, lest the neural network end up interpreting images in different ways than the trainer's intention. As the training set of convolutional neural networks infuences the way the network interprets other images shown to it, the training set needs to be carefully planned so that it will not cause erratic results (Spratt 2017, 4). When an image is different from the images of the training set, the machine may end up facing diffculties. For example, in one image-to-image translation project, the machine was taught to transform images so that a winter scenery became a summer scenery, a photograph into a painting by a famous artist, or a horse into a zebra. However, the training set did not contain images of horses, which resulted in the machine transforming not only the horse's coat but also the skin of the bare-chested President of Russia riding the horse into a zebrapatterned being (Zhu et al. 2017). This exemplifes how the computer interprets visual material only on the basis of the training set that has been used. Thus, the machine does not have the contextual information and interpretation capabilities that a human in a similar situation would have.

Images have a high level of cultural coding. So even if a computer can extract large amounts of data from an image, it cannot understand the semantic side of an image as well as a human does. Current developments in computer vision aim to bridge this "semantic gap," to allow a machine to detect basic semantic meanings based on the information it can obtain from an image (Manovich 2015, 22). However, in the same way that computer vision needs to be trained to recognize images, humans have been trained by culture and society to recognize and interpret them correctly (Spratt 2017, 7). In other words, for a machine to be able to correctly analyze the signifcant elements of an image, it essentially needs the same training and cultural knowledge that a human has. The almost incomprehensibly vast amount of information that forms this cultural context of an image is where machine learning and computer vision reach their limits and where the guidance and supervision by trained art historians will for the foreseeable future remain essential for any research project.

Our interpretations are always dependent on our spatial, temporal, and cultural contexts. Any interpretation by an art historian—or anyone else for that matter—is dependent on their background (Gaehtgens 2013, 23–24). For example, given an image of a miserable tiger, a contemporary of the wartime political cartoons in the Soviet Union would have understood its signifcance as a play on the German heavy tank *Tiger* getting stuck in the muddy spring of the Eastern Front, as would someone familiar with the fate of *Tiger* tanks. However, without the contextual knowledge, the symbolism of the animal could end up signifying something else, such as the characteristics of the Germans as defeated wild animals. That is, the interpretation I make might differ largely from the interpretation someone else makes—can a computer make such semantic interpretations?

In the same way that the interpretation of data is dependent on the background of the researcher, the way that the data are organized depends on the interpretations of the researcher. Thus, the way I organize data might differ largely from how someone else does it (see Otty and Thomson 2016, 115). In other words, when making interpretations or organizing data, one needs to remember one's own contextual situation and not blindly trust digital methods and believe that they will provide completely replicable and authoritative results (for more, see Chap. 21). And until machine-learning algorithms can be trained to take into account a reasonable proportion of the cultural context of its target material, we must bear in mind that any interpretations made by such algorithms will be based on a considerably narrower background than that of any human researcher.

## 31.5 How Humans and Machines Can Work Together

The advantages of the new digital methods and of traditional art historical research are conveniently complementary. Indeed, by combining the strengths of a trained researcher with the capabilities of machine-learning algorithms, it should be possible to cancel out any limitations of either. The digitization of visual imagery enables researchers to conduct contextual analyses of images that would not be feasible without access to digital resources. Thus, it facilitates an even wider contextual analysis than Gombrich (2002, 142–154) could have had in mind when writing about the context of the image. As has been discussed, developments in the digital methods are currently on the cusp of making this possible. Well-designed databases with easy accessibility and properly annotated images would help researchers to examine the intertextuality and connections between different works of art and other cultural, social, and historical phenomena (Brandhorst 2013, 72–73). For instance, it would help the researchers if computers could do a search and comparison within a database for art works that are similar to the one that they are examining. Of course, for this to be practical and feasible, it will frst be necessary for the machines to be able to reliably identify and catalog certain elements within the visual artifacts. In this way, a computer could go through large corpora and their metadata considerably faster than a human (Klinke 2016, 16). Furthermore, the comparison of many images with each other when it comes to composition and aesthetics could provide new insights into how different artists composed their images (Pfsterer 2018, 138). After all, it is impossible to compare as many images in person as it would be with a computer.

The high level of intertextuality of political cartoons would also become more evident with such comparative computational methods. Their connections to various areas of culture, including the Soviet visual propaganda imagery, which had the tendency to repeat and borrow ideas from previous images, illustrate propaganda's dependency on the cultural context within which it operates (see Kangas 2017, 46–47). For a contextual analysis of the political cartoons, the pages on which they were published—or even whole issues of *Pravda*—could be processed with OCR for a cross-referencing of the news text with cartoon. The comparison of the text surrounding the image with the actual image could provide additional contextualization, complementing the researcher's efforts to place the image within the context of the war events. The computer could also assign a value to the similarity between specifc features of the cartoons and other images and cultural artifacts, war events, or their geographical location. These values could then be mapped onto a graph in which all the variables would be presented together in a dynamic visualization. However, due to the complexity and the wide variety of cultural representations, this is currently still beyond the capabilities of machine-learning.

More generally, with the help of computer programs that could search for such open access information—this requires open access as well—many projects could beneft from the information as a part of the contextualization of art (Dressen 2017, 4–5). And the emergence of large text databases of art historical material will enable a more thorough and accessible contextual analysis of the visual, which has traditionally been slow and cumbersome due to the large amount of background material needed (Drucker 2013, 10). Thus, with digital methods, it is possible for human researchers to take into account ever larger amounts of background information when conducting their research.

With regard to training a machine-learning algorithm to detect conventional cultural representations and thus establish connections to the broader cultural context, here too it is a question of having suffcient information available. Thus, apart from computers with the necessary processing power to do the analysis, the digitization and accessibility of cultural artifacts is essential for a full analysis that takes into account both the cultural context and the formal properties of the object (Schelbert 2017, 5). But is it possible to train a machine to see what is not presented? Indeed, in images, and texts too, what has been left out conveys semantic information. That is, in an image what you cannot see is often as important—or even more important—than what you can see (Rose 2007, 72). Even if it is possible to train a machine-learning algorithm to "see" what is missing in an image, it is diffcult for it to do an analysis of the meaning of the omissions. For example, in Soviet political cartoons, the Soviet Union or its allies are rarely shown. However, their omission does not mean that their presence is not implied. Once again, here, the interpretative skills and supervision by a trained art historian is necessary to fully understand what is going on.

The increasing use of digital methods in the study of the visual does not necessarily mean the overthrow of art history's more traditional methodologies. In combining digital and traditional quantitative methods, the researcher can draw a range of conclusions from their datasets that would be diffcult to manage without the machine's computational power. But at the same time, through qualitative analyses, the researcher can make interpretations and evaluations that a computer cannot (see Klinke 2016, 28; Lozano 2017, 6; Rose 2007, 70–71). To employ such a wide methodological oeuvre calls for interdisciplinarity and/or collaboration between specialists from varying backgrounds. There have been several calls for such collaborations (e.g., Glinka et al. 2016, 209; Klinke 2016, 31; Mercuriali 2018, 149). Having teams that employ people with expertise from different felds and with different skill sets would further the goal of creating large, accessible databases, as well as the planning of new complex methods of analysis.

## 31.6 Conclusion

In this chapter I looked at some of the advantages and challenges that the digital study of visual material will encounter. My starting point was to consider these issues in the light of a previous research project which was conducted mainly with more "traditional" methods, such as archival work on microflms, digitization of material, and conducting a "manual" qualitative analysis of the primary material. I used my earlier analysis of Soviet visual data as an example and discussed the possibilities of digital methods that I could have used in the project.

In some ways, researchers of Russian and Soviet visual material face many of the same challenges that any other academics face when using machine-learning methods to enhance their research. For instance, it is important to train the machine to be "intelligent" and to learn to "see" appropriately and ethically we would not want the machine to learn to tamper with the data to make the researcher happy. Additionally, the training process is still too slow and complex to be used in a small-scale research project, but the development of machine and deep learning might change the situation and make these methods more approachable for a wider base of researchers.

In other ways, researchers of Russian art history face their own unique set of challenges in adopting the new digital methods. For instance, the problem of the "semantic gap," that is, that computers are not able to handle the semantic side of the objects they are analyzing, is especially pertinent when analyzing visual imagery, which is heavily reliant on a large amount of contextualizing information. And the collection of such contextualizing information in digital databases, so as to make it useful for machine-learning algorithms, is further confounded in Russia by their restrictive copyright laws and permission culture.

These restrictive copyright laws are especially detrimental, as the lack of openly accessible, large-scale databases of visual material is the primary bottleneck preventing the use of new digital methods for conducting art historical research in Russia. As a result, these digital methods have not yet found a secure foothold within Russian visual studies. Some research has been conducted, but it has mainly relied on rather traditional computational methods, such as content analysis. Considering the breadth of visual material that Russia—which is generally considered to be a very visual culture—and the Soviet Union have produced, it would be extremely advantageous to employ some of the more recent digital methodologies to that material.

Nonetheless, larger projects featuring interdisciplinary teams and collaborations within art history and other felds that employ digital methods for visual analysis could yield considerable results. In many cases, a digital project would beneft from the participation of people with varying backgrounds and skills, such as IT, quantitative, and qualitative methods. Through the co-operation of people with all of these different skill sets, it would be possible to employ the digital methods more fully and fnd new creative solutions to, for instance, create suitable databases that better serve the researchers or more dynamic visualizations of the research results. So, despite the challenges some of them still face, the new digital methods provide many new possibilities for the study of the visual, facilitating an easier examination of the images' context, caption, and code, in the spirit of Ernst Gombrich.

## References

Prism of the Satirical Magazine Perec' (1964–1991): Database, Content Analysis of Caricatures]. *Istoric*̌*eskaâ informatika* 4: 50–67.

**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/ by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

## Geospatial Data Analysis in Russia's Geoweb

## *Mykola Makhortykh*

## 32.1 Introduction

The rise of digital technologies has led to the emergence of new ways in which physical spaces are perceived, experienced, and mapped. The availability of high-quality satellite imaginary amplifed by the unprecedented possibilities for crowdsourcing geospatial data (Crampton 2009) has enabled the emergence of multiple platforms dealing with geographic information. It was followed by the integration of geographically aware computing in the architecture of major social media platforms (Crampton et al. 2013) and the growing capabilities for location tracking embedded into mobile devices (Sansurooah and Keane 2015). Together, these changes have given rise to a global collection of services which use the geographic data for different domains' applications. These services are currently known as "geospatial Web" (Lake and Farley 2009) or simply "geoweb" (Crampton 2009).

The emergence of geoweb and associated "neographic" (Haklay et al. 2008) practices of publishing, sharing, and visualizing information about places and people has signifcant implications for academic research. In the large-scale review of studies, which use geospatial data, Stock (2018) demonstrates these data's applicability to a wide range of research felds, including recreation, crisis management, and environment studies. The reasons for the growing adoption of geospatial data vary from the emergence of geographic datasets of unprecedented size and granularity (Elwood 2010) to the transformation of citizens into geospatial subjects able to produce and employ geospatial data (Wilson 2011). Their use is amplifed by innovative possibilities for identifying and

M. Makhortykh (\*)

University of Bern, Bern, Switzerland e-mail<EMAIL_ADDRESS>
© The Author(s) 2021 585

D. Gritsenko et al. (eds.), *The Palgrave Handbook of Digital Russia Studies*, https://doi.org/10.1007/978-3-030-42855-6\_32

mapping spatial relationships enabled by artifcial intelligence and big data (VoPham et al. 2018).

Russia is not an exception from this trend as shown by the increasing number of studies applying geospatial data to study subjects varying from electoral fraud (Kobak et al. 2016) to Silk Road tourism (Tikunov et al. 2018) to Second World War remembrance (Bernstein 2016). Yet, the use of geospatial data in the context of Digital Russian Studies has its own specifcs attributed both to the general role of digital media in Russia's media ecologies and to the particular importance of geoweb in this geopolitical context. The explosive growth of Internet use in Russia in 2000s has led to profound changes in the language and communication in multiple domains, including politics (Gorham et al. 2014). The importance of the digital sphere increased even further since the beginning of the Ukraine crisis in 2014, which marked the unprecedented level of state-sponsored cynicism toward the media sphere and its growing instrumentalization for propaganda and disinformation (Roudakova 2017). In this "post-truth" (Surowiec 2017) environment, geolocation data that allow to (dis)prove the existence of specifc phenomena emerge as a pivotal factor for making and refuting knowledge claims (e.g. about the presence of Russian troops in Ukraine (Shim 2018)).

To further contextualize the features of Russian geoweb and examine how recent studies address opportunities and challenges provided by it, I will start by reviewing different sources of geospatial data available in the Russian context, varying from social media platforms to crowdsourced databases. I will then move toward discussing possible ways of extracting location information; these ways vary from mapping location names provided through metadata to specifc geographic coordinates to extracting location from verbal or visual texts or inferring it from users' activity on social media. Then, I will explore different ways to use geospatial data, such as mapping spatial distribution of socioeconomic phenomena and analyzing mediatization of cultural practices. Additionally, I will briefy discuss the ethical aspects of some of these uses, in particular privacy-related issues. Finally, I will conclude by recapping the main arguments of the chapter and scrutinizing possible directions for future uses of geospatial data in Digital Russian Studies.

## 32.2 Data Acquisition

The frst question to address in research using geoweb analysis is what kind of geospatial data is to be used. As I mentioned in the introduction, the distribution of location tracking devices and geographic crowdsourcing gave rise to multiple platforms dealing with geospatial data; however, the format, scope, and quality of these data vary signifcantly depending on the platform. To illustrate these differences, I will review below three categories of geospatial data sources, which are of particular relevance for Digital Russian Studies: crowdsourced databases, open datasets, and social media.

#### *32.2.1 Crowdsourced Databases*

The availability of digital technology allowing to collect, visualize, and share geospatial data led to the emergence of multiple projects focused on crowdsourcing "volunteered geographic information" (Goodchild 2007). Unlike established sources of geographic information (e.g., open datasets produced by national mapping agencies), crowdsourced databases rely on the assumption that geospatial content produced and edited by multiple individuals will eventually converge on a consensus (Elwood et al. 2012, 575). While this assumption does not guarantee the same quality of data as in the case of sources produced by certifed experts, crowdsourced projects are able to account for attributes which are usually omitted by traditional mapping agencies and capture fast-changing phenomena (e.g., natural disasters).

The scope and focus of volunteered geographic projects vary signifcantly. Some of them, such as Open Street Map (OSM) (https://www.openstreetmap.org), HERE Maps (https://mapcreator.here.com/), or Yandex People's Map (https://n.maps.yandex.ru/), pursue the goal of creating and sustaining free digital maps or gazetteers. Other projects have limited temporal and thematic focus. Both in Russia and in the West,1 the latter projects often arise as part of the volunteered reporting in the context of natural disasters2 or armed conficts.3

Both categories of crowdsourced databases can be of use in the context of Digital Russian Studies. Many global initiatives provide relevant geospatial information, which can be used for Russia-centered research. For instance, Quinn and Tucker (2017) used OSM and Wikimapia (https://wikimapia. org/) to trace how crowdsourced maps are used to represent disputed areas such as Crimea and found substantial differences in the ways geopolitical disagreements were visualized and addressed. These differences were attributed to the OSM hosting more contributions from Western editors, whereas Wikimapia was more eager to transmit the Russian offcial discourse. Other examples include the study by Kulakov, Petrina, and Pavlova (2016), who used Wikimapia for evaluating digital smart services utilized for cultural heritage tourism planning, and the research by Karbovskii et al. (2014), who employed Wikimapia for simulating the process of decision making based on 2012 Krymsk fooding.

Additionally, the Russian digital landscape features a number of crowdsourced projects dealing with specifc domains or topics. Despite their variety and rich data, these projects have so far received limited acknowledgement in academic scholarship. A few exceptions include, for instance, *Pomnite nas* (Remember Us) (http://www.pomnite-nas.ru/), a project devoted to collecting geospatial data about Second World War monuments devoted to Soviet soldiers (Bernstein 2016). Another example is *RosYama* (Russian Pit) (https:// rosyama.ru/), a civic project initialized by Alexei Navalny, a Russian antisystemic opposition leader and activist, who created an online crowdsourced service for reporting road potholes (Ermoshina 2014). Many of these projects are not necessarily designed as sources of geolocation data for academic research and, instead, intended to facilitate social activities (e.g. collective remembrance of the Second World War in the case of *Pomnite nas*). Despite these nonacademic goals, these projects can still be a valuable asset to the researcher who would creatively approach their data. For instance, geolocation data offered by *RosYama* can be used not only for research focused on the quality of Russian roads but also for visualizing geographic networks of activists or detecting the misappropriation of funds planned by specifc regions for repairing the roads (for more projects like this, see Chap. 8).

The major challenge of using crowdsourced databases is related to the quality of data provided through them. Because of the lack of authoritative control over their content, the possibility of encountering errors or conscious distortions of geographic facts is higher than in the case of open datasets. In the larger crowdsourced databases such as Wikimapia or Yandex's People Map, such probability is lower because of the large number of contributors, which leads to faster error correction. The situation with small databases is more challenging: often, these projects are curated by small groups of users with limited time and fnancial resources. While the data offered by them can still be valuable (or even unavailable by other means), it is important to critically assess their quality and identify (as much as possible) who contributes to the database and for what ends.

#### *32.2.2 Open Datasets*

Besides the rise of volunteered geographic initiatives, the unprecedented ease of accumulating and sharing geospatial data resulted in the distribution of open datasets produced by certifed actors such as state institutions and mapping agencies. Generated using authoritative geographic sources, these datasets are characterized by higher data quality when compared with crowdsourced databases. While the turn toward open data that are made available through offcial portals (for instance, data.gov or europeandataportal.eu) originated in the West, where these datasets are often employed in academic research on the subjects varying from earthquakes to government institutions' budgets (Ding et al. 2010; Shadbolt et al. 2012), Russia increasingly joins the open data movement.

A number of Russian offcial agencies make their data available through online portals, such as Russian Open Data Portal (RODP) (data.gov.ru) or Open Data Portal of Moscow City Government (data.mos.ru) (Bundin and Martynov 2015; Koznov et al. 2016; Repponen 2018). A selection of Russian portals, where open datasets are published, is provided in Table 32.1. Despite being subjected to a number of drawbacks, including often limited data preprocessing, absence of unifed data standards for different organizations, and the lack of application programming interfaces (APIs) (fftin 2017), these portals provide access to a variety of unique geospatial datasets from different domains, varying from culture (e.g., the dataset on the geospatial distribution of places related to Russian poetess Anna Akhmatova in Moscow [Data.gov

**Table 32.1** Open datasets in Russian geoweb

2016]) to crime (e.g., data about the number of committed, resolved, and unresolved crimes by region in Russia [Data.gov 2014]; for more on government data, see Chap. 23).

Two platforms which are of particular interest in this context are Russian Open Data Portal (RODP) and Open Data Hub (ODH). Both platforms provide a large number of datasets (22,233 for RODP and 8151 for ODH) from multiple Russian organizations (1102 and 42 organizations, respectively). These organizations vary from the federal organizations (e.g., the Ministry of Justice or the Federal Statistics Service) to the local ones (e.g., Tomsk Oblast administration). Not all of these datasets deal with geospatial information, but many of them do and can serve as a valuable source of data for geospatial research.

#### *32.2.3 Social Media*

As noted in other chapters of the handbook (see Chapters 20 and 30 on social media use in the context of Digital Russian Studies), social media platforms constitute a major source of digital data. Geospatial data are not an exception as the majority of social media platforms provide in one form or another information about the location of their users and/or content produced. Stock (2018) notes that the majority of studies focus on a few Western platforms, such as Twitter and Flickr, which have accessible APIs and contain geotagged content.4 This combination allows both identifying the location in which some content available through the platforms is produced and also searching and retrieving data for the specifc geographic range (e.g., for collecting messages and images produced within recreational areas to trace visitors' numbers [Tenkanen et al. 2017] and behavior [Sessions et al. 2016]).

In addition to Western social media platforms, Russian geoweb includes several major local platforms, such as VK (also known as VKontakte), Odnoklassniki, and Moj Mir. Among these platforms, however, only VK provides easy access to its API, which allows retrieving a wide range of geospatial data (Tikunov et al. 2018). Specifcally, VK API includes a number of functions also known as methods, which can be used for data extraction (for more on social networks, see Chaps. 19 and 30).5

The most common type of geospatial data provided by VK is the one on the country and the city/town of residence, which constitutes part of user profle (Zamyatina and Yashunsky 2018). In the case of publicly available profles, these data can be retrieved using users.get method. The method takes as its input user ids which are of interest for the researcher and the list of felds that have to be retrieved ("country" and "city" are a common choice). These data can be further enriched and/or verifed via other profle felds available on VK such as the ones on employment and education.

Besides data available as part of user profles, VK also provides access to check-in data, which can be retrieved via places.getCheckins method. The method takes as input latitude and longitude coordinates and returns posts made within the specifed area together with ids of users who published them. Similarly, VK allows retrieving images uploaded by users together with these images' geographic coordinates using photos.get method. The method returns geographic coordinates of retrieved images if these coordinates are provided by the user. Using this method, it is possible to retrieve a sample of images from specifc geographic regions in order to, for instance, examine the ways in which these regions are represented online (Tikunov et al. 2018).

## 32.3 Location Extraction

After choosing the specifc data source(s) and acquiring actual data, the next step is to process these data. In the case of geospatial data, the major purpose of processing involves the extraction of specifc location(s) to which the data refer to or represent. Depending on the data format and available metadata, the process of location extraction can be as simple as retrieving exact geotags present in the metadata or mapping the location name to data from a geographic information system. In other cases, it can be more complex and involve the use of machine learning techniques to recognize the names of geographic entities in visual or verbal texts or to infer the location based on online user activity.

*Geographic coordinates extraction from document metadata*. The easiest and most common (Stock 2018)—way of detecting location is by using geographic coordinates included in the document (meta)data. Such an approach is particularly applicable for data available from open datasets as well as crowdsourced databases, which often include specifc geographic coordinates. Additionally, some platforms such as Twitter and VK provide geographic coordinates for some types of their content.6 The question of validity of these data, however, is an open one: especially in the case of geotagged content from social media platforms, there is also a need to differentiate between the place in which the content was published and the place to which it actually refers.

*Location name extraction from document metadata*. In the cases when geographic coordinates are not provided, one of the alternatives is to extract place names from the metadata. This process usually consists of two steps: (1) toponym recognition: that is, identifcation of the toponym in the body of the metadata (Sagcan and Karagoz 2015), and (2) toponym resolution: that is, assigning of geographic coordinates to the recognized toponyms (Lieberman and Samet 2012). An example of the platform for which this approach can be highly benefcial is VK, which allows users to report their place of residence in their profles. While the platform itself does not connect these data to a geographic information system, the location names can be retrieved via VK API and then connected to a geocoding service (e.g., Google Maps) to generate geographic coordinates (Lee et al. 2013; Baucom et al. 2013).

The most popular approach to location name extraction from the metadata is the gazetteer-based one, where the extracted location names are matched with the list of geographic named entities such as the ones provided by GeoNames (https://www.geonames.org/). Because of the limited number of gazetteers for the Russian language, such lists are often taken from Wikipedia or from a few training datasets such as FactRuEval (Starostin et al. 2016). At the same time, this approach suffers from a number of issues, including, for instance, intended or unintended mispronunciation (such as *Maskva* instead of *Moskva*) or instances of double naming (e.g. *Sankt-Peterburg* and *Piter*). To address these limitations, more complex approaches were proposed (for reviews, see Leidner 2007; Leidner and Lieberman 2011); a recent study comparing different approaches to the task indicates that approaches using lexical context of toponyms and their importance (e.g., by solving typonym-related ambiguity by always preferring options with the largest population) perform particularly well (Weissenbacher et al. 2019).

*Location name extraction from raw tex*t. This approach is similar to the location name extraction from document metadata and involves the same two steps: toponym recognition and toponym resolution. However, unlike the former approach which relies on the document's metadata, the latter one takes as input raw text data. Stock (2018, 219) notes that a major beneft of this approach is that it can be used for any text-based message (e.g. photo/video descriptions or blog posts). This approach tends to be less accurate than the one relying on supplied geotags, especially as geographic names are often ambiguous. However, it is often the only way to extract location in the cases when geographic coordinates are not provided.

The usual way of extracting location from raw texts employs the named entity recognition approach: that is, automatic detection of the words which refer to certain geographic locations. The process of detection is based on named entity recognition tools, such as Stanford or GATE, which combine machine learning techniques with pre-made geographic gazetteers, such as GeoNames or OpenStreetMap (Stock 2018, 220; for practical examples see Jaiswal et al. 2013; Inkpen 2016; Bassi et al. 2016).

While most of the research on named entity recognition approach is tailored to the English language, in recent years the growing number of works employs this technique for the Russian context.7 Because of the limited number of pre-made Russian gazetteers, a number of studies (see, for instance, Sysoev and Andrianov 2016) employ Wikipedia as a source of information. Additionally, there are several training datasets which include geographic data. An example of such a dataset is FactRuEval, an open annotated corpus of Russian texts.8 The paper by Ivanitskiy et al. (2016) discusses in more details how FactRuEval can be used for geographic named entity retrieval from Russian sources.

*Location inference from user activity*. In some cases, the documents in question do not provide explicit references to the geographic entity; however, even under these conditions, it is still possible to infer the location based on earlier user activity. Jurgens et al. (2015) summarizes several approaches based on user networks which can be applied for dealing with this task. The majority of these approaches involve identifcation of users sharing the closest connections with the user in question and then using data from them to infer the user's location.

Another approach is based on content produced by the user online. A number of studies (Cheng et al. 2010; Chang et al., 2012; Han et al. 2014) discuss the possibility of inferring geographic location from local terms also known as location indicative words (LIWs) (Han et al. 2014). LIWs are terms which are particularly representative for specifc places, either because of being indicative of certain locations (e.g. "rockets" for Houston) or language practices (e.g. "howdy" for Texas). Consequently, LIWs can be used to predict the location of a user who uses these terms through machine learning techniques.

Several studies (Han et al. 2014; Mourad et al. 2017) apply the latter approach to detect location based on Russian LIWs. The main idea behind it is to acquire textual data produced by users at certain geographic locations (Twitter was used in the above-mentioned studies, but the same principle can be employed for Instagram or VK) and then create separate text corpora for each location in question. Then, for each location LIWs are extracted and the model is trained. Han et al. (2014) offer a detailed discussion of different approaches toward LIWs extraction and show that information gain ratio approach provides the best performance.

*Location name extraction from image*. While location extraction from images is more challenging than from textual data, several techniques allow addressing this task. The frst of them is based on the use of geographic information, in particular geotags, embedded in the image metadata. Usually provided in EXIF format (Stock 2018, 222), these metadata are created by the camera and include data about the image creation date, camera settings, and geolocation. Some platforms, such as Flickr, provide API access to these metadata, thus allowing to search these platforms' contents for images from specifc areas and specifc time span (McDougall and Temple-Watts 2012).

The second technique can be employed in the cases where no metadata is provided and involves the comparison of image similarity. Stock (2018, 222) identifes a number of approaches used to address this task, varying from the use of scale-invariant feature transformation (SIFT) for comparing selected image features (Crandall et al. 2009) to color and texton histograms employed in the domain of computer vision (Gallagher et al. 2009). After identifying these features for the image in question, they can then be compared with large image datasets (e.g., coming from Flickr) to identify similarities.

*Location name extraction from video*. Similar to location extraction from image, several other major approaches for location extraction can be identifed. The frst of them involves the use of video metadata (e.g., geographic coordinates produced by Global Positioning System [GPS] and compass sensors, which are embedded into video descriptions). This information can be used to identify the region in which the video was produced. Then geoinformation services (e.g., OSM) can be used to extract data about visible objects in the region (e.g., monuments or offce buildings) in 2D or 3D.9 Using OSM data, the descriptive tags can be generated for different objects in the area (e.g., their addresses and names), and then the object models can be compared with objects from the videos. Then, the relevance of each tag for specifc video frame is calculated (i.e., to detect if a specifc tag is present or absent on the frame) (Shen et al. 2011). While currently there are no papers applying this approach to the Russian context, such an approach is language-agnostic and can be implemented for any video independently of the language in which it is produced, until there is some metadata available.

The second approach can also be employed in the cases where no video metadata is present and combines audio and visual features of videos for identifying the location shown in them. For this purpose, a geotagged collection of videos is required; this collection is then used for calculating the audiovisual similarity with non-geotagged content. Specifcally, visual frames and soundtrack are extracted from the videos, and then visual and acoustic features are computed for each one of them. Following the extraction, k-nearest neighbor algorithm (a classifcation algorithm, which classifes the unknown objects according to the classes of k closest neighbors) is employed to identify geotagged videos which look and sound more similar to the non-geotagged content (Sevillano et al. 2015).

## 32.4 Location Use

After the location is extracted and identifed, it can be used for actual analysis. As I noted earlier, the advantage of geospatial data is their versatility and applicability for addressing a wide range of research questions. In this section, I scrutinize some of the uses of geoweb in the context of Digital Russian Studies, from mapping the spatial distribution of phenomena and specifying actors' identities and relationships to scrutinizing the role of location in online cultural practices.

*Mapping the spatial distribution of phenomena*. An important feature of using geospatial data is its rich potential for mapping socioeconomic and (geo) political phenomena. These phenomena vary from tourist mobility (e.g., spatial and temporal dimensions of tourist fows [Lu and Stepchenkova 2015; Kirilenko and Stepchenkova 2017]) to electoral fraud during Russia's federal elections (Kobak et al. 2016) and migration patterns (Zamyatina and Piliasov 2013). Geotag data can be also used for mapping contested phenomena, when offcial reports are often subjected to censorship or disinformation, such as the involvement of Russian troops in the confict in Eastern Ukraine based on Instagram data (Czuperski et al. 2015). While the use of geospatial data for studying such contested cases often raises multiple concerns (e.g., concerning the reproducibility and the quality of available data), it can still provide valuable insights for researchers.

*Specifying actor identities and relationships*. Another common use of geospatial data is for identifying specifc actors and tracking connections between them. Such tasks are particularly common for studies in political communication and/or disinformation online: for instance, Zelenkauskaite and Balduccini (2017) used geospatial data to specify the origins of users commenting on Russian language news portals in Lithuania, whereas Helmus et al. (2018) employed geoweb to track the identities of users involved in Russian propaganda and counter-propaganda efforts on Twitter. Disinformation, however, is not the only subject which can be investigated in this context as shown by Smirnov et al. (2016), who used geospatial data for identifying friendship networks between youngsters on VK.

*Scrutinizing digitization of cultural practices*. The use of geospatial data increasingly becomes part of the mediatization of cultural practices, varying from war remembrance to tourism. Bernstein (2016) in his research on Second World War memory in Russia showed how the formation of a geotagged database of Soviet monuments enriches existing memory practices by producing virtual embodiments of existing memorials and re-iterating the mainstream Soviet narrative of the war. Another example is the use of geotagged images as part of sharing—and shaping—travel experiences as shown by several studies focused on the use of geospatial information to examine vacation culture in Russia (Kirilenko and Stepchenkova 2017; Tikunov et al. 2018).

*Exploring identity narration*. Besides extensive possibilities for tracking phenomena, digital platforms also enable new ways of (re)-imagining individual and collective identities. A number of studies (Stefanidis et al. 2013; Croitoru et al. 2015) suggest that geospatial data can serve as a strong identifer of group belonging and individual self-expression. Examples of such identifcations are, for instance, elements of individual user profles on Wikipedia, where userboxes are employed for declaring individuals' interests, preferences, and personal details (Neff et al. 2013). In the context of Digital Russian Studies, these means of self-expression often deal with geospatial data (e.g., place of residence [Dounaevsky 2014]) or geopolitical aspects of territoriality (e.g., belonging of the Southern Ossetia to Georgia). Another example is the use of geolocation data for producing digital maps of the confict in Eastern Ukraine (e.g., MilitaryMaps or Liveuamap), which are used to visualize the borders of imagined communities (e.g., of the self-declared confederation of Novorossiya [Makhortykh 2018]).

## 32.5 Geospatial Data and Research Ethics

The advent of big data research opens unprecedented possibilities for studying different phenomena, but it also raises multiple ethical concerns. Some of these concerns are related to the general considerations of using big data for research purposes (e.g., acquiring proper permissions for data use [Richards and King 2014]), but some are rather specifc for geospatial data, in particular in the Russian context. In this pre-fnal section, I will briefy discuss three of these concerns: validity, privacy, and reliability.

*Privacy.* Security and privacy are two key concerns of using geospatial data for research purposes (Li et al. 2016). The use of portable GPS receivers in mobile devices together with the enrichment of social media data with geospatial information raise concerns about the use of these data for tracking individuals' actions and movements (Loebel 2012). While such data can be benefcial for many types of research, their use also requires the researcher to recognize the potential consequences for the privacy of users. Such consequences are particularly important in cases dealing with highly sensitive and/or polarizing subjects, where the use of geotag data can cause material or immaterial harm for research participants.

The privacy risks are even greater when geotag data is used for studying phenomena occurring in authoritarian states. An example of a highly privacysensitive subject is research on anti-government protests, where geospatial data can be (ab)used to identify the location of individual protesters and expose their involvement in the protests, thus bringing legal repercussions by the state. To address this concern, the use of personal data should be minimized and (pseudo)anonymization techniques should be used. On the offcial level, however, Russian legislation is still catching up with the notion of big data and their uses for research purposes (for an overview, see Zharova and Elin 2017). Consequently, the protection of the data rights of individuals in Russia is still signifcantly less strict than in the European Union (EU) countries, where it is regulated by the EU General Data Protection Regulation (GDPR).

*Validity*. Sheppard (2005, 74) defnes validity as the degree to which the use of a specifc instrument or fnding is sound, defensible, and well-grounded for the issue at hand. The question of validity is of particular relevance for the use of geospatial data, because of their signifcant potential for being used for manipulation: both through the data and their visualization (Sheppard and Cizek 2009). In some cases, the use of data can be invalidated by their wrong interpretation (i.e., when geospatial information is used to prove a point which is incorrect), whereas in other cases obscure visualizations of data can mislead the public.

An example of the invalid use of geographic data is the contrasting reporting of the 2018 clashes near Chigari village in Eastern Ukraine. Both the Ukrainian authorities and pro-Russian insurgents produced video records showing them controlling certain landmarks, which were claimed to be related to the village in question. Despite these claims, not all of the shown landmarks were related to Chigari and eventually it was proven that the village was controlled by the Ukrainian army, but not before causing signifcant confusion. A possible way of increasing validity according to Sheppard and Cizek (2009, 2112) is to use more fexible and interactive approaches for geospatial data analysis, thus allowing end users more control over results' reporting.

*Reliability*. Sheppard (2005) argues that reliability is another major concern of using geospatial data. Unlike validity, which focuses on the possible (ab)uses of geospatial data for drawing invalid conclusions, reliability concerns the internal consistency of analysis and the possibility to produce the same results under similar conditions. The issue of reliability is of particular importance for analyses produced via crowdsourced databases and social media as both data sources are subjected to frequent changes and often provide limited possibilities for consistent data access.

An example of reliability issues which accompany the use of geospatial data is MilitaryMaps mentioned earlier. This crowdsourced database aggregates updates from conficts in the post-Soviet space as well as in the Middle East and provides geotags indicating the movement of troops and outbursts of violence. From September 2018, however, the previously open project switched toward paid subscription, which made it harder to recreate analyses based on MilitaryMaps data. Another reliability-related limitation of the project is its reliance on the GoogleMaps framework, which stores markers that are added to the map only for a one-year period. Sheppard and Cizek (2009) suggest that the main way to amend these and other reliability issues is the use of more prescriptive approaches to data analysis and presentation based on recognized quality standards.

## 32.6 Conclusions

In this chapter, I discussed the possible uses of data available through geoweb, the integrated and discoverable collection of geographically related web services and data (Lake and Farley 2009), in the context of the Digital Russian Studies. Increasingly employed for academic studies worldwide, geoweb data are of particular importance for Russia-centered digital research, serving both as a pivotal factor for making and verifying knowledge claims by regional actors and an integral means of producing individual and collective narratives on subjects varying from international conficts (Shim 2018) to presidential elections (Kobak et al. 2016).

The use of geoweb for Digital Russian Studies is facilitated by the large volume of geospatial data available today. As I discussed above, these data can be divided into three broad categories according to their source: (a) crowdsourced databases, (b) open datasets, and (c) social media. Out of these three, social media data are the hardest to get and often require extensive pre-processing; however, they are also applicable to a wide range of research questions, in particular the ones related to inter-user interactions. Furthermore, the largest Russian social media platform, VK, provides public access to multiple forms of geospatial data (e.g. users' self-declared place of residence/work and check-in data), thus enabling more possibilities for data collection than many Western platforms.

The research possibilities provided by geospatial data are amplifed by the quickly developing toolkit of analytical techniques used to extract geographic location from different data formats. The complexity of techniques varies depending on the data format. In the simplest scenarios, geographic coordinates or the location's administrative address are provided in the metadata and only has to be matched with data from existing geographic information systems. In the more diffcult scenarios, the location has to be extracted from the content or inferred from the user's earlier activity using a combination of machine learning and geographic gazetteers. Much still can be done to better adapt these techniques to the Russophone context, in particular in terms of improving named entity recognition techniques and developing better gazetteers. Yet, even in the current state of research, there are plenty of possibilities for using the mentioned techniques for different types of Russia-centered studies.

The importance of location extraction techniques is exemplifed by the wide range of research questions to which Russian geospatial data are applicable. These research questions vary from the spatial distribution of socioeconomic and political phenomena, such as migration and electoral fraud, to the verifcation of knowledge claims about the presence of Russian troops in Eastern Ukraine to the analysis of mediatization of cultural practices of war remembrance and the exploration of narrative uses of geospatial data for communicating individual and collective identities.

Despite their signifcant potential for Digital Russian Studies, the future of geospatial data is not fully clear. The existing concerns about complex interrelations between privacy and geospatial data are amplifed by the current calls for tightening the government's control over the Internet in Russia, leading to increasing restrictions on data retrieval from Russian platforms' APIs, including VK. These limitations might curb the amount of geospatial data available from social media; however, the growing number of open datasets and crowdsourced databases suggests that Russia's geoweb will remain a valuable research venue for Digital Russian Studies for years to come.

## Notes

## References

Assessing the Usability of Social Media Data for Visitor Monitoring in Protected Areas. *Scientifc reports* 7 (1): 1–11.

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## Index1

### **A**

Academic activity, 485, 490 Academic corruption, 492 Academic culture, 484 Academic genre, 485 Academic plagiarism, 9, 483–497 Activism, 87, 135–150, 162, 206, 207, 209–214, 255, 261, 272, 537 Affordance analysis, 372 Amateur literature, 265–267 Antiplagiat, 498n6 Archive, 2, 8, 9, 158, 353–366, 371–385, 391, 392, 438, 570–572 Artifcial intelligence (AI), 7, 45, 46, 53, 55, 60, 62, 65, 82, 169n4, 180, 181, 270, 586 Authenticity, 353, 366, 374, 375, 484, 485, 490, 492–497 Authorship, 166, 245, 246, 485 Automated indexing, 321 Automated text analysis, 338, 417 Automatic document processing, 320, 322, 324, 328, 331 Automatic sentiment analysis, 9, 501–511 Automatic text categorization, 330 Average distance, 526, 527

#### **B**

Betweenness, 525, 528, 529

### **C**

Centrality, 48, 373, 525, 526, 528, 529, 550, 552 Character network, 533 Civil activity, 338 Civil law, 83, 392 Clique, 544, 545 Clique analysis, 545 Close reading, 124, 427, 433–435, 443, 455, 520 Collaborative consumption, 230–233 Community network, 544 Complex affordance, 374, 375, 382–384 Computational analysis, 427, 430, 436, 438

1Note: Page numbers followed by 'n' refer to notes.