# Quantitative Analysis of Cultural Dynamics Seen from an Event-based Social
Network

Bayu Adhi Tama  University of Maryland, Baltimore County (UMBC)BaltimoreMDUSA
,  Jaehong Kim  Korea Advanced Institute of Science & Technology
(KAIST)DaejeonRepublic of Korea ,  Jaehyuk Park  KDI School of Public Policy
and ManagementSejongRepublic of Korea ,  Lev Manovich  City University of New
YorkNYUSA and  Meeyoung Cha  Institute for Basic ScienceDaejeonRepublic of
Korea

(2018)

###### Abstract.

Culture is a collection of connected and potentially interactive patterns that
characterize a social group or a passed-on idea that people acquire as members
of society. While offline activities can provide a better picture of the
geographical association of cultural traits than online activities, gathering
such data on a large scale has been challenging. Here, we use multi-decade
longitudinal records of cultural events from Meetup.com, the largest event-
based social networking service, to examine the landscape of offline cultural
events. We analyze the temporal and categorical event dynamics driven by
cultural diversity using over 2 million event logs collected over 17 years in
90 countries. Our results show that the national economic status explains 44.6
percent of the variance in total event count, while cultural characteristics
such as individualism and long-term orientation explain 32.8 percent of the
variance in topic categories. Furthermore, our analysis using hierarchical
clustering reveals cultural proximity between the topics of socio-cultural
activities (e.g., politics, leisure, health, technology). We expect that this
work provides a landscape of social and cultural activities across the world,
which allows us to better understand their dynamical patterns as well as their
associations with cultural characteristics.

Event-based social networks, cultural differences, event dynamics,
quantitative analysis.

††copyright: acmcopyright††journalyear: 2018††doi: XXXXXXX.XXXXXXX††journal:
JACM††journalvolume: 37††journalnumber: 4††article: 111††publicationmonth:
8††ccs: Social and professional topics Cultural characteristics††ccs: Applied
computing Law, social and behavioral sciences††ccs: Human-centered computing
Empirical studies in collaborative and social computing

##  1\. Introduction

Social events are spatial-temporal phenomena, where each event has its own
unique characteristics of interactions among the participants, topic,
description, and geographic region. In particular, a live social event on a
certain topic facilitates a unique offline human interaction, as they serve
the special purpose of social gathering (Getz, 2008). The expectations,
emotions, and attitudes of participants evolve, resulting in a unique human
experience, even when events are held on the same topic. Because of such a
one-of-a-kind experience, offline live events are appealing, even fascinating,
and the organizer’s objective is to cultivate a ’once in a lifetime’ image for
them (Fenich, 2013; Getz and Page, 2019). Furthermore, virtual events, which
are conveyed via various social media platforms, provide something of interest
and value to people and the tourism industry; they are different types of
event experiences (Rojek et al., 1997; Rogers et al., 2014).

Live events are frequently used to disseminate information. One of the most
important characteristics of globalization in the twenty-first century is the
adoption of different ideas, interests, beliefs, and lifestyles across
cultural boundaries (Boli and Lechner, 2015; Choudhary et al., 2019; Frigo,
2017). Small-scale ethnographic studies can capture local examples of these
processes in specific moments. In contrast, computational analysis of social
network services, blogs, or websites can provide a global landscape, albeit at
the risk of overestimating the impact of globalization (Manovich, 2020; Can
and Alatas, 2019; Hamidi et al., 2011).

This research employs a novel method for studying cultural globalization by
using longitudinal data from millions of offline events scheduled through
Meetup.com. This platform allows users to communicate virtually with others
and meet in person to discuss whatever and wherever they are interested in
(Liu et al., 2012). The existence of a physical event on a particular topic in
a specific location implies that enough people in the area are interested in
the topic. The geographic binding of events is perhaps the most critical
difference between offline events like those hosted on Meetup and other online
communities, which do not restrict who can join and thus give the impression
that all ideas are present everywhere. As a result, we assume that offline
events, rather than their online counterparts, provide better geo-contextual
signals on the topical interest of users in a given region (Manovich, 2020).

Meetup.com, as a prominent social media platform specializing in hosting
offline cultural events, has its own design and conventions that define how
people interact with the platform. Many of its topics, for example, are
amusing and attract participants, particularly the civically disengaged. It
also allows people to connect regardless of their ethnicity, social class, or
geographic location. Book clubs, for example, can unite people of all ages,
classes, and places (Sander, 2005; Sharma et al., 2019; Pakrashi et al.,
2018). The platform’s versatility in terms of how it is used for not only
enjoyment but also social involvement and participation is unique. Meetup.com
mixes virtual and real elements to enhance social capital from strangers,
which makes it one of the best platforms for analyzing cultural events at a
regional level.

There has been multidisciplinary research focusing on specific patterns of
social media usage, such as the motivations for user participation and
consequences (Zheng and Lee, 2016; Khan, 2017). Prior research has shown that
hashtagging behavior as a form of social media communication can be culturally
affected (Sheldon et al., 2017). Other works, such as (Sheldon et al., 2017;
Hong and Na, 2018; Lin and Ho, 2018), have discussed the cultural implications
of social media communication. Social platforms also enable political
participation, ideological categorization, and social movement (Koiranen et
al., 2020; Shen et al., 2020; Yu and Oh, 2018). Moreover, the work of (Lee et
al., 2018) examines the correlation between social media usage and political
polarization through increased political engagement. However, no prior study
has investigated the specific link between cross-cultural differences and
event-based social networks at the country level due to a lack of empirical
data.

Here, by proposing an organic approach, we advance the research on the
relationship between cultural traits and the event dynamics patterns of
Meetup.com in many countries worldwide. Event dynamics of each country are
examined using various behavioral features, including the participating group,
temporal, and category-related patterns. Furthermore, our approach identifies
the hierarchical predominance of event categories across multiple countries
and argues that scrutinizing the inter-connected hierarchical structure is a
crucial step toward a wider understanding of cultural structures and traits.
We show that the category prevalence is shaped by the hierarchical structure
of cultural traits, specifically the Hoefstede (Hofstede et al., 2010) and
Inglehart-Welzel (Inglehart and Welzel, 2010b) cultural dimension theory. Our
dataset of over 2 million events organized in over 17,000 cities in 90
countries span 17 years between 2003 and 2019 (see Figure 1(a)). The dataset
includes its category, date, location, and short text description posted by
event organizers, as shown in the main interface (Figure 1(b)). Event
organizers select the categories from the 33 available categories, which are
grouped into eleven mid-level categories and seven top-level categories (see
Appendix).

(a)

(b)

Figure 1. (a) Number of events (log-scaled) at country level. Cultural events
through Meetup.com are happening across all continents, although more
concentrated in North America, Western Europe, and Southeast Asia. (b) A
Meetup.com example page for a hiking event in Seoul, which includes
information about the event’s name, location, date, and description.

The remainder of the paper is organized as follows. Section 2 describes how
overall, temporal, and category-related behavior patterns are measured. The
results of this study, presented in Section 4, include the correlation results
and hierarchical structure of the category prevalence. We discuss our findings
in Section 5, and lastly, we conclude this paper with some remarks in Section
6.

##  2\. Methods

###  2.1. Measuring Overall Behavior Pattern

Throughout the investigation, an overall behavior pattern is examined via a
summary statistic consisting of the total event count (TEC) for the period,
the total number of transactions (N${Trans}$), the average number of events
per transaction ($\mu{Trans}$), and the standard deviation of events per
transaction ($\sigma{Trans}$). We specify the transaction table of each
country as follows. Let transaction $t$ denote a set of event categories that
exist on the same day in a certain nation, $\mathcal{T}$ denote a set of $m$
transactions, $\mathcal{T}=\\{t_{1},t_{2},...,t_{m}\\}$, denoting a
transaction table, and $\mathcal{E}$ denote a set of $n$ different event
categories $\mathcal{E}=\\{e_{1},e_{2},...,e_{n}\\}$. An $\mathcal{S}$ is a
collection of event categories, such as $\mathcal{S}\subset\mathcal{E}$.
Because the distributions of TEC and NTrans are positively skewed, we apply
log transformation to them. We use the coefficient of variation (CoV) to
measure the relative variability of a country’s event patterns:

(1) $CoV=\frac{\sigma_{Trans}}{\mu_{Trans}}$

The country is more likely to have unequal distribution of events across
transactions as $CoV$ rises and vice versa.

###  2.2. Measuring Temporal Behavior Pattern

Three time frames, namely, week (W), month (M), and quarter (Q), are used to
measure temporal behavior patterns at various granularities. To calculate the
variability of a country’s event pattern through time windows, we define
$\mathcal{E}^{W}=\\{e_{Mon},e_{Tue},...,e_{Sun}\\}$ as the total of event $e$
produced in each week; $\mathcal{E}^{M}=\\{e_{W1},e_{W2},e_{W3},e_{W4}\\}$ as
the total event $e$ produced in each month; and
$\mathcal{E}^{Q}=\\{e_{M1},e_{M2},e_{M3}\\}$ as the total of event $e$ in each
three-month intervals. We calculate the variability of event patterns in
weekly ($\mu_{W}$), monthly ($\mu_{M}$), and quarterly ($\mu_{Q}$)
measurements for each country.

The average cosine similarity coefficients across adjacent time intervals are
used to calculate a country’s consistency in creating events during a monthly
(PM) and quarterly (PQ) observation period. We calculate the monthly
persistence period by aggregating the number of events at the weekly interval
(e.g., four elements per month) and obtaining the proportion of each element.
Monthly persistence is computed as:

(2) $P_{M}=\frac{\sum_{i=0}^{n-1}cos(Z_{i},Z_{i+1})}{n}$

where $Z_{i}$ is the vector representing the relative amount of events created
in each monthly interval in a given month $i$, and $n$ is the number of months
in a country. Similarly, for the quarterly observation periods, we construct
$P_{Q}$ by aggregating the number of events monthly (i.e., three elements for
each quarter). A persistence $P$ value of 1 indicates that the relative number
of occurrences remains constant over time periods and vice versa.

Bursty dynamics ($\beta$) is a method of calculating the intensity of a large
number of events over a short period of time. The inter-event times are first
calculated as the daily difference between two adjacent transactions. Inter-
event times are described as $\gamma_{j}=\Gamma_{j}-\Gamma_{j-1}$, where
$\Gamma_{j}$ signifies the transaction created at time $j$. The following
formula can be used to determine bursty dynamics:

(3) $\beta=\frac{\zeta-1}{\zeta+1}$

where $\zeta$ is specified as $\zeta=\frac{\tau}{\sigma}$ with $\tau$ and
$\sigma$ indicate the average and standard deviation of the transaction’s
inter-event timings, respectively. When the burstiness parameter $\beta$ is
-1, the country’s event pattern is completely steady. The country’s event
pattern is entirely random when $\beta=0$. Finally, a value of 1 for the
parameter $\beta$ results in significant and unexpected increases in the
number of occurrences in each transaction.

###  2.3. Measuring Category-related Behavior Pattern

The category-related behavior metrics are calculated using the total number of
events to assess the frequency of occurrences in various categories. The
number of categories ($N_{Cat}$) indicates how many separate categories were
covered during the period. In addition, we assess the diversity of events
produced by each country by examining the diversity of categories, $D_{Cat}$,
as defined by the Shannon-Wiener diversity index’s relative score:

(4) $D_{Cat}(c)=-\frac{\sum_{k=1}^{N_{Cat}}p_{kc}log(p_{kc})}{logN_{Cat}}$

where $N_{Cat}$ is the number of unique categories of country $c$,
$p_{kc}=\frac{V_{kc}}{\sum_{k=1}^{N_{Cat}}V_{kc}}$ and $V_{kc}$ is the number
of events created by the country $c$ in the category $k$. A low $D_{Cat}$
score indicates that the country’s events are focused on only a few
categories. A high value of $D_{Cat}$, on the other hand, indicates that a
country’s number of events is evenly spread throughout all categories.

###  2.4. Measuring Category Prevalence

We create a matrix that shows how common each category is in each country.
Each element in this matrix, $M_{kc}$, represents the normalized rank of
category $k$ in country $c$. First, we select a country with $k\geq 10$,
resulting in 63 countries to be evaluated further. We sort each country’s
top-10 categories and then use the union to combine the results. This results
in 28 top categories, which are found in 63 countries. Each category is sorted
in ascending order from the highest to the lowest for each country, based on
the total event count (TECk). We make the implicit assumption that a larger
TECk value is preferred. As a result, the category $k_{i}$ is ranked 1 for
each country, with $TEC_{ki}>TEC_{ki^{\prime}}\forall
i^{\prime},i,i^{\prime}\in\\{1,2,...,n\\},i\neq i^{\prime}$ is ranked 1. Any
missing category is given a rank of 28, indicating that it is not considered
important. Formally, a pseudo-code for creating a category rank matrix is
summarized in Algorithm 1.

Algorithm 1 Create category rank matrix

country $c$, category $k$, total event count of each category $TEC_{k}$.

Previous top-10, $k_{A}=()$

for $c=1,2,...,90$ do

if $k\geq 10$ then

Sort $TEC_{k}$ in ascending order

Current top-10, $k_{B}$ $\leftarrow$ get top-10

Top-10 $k$ $\leftarrow$ $k_{A}$ $\cup$ $k_{B}$

$k_{A}$ $\leftarrow$ $k_{B}$

end if

end for

$M_{TEC}$ $\leftarrow$ Top-10 $k$ $\Join$ $TEC_{k}$

$M_{kc}$ $\leftarrow$ Assign rank to each column of $M_{TEC}$

##  3\. Data

###  3.1. Data Description

The data used for this study is comprised of Meetup events from 146 countries
gathered from February 13, 2003, to May 5, 2019, which was collected at the
City University of New York using the Meetup API by the author. The data
includes the date, category, geographic location, and short text descriptions
posted by event organizers. A total of 2,635,724 event samples were recorded.
To narrow this study’s scope, we look at the top-90 countries in terms of
total event count (TEC), in which a country having fewer than 18 events during
the designated period is omitted. Finally, a total of 2,635,508 event samples
were included for further analysis. Figure 2 shows an example of the
spatiotemporal evolution of Meetup events across time. A total of 33 different
categories were covered in only four countries, while other countries had
fewer (see Table 1). A list of 33 categories, including the corresponding mid-
and top-level categories, is presented in Table 2. Table 1 summarizes the
dataset used in the analysis.

Figure 2. Cumulative number of events (TEC) for each category in six major
continents across the timeline. Note that TEC is normalized with z-scores.

###  3.2. Normality of the Data Distribution

We examine the normality of logarithmic total event count (TEC), logarithmic
population, logarithmic GDP per capita (GDP), human development index (HDI),
social progress index (SPI), mobile cellular subscriptions per 100 people
(MSubs), and individuals using the Internet as a % of the population (IntUs)
of the top-90 countries. The tests indicate that TEC and GDP follow a normal
distribution, while population, HDI, SPI, MSubs, and IntUs do not follow a
normal distribution. The $p$-values of the Jarque–Bera test for TEC,
population, GDP, HDI, SPI, MSubs, and IntUs are 0.417, 0.0016, 0.204, 0.014,
0.091, 0.004, and 0.081, respectively. Hence, we fail to reject the null
hypothesis that the data is normally distributed for TEC and GDP but not for
the population, HDI, SPI, MSubs, and IntUs. The linear Q-Q plot (see Figure 3)
confirms the normality of TEC and GDP distributions.

Figure 3. Q-Q plots of logarithmic TEC, logarithmic population, logarithmic
GDP, HDI, SPI, MSubs, and IntUs.

##  4\. Results

###  4.1. Association Between Event Dynamics and Socioeconomic Performances

Variations in event dynamics patterns are strongly linked to disparities in
economic conditions between countries. These correlations provide support for
the external validity of the Meetup.com platform. As an illustration, consider
that overall, temporal, and category-related traits positively correlate to
all socioeconomic indicators except the bursty dynamics. We observe that
bursty dynamics negatively correlate with prosperity (as indexed by GDP per
capita and human development index (HDI) (Fund, 2019)), social progress
imperative (as scored by basic human needs, foundations of wellbeing, and
opportunity (Imperative, 2020)), and digital development index (as measured by
the number of internet users per 100 people (Union, 2020)) (see Appendix). We
highlight three significant relationships that reveal intriguing findings.
First, Figure 4 reveals a significant association ($R$= -0.22, $p<0.05$)
between bursty dynamics ($\beta$) and foundations of wellbeing. Second, bursty
dynamics and opportunity score based on the social development index (Figure
4b) have a substantial link ($R$= -0.22, $p<0.05$). Finally, we discover that
wealth (as assessed by GDP per capita (Fund, 2019)) has a positive correlation
($R=0.26$, $p<0.05$) with the diversity index of categories ($D_{Cat}$)
(Figure 4c).

Figure 4. There is a link between event dynamics and socioeconomic indicators.
Each panel displays Pearson’s $R$ and $p$ values for the correlation test
between the relevant pair of variables. a. Association between log wellbeing
score and bursty dynamics ($n=83$). b. Association between opportunity and
bursty dynamics ($n=83$). c. Association between log GDP per capita and the
Meetup categories’ diversity index ($n=90$).

###  4.2. Cultural Traits versus Event Dynamics

Features revealed by event dynamics demonstrate a strong correlation to
cultural differences according to Hofstede’s Cultural Scale (Hofstede et al.,
2005) and Ingelhart-Welzel World Value Survey (Inglehart and Welzel, 2010a;
Inglehart, 2018; Inglehart and Welzel, 2010b), except that
masculinity/femininity (see Appendix). We discuss these correlations of
Hofstede’s cultural scale as follows. The perceived utility of social networks
is directly linked to masculine culture’s values of achievement and progress.
However, our findings show no substantial relationship between
masculine/feminine cultures and event dynamics characteristics. The other five
primary cultural attributes and predictors of event dynamics elements are
highlighted. First, we notice systematic differences between individualistic
and collectivistic communities. Individualistic cultures, which value utility
and active engagement in social exchange (Sánchez-Franco et al., 2009), show a
substantial positive connection to total event count (TEC) and other temporal
behavior-related variables (Figure 5a) (see Appendix).

Second, countries with high degrees of uncertainty avoidance have only a weak
negative relationship with the event category variety index ($D_{Cat}$)
(Figure 5b). Third, people in low-power distance cultures are more dependent
and hence more vulnerable to Meetup.com regardless of power status. Our
findings show that bursty dynamics are substantially correlated with high-
level power distance cultures (Figure 5b-inset). Fourth, we highlight a
collection of correlations involving long-term orientation indices in which
specific dynamics properties are significant (see Appendix). We find that the
frequency of events and coefficient of variation (CoV) is related in long-term
orientation societies (Figure 5c). Finally, we observe that indulgence
cultures have significant effects on the average number of events per
transaction ($\mu_{Trans}$) and category variety index ($D_{Cat}$) (Figure
5d).

Figure 5. Hoefstede’s cultural scales and event dynamics aspects are linked.
Each panel displays Pearson’s $R$ and $p$ values for the correlation test
between the relevant pair of variables. The relationship between log
individualism and log total event count (TEC) ($n=74$) (a), or bursty dynamics
($\beta$) ($n=74$) (b). The relationship between log uncertainty avoidance and
the category diversity index ($D_{cat}$) ($n=74$) (c) and the relationship
between log power distance and bursty dynamics ($n=74$) (d). The relationship
between log long term orientation and log number of transactions ($N_{Trans}$)
($n=66$) (e), or coefficient of variance (CoV) ($n=66$) (f). Relationship
between log indulgence and average transaction number of events
($\mu_{Trans}$) ($n=63$) (g), or category diversity index ($D_{cat}$) ($n=63$)
(h).

###  4.3. Hierarchical Structure of the Category Prevalence

Based on their geographic location, we can locate countries with homogeneous
category preference vectors and explore clusters of countries with such event
category predominance. We identified 63 countries with at least ten
categories, assigned normalized category ranks to each country, and performed
hierarchical clustering using Euclidean distance and Ward’s clustering
(Murtagh and Legendre, 2014) (see Methods). Three distinct preference clusters
of countries were discovered in this investigation. These are illustrated in
Figure 6, and are generally consistent in terms of cultural proximity,
according to the Inglehart-Welzel World Value Survey 2005-2020 (Inglehart and
Welzel, 2010a; Inglehart, 2018; Inglehart and Welzel, 2010b).

Figure 6. Hierarchical cluster of countries based on category prevalence rank.
It is worth noting that the rank is determined using $z$-scores. 63 countries
were chosen, each with at least ten categories. Three country-level clusters
are represented by the three colors of dendrogram branches. The Inglehart-
Welzel World Value Survey 2005-2020 was used to label countries. Significant
contrasts can be seen in the differences between clusters. Traditional
cultures, for example, dominate Cluster 1. Cluster 3, on the other hand,
contains a significant proportion of secular-rational cultures.

The first cluster includes African-Islamic, Catholic Europe, Latin America,
and other countries not represented on the World Cultural Map. Countries in
the cluster also stress Traditional and Survival Values, Traditional and Self
Expression Values, and Secular-Rational and Survival Values. The second group
comprises Catholic Europe and several Orthodox European countries as well as
African Islamic countries and Latin American states. This cluster also
includes Traditional and Survival Values and Secular-Rational and Self-
Expression Values. English-speaking Protestant Europe and Confucianist
cultural groupings such as Macao, Japan, China, and Hong Kong make up the
third cluster. Secular-Rational and Self-Expression Values, as well as
Secular-Rational and Survival cultural groups, are all included.

Figure 7. Mean rank $z$-scores of the three main clusters as cluster 1,
cluster 2, and cluster 3. The mean rank $z$-scores of three clusters is shown
as a radar map, revealing an exciting pattern of variations across the
clusters throughout the 28 event categories. When compared to countries in
other clusters, countries in cluster 3 have a strong preference for leisure-
type event categories (such as music, sports and recreation, fitness, and
socializing). Figure 8. Boxplots (center, median; plus, mean; box,
interquartile range (IQR); whiskers, 1.5$\times$IQR; outliers, dots) showing
Hofstede’s six cultural value dimensions of three major clusters. An omnibus
test was calculated using a one-way ANOVA test to compare multiple groups,
while pairwise comparisons were calculated using the Wilcoxon test. There are
significant differences between clusters. Cluster 1 is mostly made up of
civilizations that seek to avoid uncertainty, whereas cluster 3 is made up of
countries with high levels of individuality.

On the other hand, as indicated by cluster 1, traditional cultures have a high
interest in movement and politics because they respect national pride and a
patriotic viewpoint. In other words, countries in cluster 1 appear to use
Meetup.com more frequently to engage in national politics or transformations
dialogue. Cluster 2, on the other hand, demonstrates a considerably larger
predilection for writing and technology than the other two clusters, despite
the lack of cultural proximity. Finally, all clusters appear to have the same
modest inclination towards language and identity, new age and spirituality,
and singles.

##  5\. Discussion

A study on cultural analytics suggests that data science methods can be used
to quantitatively evaluate the patterns, trends, and dynamics of people’s
intellectual preferences (Michel et al., 2011; Kim et al., 2014; Manovich,
2020). Meetup is a “digital culture” as its platform connects physical
activities. The main contribution of this study is an in-depth quantitative
investigation of Meetup.com’s event dynamics patterns and how they connect to
cultural attributes. By examining a vast data set from Meetup.com that
captures real, cultural events organized by people worldwide, we offer an
organic way to identify world cultural features and differences. Our method
captures differences in cultural event dynamics and generates a set of
features that can be used to quantify cultural regularities statistically
across countries throughout the world. Furthermore, it enables us to detect
cultural closeness, reflecting groups of countries with a shared interest in
specific issues. We tested whether event dynamics patterns help explain
socioeconomic indicators across 90 societies on all continents. The
implications of our findings are discussed as follows.

First of all, the current study found a negative association between the event
consistency and the country’s well-being. In other words, the more
foundational education, information, and understanding a country’s population
has and the conditions for living a healthy life, the more probable the
country is to have entirely stable event usage. The result confirmed previous
findings that the usage of social networking sites might boost well-being by
increasing social capital, which in turn enhances social support (Van Ingen et
al., 2016; Trepte et al., 2015; Trepte and Scharkow, 2016; Utz and Breuer,
2017). While most existing studies focused on pure online social networking
sites, this study added a research implication that social support could also
happen in an offline setting through face-to-face meeting events. In a similar
vein, the opportunity domain of the SPI is negatively associated with its
bursty dynamics. Creating a consistent number of Meetup events in a given
country is intimately linked to a citizen’s right to freedom of speech. This
correlation is explained by a society that supports an individual’s self-
esteem and encourages more self-expression. Furthermore, our correlation
showed that a rich country tends to have diverse event categories. The number
of events in wealthy countries is not concentrated in a few categories, owing
to the enormous number of people who engage in various cultural activities.
While Meetup events are considered to represent real-world cultural
activities, one could argue that cultural and social involvement has always
been a means of encouraging rapid economic development in a given country
(Ivaldi and Santagata, 2017).

This current research revealed that individualistic cultures also have a more
consistent pattern regarding the number of events ($\beta<0$). This
correlation aligns with the existing literature in which individualistic
cultures value self-expression (e.g., self-esteem) and independence more than
collectivist societies. The Meetup.com platform is based on events organized
by individuals who want to share their ideas and communicate with others about
similar issues. Even though Meetup comprises social structures and not
stranger-to-stranger contacts, motivation is required to create events that
will entice people to participate in such activities. In contrast to
collectivist societies, the more independent a society is, the more encouraged
people are to share their opinions and fearlessly express them. In the
countries with a high value of uncertainty avoidance score, we show that such
countries have low $D_{Cat}$, indicating the choice of attending the event was
limited to a few categories. Because Meetup.com allows for social presence,
there is a lesser sense of uncertainty, which makes meeting new people less
intimidating. However, as evidenced by the low $D_{Cat}$, we can deduce that
uncertainty dampens the impact of shifting to new event categories. For
societies with high-level power distance, there is a tendency that the social
media platform is difficult to adopt since the number of events has appeared
at random ($\beta>1$) (Sander, 2005).

Furthermore, our correlation shows that the number of events increases in
long-term-oriented societies. We might argue that Meetup.com has been
preferred to maintain a long-term connection. Building trust and deep bonds in
high short-term oriented societies, on the other hand, is difficult and time
consuming (Marcus and Krishnamurthi, 2009). Meetup.com appears to be an
excellent platform for attracting devoted attendees, allowing event organizers
to identify and better predict turnout. This study also shows a positive
impact of the social media platform on indulging societies. Indulgent
societies emphasize enjoying life and having fun, mainly leisure, spending,
and consumption. Thus, we can argue that the average number of events per
transaction and category variations on Meetup.com are linked to social
leisure, offering the members the opportunity to fulfill social needs. This
result is in line with previous work mentioning that Meetup.com is a platform
that meets Maslow’s Hierarchy of Needs for belonging, esteem, and self-
actualization (Edgerly, 2010).

Next, we discuss the correlations between dynamic pattern features and
cultural traits based on the Ingelhart-Welzel World Value Survey. We observe
that survival vs. self-expression cultural values possess significant
correlations with all pattern dynamics measures, including overall behavior
pattern, temporal behavior pattern, and event category-related behavior
pattern (see Appendix). In other words, it can be implied that the higher the
self-expression values, the larger the production of Meetup.com events.
Furthermore, the events were evenly distributed in all available categories,
implying that the events were diverse. The low level of trust in a survival-
oriented culture may explain why Meetup.com is not widely accepted. An
adequate level of trust is required to enable the formation of the social
structure (Sander, 2005). In a society that lacks trust, it is hard to form a
corporate structure (Govier, 1997). Consequently, it may be challenging to
build a social structure in a survival-oriented society with low-level trust.

Our findings show that the associations between traditional vs. secular-
rational authority values and all dynamic pattern features are substantial,
except for bursty dynamics and the category variability index (see Appendix).
The significant association between these cultural values and the overall
event count, transaction count, or the average number of events per
transaction on Meetup.com shows that events are actively generated in more
secular cultures. If Meetup.com represents cultural beliefs, it is reasonable
to assume that it is rarely used for religious or traditional family purposes.
Secular cultures regard religion and traditional family values less. Another
important conclusion is that, despite the large number of categories
($N_{Cat}$), events in secular-oriented societies were not evenly distributed
throughout the various categories (i.e., $D_{Cat}$ is unimportant).

The clustering pattern suggests that a group of countries’ cultural proximity
may enable them to acquire common interests in event categories. The
differences between the clusters show an interesting pattern of variation. As
demonstrated in Figure 7. For example, compared to Cluster 1, countries in
Cluster 3 have a substantially lower preference for religion and beliefs,
movement and politics, and religion and beliefs. Cluster 3 is a secular-
rational-oriented culture in which religion, traditional family values, and
authority are less important. Cluster 3 countries, on the other hand, are more
concerned with personal values such as leisure and the development of
interpersonal ties. Music, socializing, sports & entertainment, and fitness
are becoming increasingly popular.

Our clustering pattern also reveals cultural proximity as measured by
Hofstede’s cultural dimension values. Cluster 1 is mainly made up of countries
that mark low in uncertainty and ambiguity values, while Cluster 3 is mostly
made up of countries that value individuality, as seen in Figure 8. Compared
to the other two clusters, Cluster 2 has no specific cultural traits.
Uncertainty avoidance cultures tend to construct specific beliefs and norms to
prevent uncertain situations, and they do not allow unorthodox behaviors
(Smith, 1992). (Mari et al., 2021) suggested that the uncertainty avoidance
index, in particular, impacted institutional trust. As a result, the
prominence of the movement and politics categories in Cluster 1 can be
interpreted to be significantly associated with a high level of uncertainty
avoidance. Finally, countries with individualistic cultures (e.g., Cluster 3)
that value out-group connections above in-group interactions strongly prefer
socializing and other leisure-related categories. It could be because people
in an individualistic society have a lot of ability to pick and choose their
organizations, and there are a lot of them.

##  6\. Conclusion

We investigated the landscape of cultural events held on Meetup.com, an event-
based social network that allows organizers to host offline events. Using 17
years of event data, we developed multiple statistical analysis approaches to
capture the dynamic event patterns such as the overall behavior pattern,
temporal pattern, and category-related pattern. We demonstrated that many
event dynamics and category occurrences are strongly correlated with the
economic and cultural characteristics of the countries. Furthermore, a
clustering technique to uncover the category prevalence was built. The result
offered a better understanding of cultural differences, particularly how
people from traditional and secular cultures chose their preferences within
Meetup’s categories.

Subsequently, we also raise several issues and limitations associated with our
study. Even though Meetup.com is extensively used worldwide, the event
distribution is still heavily skewed towards the United States and specific
event categories such as movement and politics in Italy. Furthermore, because
social diffusion dynamics drive Meetup’s growth directly or indirectly, our
data set may indicate greater aggregation and imbalanced distortions.
Furthermore, we assume that event categories are disjoint sets, even though
they are likely to overlap in the actual world because event organizers may
choose them at random when planning a new event. Furthermore, we assume that
the total of individual or group cultural values equals country-level values.
However, cultural values are unlikely to be universally agreed upon by all the
people of a country (Leung and Morris, 2015; Peterson, 2016). Finally, we may
identify how these values are shared across countries by obtaining individual-
level data on cultural value orientations and category preferences, allowing
for a more nuanced assessment of the extent to which a country implies culture
(Gelfand et al., 2011). Despite these constraints, we believe that this
research can provide a new viewpoint on how event dynamics patterns in social
network platform reflect country-level cultural norms. Our method highlights
the relationship between culture, nation, and group behaviors, as illustrated
on Meetup.com, allowing us to learn more about how cultural events are
created, organized, valued, and experienced.

## References

  * (1)
  * Boli and Lechner (2015) John Boli and Frank J. Lechner. 2015.  Globalization and World Culture.  In _International Encyclopedia of the Social & Behavioral Sciences (Second Edition)_ (second edition ed.), James D. Wright (Ed.). Elsevier, Oxford, 225–232.
  * Can and Alatas (2019) Umit Can and Bilal Alatas. 2019.  A new direction in social network analysis: Online social network analysis problems and applications.  _Physica A: Statistical Mechanics and its Applications_ 535 (2019), 122372\.
  * Choudhary et al. (2019) Sonal Choudhary, Rakesh Nayak, Sushma Kumari, and Homagni Choudhury. 2019.  Analysing acculturation to sustainable food consumption behaviour in the social media through the lens of information diffusion.  _Technological Forecasting and Social Change_ 145 (2019), 481–492.
  * Edgerly (2010) Peia Marie Edgerly. 2010\.  _Using the internet as an access to leisure: a study of Meetup_.  Thesis. California State University at Sacramento.
  * Fenich (2013) George G Fenich. 2013\.  _Meetings, Expositions, Events & Conventions: Pearson New International Edition: An Introduction to the Industry_.  Pearson Higher Ed.
  * Frigo (2017) Giovanni Frigo. 2017\.  Energy ethics, homogenization, and hegemony: A reflection on the traditional energy paradigm.  _Energy Research & Social Science_ 30 (2017), 7–17.
  * Fund (2019) International Monetary Fund. 2019\.  _World Economic Outlook Database_.  https://www.imf.org/en/Publications/WEO/weo-database/2019/April
  * Gelfand et al. (2011) Michele J Gelfand, Jana L Raver, Lisa Nishii, Lisa M Leslie, Janetta Lun, Beng Chong Lim, Lili Duan, Assaf Almaliach, Soon Ang, Jakobina Arnadottir, et al. 2011\.  Differences between tight and loose cultures: A 33-nation study.  _Science_ 332, 6033 (2011), 1100–1104.
  * Getz (2008) Donald Getz. 2008\.  Event tourism: Definition, evolution, and research.  _Tourism management_ 29, 3 (2008), 403–428.
  * Getz and Page (2019) Donald Getz and Stephen J Page. 2019.  _Event studies: Theory, research and policy for planned events_.  Routledge.
  * Govier (1997) Trudy Govier. 1997\.  _Social trust and human communities_.  McGill-Queen’s Press-MQUP.
  * Hamidi et al. (2011) Yasin Hamidi, Yasaman Hamidi, and Shahrbanou Mehrbabak. 2011\.  Localization versus globalization of social networks.  _Procedia Computer Science_ 3 (2011), 191–196.
  * Hofstede et al. (2005) Geert Hofstede, Gert Jan Hofstede, and Michael Minkov. 2005\.  _Cultures and organizations: Software of the mind_. Vol. 2.  Mcgraw-hill New York.
  * Hofstede et al. (2010) Geert Hofstede, Gert Jan Hofstede, and Michael Minkov. 2010\.  _Cultures and organizations: Software of the mind_. Vol. 2.  Mcgraw-hill New York.
  * Hong and Na (2018) Seungbeom Hong and Jinkyung Na. 2018.  How Facebook is perceived and used by people across cultures: The implications of cultural differences in the use of Facebook.  _Social Psychological and Personality Science_ 9, 4 (2018), 435–443.
  * Imperative (2020) Social Progress Imperative. 2020\.  _Global Index 2020_.  https://www.socialprogress.org/index/global
  * Inglehart (2018) Ronald Inglehart. 2018\.  _Cultural evolution: People’s motivations are changing, and reshaping the world_.  Cambridge University Press.
  * Inglehart and Welzel (2010a) Ronald Inglehart and Christian Welzel. 2010a.  Changing mass priorities: The link between modernization and democracy.  _Perspectives on politics_ 8, 2 (2010), 551–567.
  * Inglehart and Welzel (2010b) Ronald Inglehart and Chris Welzel. 2010b.  The WVS cultural map of the world.  _World Values Survey_ (2010).
  * Ivaldi and Santagata (2017) Enrico Ivaldi and Marta Santagata. 2017.  Cultural Well-Being and GDP Per Capita in Italy: Empirical Evidence.  _Quality of Life and Quality of Working Life_ (2017), 151.
  * Khan (2017) M Laeeq Khan. 2017\.  Social media engagement: What motivates user participation and consumption on YouTube?  _Computers in Human Behavior_ 66 (2017), 236–247.
  * Kim et al. (2014) Daniel Kim, Seung-Woo Son, and Hawoong Jeong. 2014\.  Large-scale quantitative analysis of painting arts.  _Scientific Reports_ 4, 1 (2014), 1–7.
  * Koiranen et al. (2020) Ilkka Koiranen, Aki Koivula, Arttu Saarinen, and Teo Keipi. 2020.  Ideological motives, digital divides, and political polarization: How do political party preference and values correspond with the political use of social media?  _Telematics and Informatics_ 46 (2020), 101322.
  * Lee et al. (2018) Changjun Lee, Jieun Shin, and Ahreum Hong. 2018.  Does social media use really make people politically polarized? Direct and indirect effects of social media use on political polarization in South Korea.  _Telematics and Informatics_ 35, 1 (2018), 245–254.
  * Leung and Morris (2015) Kwok Leung and Michael W Morris. 2015.  Values, schemas, and norms in the culture–behavior nexus: A situated dynamics framework.  _Journal of International Business Studies_ 46, 9 (2015), 1028–1050.
  * Lin and Ho (2018) Hsien-Cheng Lin and Wen-Hsien Ho. 2018.  Cultural effects on use of online social media for health-related information acquisition and sharing in Taiwan.  _International Journal of Human–Computer Interaction_ 34, 11 (2018), 1063–1076.
  * Liu et al. (2012) Xingjie Liu, Qi He, Yuanyuan Tian, Wang-Chien Lee, John McPherson, and Jiawei Han. 2012\.  Event-based social networks: linking the online and offline social worlds. In _proc. of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining_. 1032–1040.
  * Manovich (2020) Lev Manovich. 2020\.  _Cultural Analytics_.  MIT Press.
  * Marcus and Krishnamurthi (2009) Aaron Marcus and Niranjan Krishnamurthi. 2009.  Cross-cultural analysis of social network services in Japan, Korea, and the USA. In _International Conference on Internationalization, design and global development_. Springer, 59–68.
  * Mari et al. (2021) Silvia Mari, Homero Gil de Zúñiga, Ahmet Suerdem, Katja Hanke, Gary Brown, Roosevelt Vilar, Diana Boer, and Michal Bilewicz. 2021\.  Conspiracy Theories and Institutional Trust: Examining the Role of Uncertainty Avoidance and Active Social Media Use.  _Political Psychology_ (2021).
  * Michel et al. (2011) Jean-Baptiste Michel, Yuan Kui Shen, Aviva Presser Aiden, Adrian Veres, Matthew K Gray, Google Books Team, Joseph P Pickett, Dale Hoiberg, Dan Clancy, Peter Norvig, et al. 2011\.  Quantitative analysis of culture using millions of digitized books.  _Science_ 331, 6014 (2011), 176–182.
  * Murtagh and Legendre (2014) Fionn Murtagh and Pierre Legendre. 2014.  Ward’s hierarchical agglomerative clustering method: which algorithms implement Ward’s criterion?  _Journal of classification_ 31, 3 (2014), 274–295.
  * Pakrashi et al. (2018) Arjun Pakrashi, Elham Alghamdi, Brian Mac Namee, and Derek Greene. 2018.  MeetupNet Dublin: Discovering Communities in Dublin’s Meetup Network.  _arXiv preprint arXiv:1810.03046_ (2018).
  * Peterson (2016) Mark F Peterson. 2016\.  A culture theory commentary on Meyer and Peng’s theoretical probe into Central and Eastern Europe.  _Journal of International Business Studies_ 47, 1 (2016), 33–43.
  * Rogers et al. (2014) Everett M Rogers, Arvind Singhal, and Margaret M Quinlan. 2014\.  Diffusion of innovations.  In _An integrated approach to communication theory and research_. Routledge, 432–448.
  * Rojek et al. (1997) Chris Rojek, John Urry, et al. 1997\.  _Touring cultures: Transformations of travel and theory_.  Psychology Press.
  * Sánchez-Franco et al. (2009) Manuel J Sánchez-Franco, Francisco J Martínez-López, and Félix A Martín-Velicia. 2009\.  Exploring the impact of individualism and uncertainty avoidance in Web-based electronic learning: An empirical analysis in European higher education.  _Computers & Education_ 52, 3 (2009), 588–598.
  * Sander (2005) Thomas Sander. 2005\.  E-associations: using technology to connect citizens: the case of Meetup.com. American Political Science Association.
  * Sharma et al. (2019) Abhishek Sharma, Gede Artha Azriadi Prana, Anamika Sawhney, Nachiappan Nagappan, and David Lo. 2019\.  Analyzing Offline Social Engagements: An Empirical Study of Meetup Events Related to Software Development.  _arXiv preprint arXiv:1912.07352_ (2019).
  * Sheldon et al. (2017) Pavica Sheldon, Philipp A Rauschnabel, Mary Grace Antony, and Sandra Car. 2017.  A cross-cultural comparison of Croatian and American social network sites: Exploring cultural differences in motives for Instagram use.  _Computers in Human Behavior_ 75 (2017), 643–651.
  * Shen et al. (2020) Fei Shen, Chuanli Xia, and Marko Skoric. 2020.  Examining the roles of social media and alternative media in social movement participation: A study of Hong Kong’s Umbrella Movement.  _Telematics and Informatics_ 47 (2020), 101303.
  * Smith (1992) Peter B Smith. 1992\.  Organizational behaviour and national cultures.  _British Journal of Management_ 3, 1 (1992), 39–51.
  * Trepte et al. (2015) Sabine Trepte, Tobias Dienlin, and Leonard Reinecke. 2015\.  Influence of social support received in online and offline contexts on satisfaction with social support and satisfaction with life: A longitudinal study.  _Media Psychology_ 18, 1 (2015), 74–105.
  * Trepte and Scharkow (2016) Sabine Trepte and Michael Scharkow. 2016.  How social capital and social support received in media environments contribute to well-being.  _The Routledge handbook of media use and well-being: International perspectives on theory and research on positive media effects_ (2016), 304–316.
  * Union (2020) International Telecomunication Union. 2020\.  _Measuring Digital Development: Facts and figures 2020_.  https://tinyurl.com/5ax2m9e9
  * Utz and Breuer (2017) Sonja Utz and Johannes Breuer. 2017.  The relationship between use of social network sites, online social support, and well-being.  _Journal of media psychology_ (2017).
  * Van Ingen et al. (2016) Erik Van Ingen, Sonja Utz, and Vera Toepoel. 2016.  Online coping after negative life events: Measurement, prevalence, and relation with internet activities and well-being.  _Social Science Computer Review_ 34, 5 (2016), 511–529.
  * Yu and Oh (2018) Rebecca Ping Yu and Yu Won Oh. 2018.  Social media and expressive citizenship: Understanding the relationships between social and entertainment expression on Facebook and political participation.  _Telematics and informatics_ 35, 8 (2018), 2299–2311.
  * Zheng and Lee (2016) Xiabing Zheng and Matthew KO Lee. 2016.  Excessive use of mobile social networking sites: Negative consequences on individuals.  _Computers in Human Behavior_ 65 (2016), 65–76.

##  Appendix A Data Description

Table 1. The description of the Meetup dataset in the chosen 90 countries,
shown in descending order by total event count (TEC).

Country | Code | Lat | Lon | #City | TEC | NCat | NTrans | $\mu_{Population}$ | GDP | HDI | MSubs | IntUs
---|---|---|---|---|---|---|---|---|---|---|---|---
United States | US | 37.090 | -95.713 | 7,117 | 1,771,002 | 33 | 5,303 | 309,445 | 65,300 | 0.926 | 92.23 | 71.04
United Kingdom | GB | 55.378 | -3.436 | 3,090 | 211,273 | 33 | 4,862 | 63,463 | 42,300 | 0.932 | 116.31 | 80.88
Canada | CA | 56.130 | -106.347 | 519 | 110,559 | 33 | 4,951 | 34,300 | 46,200 | 0.929 | 69.64 | 78.80
Italy | IT | 41.872 | 12.567 | 1,296 | 55,064 | 30 | 4,698 | 59,439 | 33,200 | 0.892 | 142.90 | 48.55
Australia | AU | -25.274 | 133.775 | 1,277 | 54,466 | 33 | 4,169 | 22,271 | 55,100 | 0.944 | 100.63 | 77.36
India | IN | 20.594 | 78.963 | 279 | 42,788 | 29 | 3,316 | 1,238,039 | 2,100 | 0.645 | 47.62 | 10.29
France | FR | 46.228 | 2.214 | 633 | 34,609 | 30 | 2,958 | 62,932 | 40,500 | 0.901 | 91.88 | 67.56
Germany | DE | 51.166 | 10.452 | 481 | 30,713 | 31 | 2,932 | 81,539 | 46,400 | 0.947 | 113.39 | 78.08
Netherlands | NL | 52.133 | 5.291 | 330 | 30,646 | 32 | 3,304 | 16,684 | 52,300 | 0.944 | 112.36 | 86.43
Japan | JP | 36.205 | 138.253 | 113 | 18,116 | 29 | 1,836 | 128,188 | 40,200 | 0.919 | 99.26 | 77.56
Switzerland | CH | 46.818 | 8.228 | 408 | 17,995 | 31 | 3,306 | 7,876 | 82,000 | 0.955 | 117.51 | 80.98
Ireland | IE | 53.413 | -8.244 | 139 | 17,283 | 28 | 3,339 | 4,473 | 78,700 | 0.955 | 103.48 | 66.54
Singapore | SG | 1.352 | 103.820 | 1 | 16,229 | 20 | 3,139 | 5,047 | 65,200 | 0.938 | 131.72 | 71.04
United Arab Emirates | AE | 23.424 | 53.848 | 13 | 14,486 | 25 | 2,748 | 7,602 | 43,100 | 0.89 | 145.61 | 68.33
Hong Kong | HK | 22.396 | 114.109 | 9 | 14,188 | 29 | 2,340 | 7,003 | 48,700 | 0.949 | 188.81 | 70.68
Spain | ES | 40.464 | -3.749 | 181 | 13,798 | 28 | 2,382 | 45,885 | 29,600 | 0.904 | 105.40 | 63.58
Belgium | BE | 50.504 | 4.470 | 214 | 13,753 | 28 | 2,593 | 10,960 | 46,400 | 0.931 | 103.70 | 72.24
China | CN | 35.862 | 104.195 | 60 | 11,799 | 29 | 2,117 | 1,372,420 | 10,300 | 0.761 | 63.09 | 31.10
South Korea | KR | 35.908 | 127.767 | 45 | 11,290 | 24 | 2,660 | 49,770 | 31,800 | 0.916 | 99.08 | 82.19
Brazil | BR | -14.235 | -51.925 | 128 | 11,071 | 29 | 1,708 | 196,276 | 8,720 | 0.765 | 90.48 | 38.94
Sweden | SE | 60.128 | 18.644 | 111 | 10,670 | 27 | 2,673 | 9,436 | 51,600 | 0.945 | 115.33 | 89.24
Thailand | TH | 15.870 | 100.993 | 59 | 8,869 | 30 | 2,470 | 67,238 | 7,810 | 0.777 | 105.34 | 25.77
Israel | IL | 31.046 | 34.852 | 98 | 8,588 | 25 | 2,323 | 7,356 | 43,600 | 0.919 | 121.93 | 57.14
Mexico | MX | 23.635 | -102.553 | 96 | 8,051 | 31 | 1,720 | 114,751 | 9,950 | 0.779 | 68.66 | 33.95
Malaysia | MY | 4.210 | 101.976 | 71 | 7,246 | 27 | 2,083 | 28,270 | 11,400 | 0.81 | 110.21 | 58.58
Denmark | DK | 56.264 | 9.502 | 81 | 7,030 | 27 | 2,711 | 5,568 | 60,200 | 0.94 | 116.24 | 89.02
Taiwan | TW | 23.698 | 120.961 | 17 | 6,963 | 26 | 1,841 | 23,179 | 32,100 | 0.907 | NA | NA
Colombia | CO | 4.571 | -74.297 | 43 | 6,951 | 27 | 1,762 | 45,428 | 6,430 | 0.767 | 87.21 | 35.11
Austria | AT | 47.516 | 14.550 | 80 | 6,056 | 23 | 1,732 | 8,477 | 50,100 | 0.922 | 136.16 | 72.40
Romania | RO | 45.943 | 24.967 | 31 | 5,072 | 21 | 1,930 | 20,534 | 12,900 | 0.828 | 98.46 | 38.40
Hungary | HU | 47.162 | 19.503 | 39 | 4,834 | 21 | 1,687 | 9,917 | 16,700 | 0.854 | 111.04 | 59.50
Philippines | PH | 12.880 | 121.774 | 81 | 4,712 | 26 | 1,597 | 94,902 | 3,490 | 0.718 | 81.82 | 24.25
Russia | RU | 61.524 | 105.319 | 20 | 4,611 | 21 | 1,498 | 144,206 | 11,600 | 0.824 | 127.92 | 43.23
Indonesia | ID | -0.789 | 113.921 | 73 | 4,320 | 26 | 1,442 | 243,766 | 4,140 | 0.718 | 83.01 | 12.23
Argentina | AR | -38.416 | -63.617 | 46 | 3,930 | 25 | 1,555 | 41,169 | 9,910 | 0.845 | 113.46 | 40.72
Turkey | TR | 38.964 | 35.243 | 40 | 3,761 | 19 | 1,299 | 73,529 | 9,130 | 0.82 | 82.70 | 37.47
Poland | PL | 51.919 | 19.145 | 26 | 3,612 | 19 | 1,441 | 38,226 | 15,700 | 0.88 | 114.69 | 55.65
Chile | CL | -35.675 | -71.543 | 32 | 3,250 | 19 | 1,453 | 17,183 | 14,900 | 0.851 | 104.02 | 49.87
Vietnam | VN | 14.058 | 108.277 | 25 | 2,574 | 22 | 1,292 | 88,634 | 2,720 | 0.704 | 91.46 | 27.48
Qatar | QA | 25.355 | 51.184 | 10 | 2,088 | 22 | 964 | 1,828 | 62,100 | 0.848 | 114.43 | 57.09
Greece | GR | 39.074 | 21.824 | 53 | 1,993 | 21 | 1,004 | 10,893 | 19,600 | 0.888 | 105.62 | 44.43
Portugal | PT | 39.400 | -8.224 | 43 | 1,762 | 22 | 905 | 10,472 | 23,300 | 0.864 | 113.17 | 51.82
Croatia | HR | 45.100 | 15.200 | 22 | 1,663 | 18 | 995 | 4,302 | 14,900 | 0.851 | 100.63 | 52.15
Bulgaria | BG | 42.734 | 25.486 | 11 | 1,475 | 18 | 802 | 7,414 | 9,830 | 0.816 | 118.87 | 42.01
Nigeria | NG | 9.082 | 8.675 | 27 | 1,219 | 13 | 751 | 161,882 | 2,230 | 0.539 | 49.25 | 11.91
Saudi Arabia | SA | 23.886 | 45.079 | 14 | 1,063 | 21 | 655 | 28,024 | 23,100 | 0.854 | 131.90 | 43.11
Egypt | EG | 26.821 | 30.802 | 32 | 1,030 | 15 | 635 | 84,544 | 3,020 | 0.707 | 68.59 | 23.82
Slovenia | SI | 46.151 | 14.995 | 23 | 1,022 | 11 | 676 | 2,039 | 25,900 | 0.917 | 103.09 | 61.98
Lebanon | LB | 33.855 | 35.862 | 27 | 970 | 15 | 661 | 5,472 | 7,580 | 0.744 | 53.57 | 40.29
Costa Rica | CR | 9.749 | -83.753 | 37 | 950 | 16 | 550 | 4,596 | 12,200 | 0.81 | 86.85 | 40.19
Sri Lanka | LK | 7.873 | 80.772 | 17 | 938 | 10 | 672 | 20,296 | 3,850 | 0.782 | 72.80 | 12.91
Slovakia | SK | 48.669 | 19.699 | 7 | 593 | 15 | 450 | 5,416 | 19,300 | 0.86 | 105.66 | 68.63
Norway | NO | 60.472 | 8.469 | 12 | 592 | 15 | 455 | 4,934 | 75,400 | 0.957 | 107.82 | 90.23
New Zealand | NZ | -40.901 | 174.886 | 15 | 580 | 15 | 367 | 4,395 | 42,100 | 0.931 | 103.88 | 76.02
Czech Republic | CZ | 49.817 | 15.473 | 13 | 553 | 10 | 401 | 10,481 | 23,500 | 0.9 | 120.66 | 61.62
Jordan | JO | 30.585 | 36.238 | 3 | 414 | 9 | 330 | 7,594 | 4,410 | 0.729 | 88.25 | 30.36
Continued on next page

Continued from previous page
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Country | Code | Lat | Lon | #City | TEC | NCat | NTrans | $\mu_{Population}$ | GDP | HDI | MSubs | IntUs
Bahrain | BH | 25.930 | 50.638 | 14 | 391 | 12 | 238 | 1,195 | 23,500 | 0.852 | 132.54 | 61.22
Morocco | MA | 31.792 | -7.093 | 15 | 378 | 14 | 286 | 32,717 | 3,200 | 0.686 | 87.25 | 39.28
Pakistan | PK | 30.375 | 69.345 | 11 | 364 | 9 | 273 | 181,804 | 1,280 | 0.557 | 48.46 | 8.91
Bangladesh | BD | 23.685 | 90.356 | 5 | 361 | 5 | 309 | 148,441 | 1,860 | 0.632 | 45.22 | 5.38
Tunisia | TN | 33.887 | 9.537 | 12 | 352 | 5 | 279 | 10,703 | 3,320 | 0.74 | 93.12 | 31.68
Nepal | NP | 28.395 | 84.124 | 19 | 243 | 11 | 209 | 26,695 | 1,070 | 0.602 | 45.73 | 7.29
Cyprus | CY | 35.126 | 33.430 | 5 | 199 | 10 | 157 | 1,106 | 27,900 | 0.887 | 111.04 | 53.28
Ivory Coast | CI | 7.546 | -5.548 | 3 | 191 | 4 | 173 | 20,995 | 2,280 | 0.538 | 66.45 | 11.68
Oman | OM | 21.513 | 55.923 | 16 | 174 | 11 | 142 | 3,377 | 15,300 | 0.813 | 116.86 | 37.38
Sudan | SD | 12.863 | 30.218 | 1 | 170 | 2 | 141 | 35,250 | 442 | 0.51 | 46.40 | 15.31
Georgia | GE | 42.315 | 43.357 | 2 | 163 | 6 | 127 | 4,105 | 4,700 | 0.812 | 85.91 | 27.22
Mauritius | MU | -20.348 | 57.552 | 11 | 157 | 5 | 116 | 1,244 | 11,100 | 0.806 | 96.40 | 29.16
Liechtenstein | LI | 47.166 | 9.555 | 4 | 112 | 5 | 68 | 36 | 181,000 | 0.919 | 96.98 | 78.47
Ecuador | EC | -1.831 | -78.183 | 7 | 111 | 9 | 97 | 15,151 | 6,180 | 0.759 | 77.15 | 27.89
Macao | MO | 22.211 | 113.553 | 1 | 107 | 12 | 88 | 547 | 84,100 | 0.909 | 210.04 | 56.25
Nicaragua | NI | 12.865 | -85.207 | 3 | 84 | 5 | 74 | 5,869 | 1,910 | 0.66 | 73.18 | 9.82
Iceland | IS | 64.963 | -19.021 | 4 | 83 | 8 | 56 | 317 | 66,900 | 0.949 | 107.90 | 92.41
Peru | PE | -9.190 | -75.015 | 8 | 75 | 9 | 47 | 29,322 | 6,980 | 0.777 | 76.26 | 31.61
Algeria | DZ | 28.034 | 1.660 | 11 | 74 | 1 | 60 | 36,685 | 3,970 | 0.748 | 81.26 | 16.40
Monaco | MC | 43.750 | 7.413 | 1 | 66 | 10 | 65 | 36 | 186,000 | NA | 70.50 | 73.90
Kenya | KE | -0.024 | 37.906 | 5 | 65 | 11 | 55 | 42,769 | 1,820 | 0.601 | 50.10 | 8.40
Jamaica | JM | 18.110 | -77.298 | 9 | 64 | 7 | 50 | 2,821 | 5,580 | 0.734 | 95.12 | 27.07
South Africa | ZA | -30.559 | 22.938 | 7 | 64 | 9 | 50 | 51,902 | 6,000 | 0.709 | 106.63 | 25.53
Cuba | CU | 21.522 | -77.781 | 7 | 51 | 8 | 44 | 11,273 | 8,820 | 0.783 | 12.84 | 18.85
Angola | AO | -11.203 | 17.874 | 1 | 43 | 3 | 24 | 24,036 | 2,790 | 0.581 | 33.59 | 4.73
Kazakhstan | KZ | 48.020 | 66.924 | 1 | 41 | 5 | 42 | 16,534 | 9,810 | 0.825 | 107.30 | 35.96
Finland | FI | 61.924 | 25.748 | 8 | 33 | 7 | 26 | 5,378 | 48,800 | 0.938 | 130.13 | 83.19
Cambodia | KH | 12.566 | 104.991 | 2 | 28 | 2 | 15 | 14,488 | 1,640 | 0.594 | 70.11 | 8.11
Dominican Republic | DO | 18.736 | -70.163 | 4 | 27 | 6 | 18 | 9,749 | 8,280 | 0.756 | 69.47 | 31.04
Kyrgyzstan | KG | 41.204 | 74.766 | 1 | 23 | 2 | 23 | 5,543 | 1,310 | 0.697 | 79.66 | 17.66
Tanzania | TZ | -6.369 | 34.889 | 5 | 22 | 5 | 17 | 45,522 | 1,120 | 0.529 | 40.28 | 3.88
Bahamas | BS | 25.034 | -77.396 | 5 | 21 | 7 | 21 | 354 | 34,900 | 0.814 | 83.68 | 48.01
Myanmar | MM | 21.914 | 95.956 | 2 | 20 | 3 | 10 | 50,920 | 1,410 | 0.583 | 23.14 | 5.18
Belize | BZ | 17.190 | -88.498 | 5 | 18 | 6 | 15 | 326 | 4,820 | 0.716 | 47.66 | 29.02

Table 2. Category names available in Meetup. The 33 categories are grouped into 11 mid-level categories and into 7 top-level categories. No | Category | Mid-level category | Top-level category
---|---|---|---
1 | Movies & Film | Cinema and Video Art | Art
2 | Sci-Fi & Fantasy
3 | Dancing | Dancing
4 | Arts & Culture | General Art
5 | Music | Music and Audio Art
6 | Career & Business | Business and Finance | Training
7 | Education & Learning | Education and Science
8 | Tech | Technology
9 | Writing | Languages
10 | Book Clubs | Everyday Culture
11 | Cars & Motorcycles
12 | Community & Environment
13 | Food & Drink
14 | Games
15 | Health & Wellbeing
16 | Hobbies & Crafts
17 | Lifestyle
18 | Outdoors & Adventure
19 | Paranormal
20 | Parents & Family
21 | Pets & Animals
22 | Photography
23 | Socializing
24 | Support
25 | Language & Ethnic Identity | Religion and Traditions
26 | LGBT
27 | New Age & Spirituality
28 | Religion & Beliefs
29 | Singles | Other | Other
30 | Movements & Politics | Politics | Politics
31 | Fashion & Beauty | Sports | Sports
32 | Fitness
33 | Sports & Recreation

##  Appendix B Overall Correlations

In this section, we provide complete correlation results as shown in Figure 9
and 10.

Figure 9. Pearson correlation between event dynamics features and
socioeconomic and technology indicators. We use the following notation to
denote statistical significance: $*p<0.05$, $**p<0.01$, $***p<0.001$. Figure
10. Pearson correlation between event dynamics features and cultural traits
based on Hoefstede’s Cultural Values and Inglegart-Welzel’s World Value
Survey. We use the following notation to denote statistical significance:
$*p<0.05$, $**p<0.01$, $***p<0.001$.


* Hattori  _et al._ (2019a) Koichi Hattori, Yuji Hirono, Ho-Ung Yee, and Yi Yin, “MagnetoHydrodynamics with chiral anomaly: phases of collective excitations and instabilities,” Phys. Rev. D 100, 065023 (2019a), arXiv:1711.08450 [hep-th] .
  * Tashiro  _et al._ (2012) Hiroyuki Tashiro, Tanmay Vachaspati, and Alexander Vilenkin, “Chiral Effects and Cosmic Magnetic Fields,” Phys. Rev. D 86, 105033 (2012), arXiv:1206.5549 [astro-ph.CO] .
  * Boyarsky  _et al._ (2015) Alexey Boyarsky, Jurg Frohlich, and Oleg Ruchayskiy, “Magnetohydrodynamics of Chiral Relativistic Fluids,” Phys. Rev. D 92, 043004 (2015), arXiv:1504.04854 [hep-ph] .
  * Rogachevskii  _et al._ (2017) Igor Rogachevskii, Oleg Ruchayskiy, Alexey Boyarsky, Jürg Fröhlich, Nathan Kleeorin, Axel Brandenburg, and Jennifer Schober, “Laminar and turbulent dynamos in chiral magnetohydrodynamics-I: Theory,” Astrophys. J. 846, 153 (2017), arXiv:1705.00378 [physics.plasm-ph] .
  * Schober  _et al._ (2018) Jennifer Schober, Igor Rogachevskii, Axel Brandenburg, Alexey Boyarsky, Jürg Fröhlich, Oleg Ruchayskiy, and Nathan Kleeorin, “Laminar and turbulent dynamos in chiral magnetohydrodynamics. II. Simulations,” Astrophys. J. 858, 124 (2018), arXiv:1711.09733 [physics.flu-dyn] .
  * Brandenburg  _et al._ (2017) Axel Brandenburg, Jennifer Schober, Igor Rogachevskii, Tina Kahniashvili, Alexey Boyarsky, Jurg Frohlich, Oleg Ruchayskiy, and Nathan Kleeorin, “The turbulent chiral-magnetic cascade in the early universe,” Astrophys. J. Lett. 845, L21 (2017), arXiv:1707.03385 [astro-ph.CO] .
  * Boyarsky  _et al._ (2021) A. Boyarsky, V. Cheianov, O. Ruchayskiy, and O. Sobol, “Evolution of the Primordial Axial Charge across Cosmic Times,” Phys. Rev. Lett. 126, 021801 (2021), arXiv:2007.13691 [hep-ph] .
  * Joyce and Shaposhnikov (1997) M. Joyce and Mikhail E. Shaposhnikov, “Primordial magnetic fields, right-handed electrons, and the Abelian anomaly,” Phys. Rev. Lett. 79, 1193–1196 (1997), arXiv:astro-ph/9703005 .
  * Field and Carroll (2000) George B. Field and Sean M. Carroll, “Cosmological magnetic fields from primordial helicity,” Phys. Rev. D 62, 103008 (2000), arXiv:astro-ph/9811206 .
  * Giovannini (2004) Massimo Giovannini, “The Magnetized universe,” Int. J. Mod. Phys. D 13, 391–502 (2004), arXiv:astro-ph/0312614 .
  * Semikoz and Sokoloff (2005) Victor B. Semikoz and D. D. Sokoloff, “Magnetic helicity and cosmological magnetic field,” Astron. Astrophys. 433, L53 (2005), arXiv:astro-ph/0411496 .
  * Laine (2005) M. Laine, “Real-time Chern-Simons term for hypermagnetic fields,” JHEP 10, 056 (2005), arXiv:hep-ph/0508195 .
  * Boyarsky  _et al._ (2012) Alexey Boyarsky, Jurg Frohlich, and Oleg Ruchayskiy, “Self-consistent evolution of magnetic fields and chiral asymmetry in the early Universe,” Phys. Rev. Lett. 108, 031301 (2012), arXiv:1109.3350 [astro-ph.CO] .
  * Grozdanov  _et al._ (2017) Sašo Grozdanov, Diego M. Hofman, and Nabil Iqbal, “Generalized global symmetries and dissipative magnetohydrodynamics,” Phys. Rev. D 95, 096003 (2017), arXiv:1610.07392 [hep-th] .
  * Glorioso and Son (2018) Paolo Glorioso and Dam Thanh Son, “Effective field theory of magnetohydrodynamics from generalized global symmetries,” (2018), arXiv:1811.04879 [hep-th] .
  * Armas and Jain (2019) Jay Armas and Akash Jain, “Magnetohydrodynamics as superfluidity,” Phys. Rev. Lett. 122, 141603 (2019), arXiv:1808.01939 [hep-th] .
  * Armas and Jain (2020) Jay Armas and Akash Jain, “One-form superfluids & magnetohydrodynamics,” JHEP 01, 041 (2020), arXiv:1811.04913 [hep-th] .
  * Hongo and Hattori (2021) Masaru Hongo and Koichi Hattori, “Revisiting relativistic magnetohydrodynamics from quantum electrodynamics,” JHEP 02, 011 (2021), arXiv:2005.10239 [hep-th] .
  * Grozdanov and Poovuttikul (2018) Sašo Grozdanov and Napat Poovuttikul, “Generalized global symmetries in states with dynamical defects: The case of the transverse sound in field theory and holography,” Phys. Rev. D 97, 106005 (2018), arXiv:1801.03199 [hep-th] .
  * Armas  _et al._ (2018) Jay Armas, Jakob Gath, Akash Jain, and Andreas Vigand Pedersen, “Dissipative hydrodynamics with higher-form symmetry,” JHEP 05, 192 (2018), arXiv:1803.00991 [hep-th] .
  * Gralla and Iqbal (2019) Samuel E. Gralla and Nabil Iqbal, “Effective Field Theory of Force-Free Electrodynamics,” Phys. Rev. D 99, 105004 (2019), arXiv:1811.07438 [hep-th] .
  * Delacrétaz  _et al._ (2020) Luca V. Delacrétaz, Diego M. Hofman, and Grégoire Mathys, “Superfluids as Higher-form Anomalies,” SciPost Phys. 8, 047 (2020), arXiv:1908.06977 [hep-th] .
  * Iqbal and Poovuttikul (2020) Nabil Iqbal and Napat Poovuttikul, “2-group global symmetries, hydrodynamics and holography,” (2020), arXiv:2010.00320 [hep-th] .
  * Landry (2021) Michael J. Landry, “Higher-form and (non-)Stückelberg symmetries in non-equilibrium systems,” (2021), arXiv:2101.02210 [hep-th] .
  * Braginskii (1965) S. I. Braginskii, “Transport processes in a plasma,” Rev. Plasma Phy. 1, 205–311 (1965).
  * Lifschitz and Pitaevskii (1981) E. M. Lifschitz and L. P. Pitaevskii,  _Physical Kinetics_ (Pergamon, 1981).
  * Huang  _et al._ (2010) Xu-Guang Huang, Mei Huang, Dirk H. Rischke, and Armen Sedrakian, “Anisotropic Hydrodynamics, Bulk Viscosities and R-Modes of Strange Quark Stars with Strong Magnetic Fields,” Phys. Rev. D 81, 045015 (2010), arXiv:0910.3633 [astro-ph.HE] .
  * Huang  _et al._ (2011) Xu-Guang Huang, Armen Sedrakian, and Dirk H. Rischke, “Kubo formulae for relativistic fluids in strong magnetic fields,” Annals Phys. 326, 3075–3094 (2011), arXiv:1108.0602 [astro-ph.HE] .
  * Hernandez and Kovtun (2017) Juan Hernandez and Pavel Kovtun, “Relativistic magnetohydrodynamics,” JHEP 05, 001 (2017), arXiv:1703.08757 [hep-th] .
  * De Groot and Mazur (1962) Sybren Ruurds De Groot and Peter Mazur,  _Non-equilibrium thermodynamics_ (North-Holland Publishing Company, 1962).
  * Hongo  _et al._ (2021) Masaru Hongo, Xu-Guang Huang, Matthias Kaminski, Mikhail Stephanov, and Ho-Ung Yee, “Relativistic spin hydrodynamics with torsion and linear response theory for spin relaxation,” JHEP 11, 150 (2021), arXiv:2107.14231 [hep-th] .
  * Hongo  _et al._ (2022) Masaru Hongo, Xu-Guang Huang, Matthias Kaminski, Mikhail Stephanov, and Ho-Ung Yee, “Spin relaxation rate for heavy quarks in weakly coupled QCD plasma,” (2022), arXiv:2201.12390 [hep-th] .
  * Cao  _et al._ (2022) Zheng Cao, Koichi Hattori, Masaru Hongo, Xu-Guang Huang, and Hidetoshi Taya, “Gyrohydrodynamics: Relativistic spinful fluid with strong vorticity,” (2022), arXiv:2205.08051 [hep-th] .
  * Israel and Stewart (1979a) W. Israel and J. M. Stewart, “Transient relativistic thermodynamics and kinetic theory,” Annals Phys. 118, 341–372 (1979a).
  * Baier  _et al._ (2008) Rudolf Baier, Paul Romatschke, Dam Thanh Son, Andrei O. Starinets, and Mikhail A. Stephanov, “Relativistic viscous hydrodynamics, conformal invariance, and holography,” JHEP 04, 100 (2008), arXiv:0712.2451 [hep-th] .
  * Kapusta and Gale (2011) J. I. Kapusta and Charles Gale,  _Finite-temperature field theory: Principles and applications_, Cambridge Monographs on Mathematical Physics (Cambridge University Press, 2011).
  * Bellac (2011) Michel Le Bellac,  _Thermal Field Theory_, Cambridge Monographs on Mathematical Physics (Cambridge University Press, 2011).
  * Schubring (2015) Daniel Schubring, “Dissipative String Fluids,” Phys. Rev. D91, 043518 (2015), arXiv:1412.3135 [hep-th] .
  * Gaiotto  _et al._ (2015) Davide Gaiotto, Anton Kapustin, Nathan Seiberg, and Brian Willett, “Generalized Global Symmetries,” JHEP 02, 172 (2015), arXiv:1412.5148 [hep-th] .
  * Landau  _et al._ (1984) Lev Davidovich Landau, Evgeníj Míkhaílovítsj Lífshíts, and LP Pitaevskii,  _Electrodynamics of continuous media_ , Vol. 8 (Pergamon Press, 1984).
  * Kluitenberg and De Groot (1954b) Gerrit Alfred Kluitenberg and SR De Groot, “Relativistic thermodynamics of irreversible processes. iv: Systems with polarization and magnetization in an electromagnetic field,” Physica 21, 148–168 (1954b).
  * Kluitenberg and De Groot (1954c) Gerrit Alfred Kluitenberg and SR De Groot, “Relativistic thermodynamics of irreversible processes. v: The energy-momentum tensor of the macroscopic electromagnetic field, the macroscopic forces acting on the matter and the first and second laws of thermodynamics,” Physica 21, 169–192 (1954c).
  * Lichnerowicz (1967) Andre Lichnerowicz,  _Relativistic hydrodynamics and magnetohydrodynamics. Lectures on the Existence of Solutions_ (WA Benjamin, Inc., New York, 1967).
  * Israel (1978) W. Israel, “The Dynamics of Polarization,” Gen. Rel. Grav. 9, 451–468 (1978).
  * Gedalin (1991) M Gedalin, “Relativistic hydrodynamics and thermodynamics of anisotropic plasmas,” Physics of Fluids B: Plasma Physics 3, 1871–1875 (1991).
  * Onsager (1931) Lars Onsager, “Reciprocal relations in irreversible processes. i.” Phys. Rev. 37, 405–426 (1931).
  * Hooyman  _et al._ (1954) GJ Hooyman, P Mazur, and SR De Groot, “Coefficients of viscosity for a fluid in a magnetic field or in a rotating system,” Physica 21, 355–359 (1954).
  * Nakajima (1957) Sadao Nakajima, “Thermal irreversible processes (in Japanese),” Busseironkenkyu 2, 197–208 (1957).
  * Mori (1958) Hazime Mori, “Statistical-mechanical theory of transport in fluids,” Phys. Rev. 112, 1829–1842 (1958).
  * McLennan (1960) James A. McLennan, “Statistical mechanics of transport in fluids,” Physics of Fluids 3, 493–502 (1960).
  * McLennan (1988) James A. McLennan,  _Introduction to Non Equilibrium Statistical Mechanics (Prentice Hall Advanced Reference Series)_ (Prentice Hall, 1988).
  * Kawasaki and Gunton (1973) K. Kawasaki and J. D. Gunton, “Theory of nonlinear transport processes: Nonlinear shear viscosity and normal stress effects,” Phys. Rev. A 8, 2048–2064 (1973).
  * Zubarev  _et al._ (1979) D. N. Zubarev, A. V. Prozorkevich, and S. A. Smolyanskii, “Derivation of nonlinear generalized equations of quantum relativistic hydrodynamics,” Theor. Math. Phys. 40, 821–831 (1979).
  * Zubarev  _et al._ (1996) Dmitrii N. Zubarev, Vladimir Morozov, and Gerd Ropke,  _Statistical Mechanics of Nonequilibrium Processes, Volume 1: Basic Concepts, Kinetic Theory_ , 1st ed. (Wiley-VCH, 1996).
  * Zubarev  _et al._ (1997) Dmitrii N. Zubarev, Vladimir Morozov, and Gerd Ropke,  _Statistical Mechanics of Nonequilibrium Processes, Volume 2: Relaxation and Hydrodynamic Processes_ (Wiley-VCH, 1997).
  * Sasa (2014) Shin-ichi Sasa, “Derivation of hydrodynamics from the hamiltonian description of particle systems,” Phys. Rev. Lett. 112, 100602 (2014).
  * Becattini  _et al._ (2015) F. Becattini, L. Bucciantini, E. Grossi, and L. Tinti, “Local thermodynamical equilibrium and the beta frame for a quantum relativistic fluid,” Eur. Phys. J. C 75, 191 (2015), arXiv:1403.6265 [hep-th] .
  * Hayata  _et al._ (2015) Tomoya Hayata, Yoshimasa Hidaka, Toshifumi Noumi, and Masaru Hongo, “Relativistic hydrodynamics from quantum field theory on the basis of the generalized Gibbs ensemble method,” Phys. Rev. D 92, 065008 (2015), arXiv:1503.04535 [hep-ph] .
  * Hongo (2017) Masaru Hongo, “Path-integral formula for local thermal equilibrium,” Annals Phys. 383, 1–32 (2017), arXiv:1611.07074 [hep-th] .
  * Hongo (2019) Masaru Hongo, “Nonrelativistic hydrodynamics from quantum field theory: (I) Normal fluid composed of spinless Schrödinger fields,” J. Statist. Phys. 174, 1038 (2019), arXiv:1801.06520 [cond-mat.stat-mech] .
  * Becattini  _et al._ (2019) F. Becattini, M. Buzzegoli, and E. Grossi, “Reworking the Zubarev’s approach to non-equilibrium quantum statistical mechanics,” Particles 2, 197–207 (2019), arXiv:1902.01089 [cond-mat.stat-mech] .
  * Hongo and Hidaka (2019) Masaru Hongo and Yoshimasa Hidaka, “Anomaly-Induced Transport Phenomena from Imaginary-Time Formalism,” Particles 2, 261–280 (2019), arXiv:1902.09166 [hep-th] .
  * Stevenson (1981) Paul M. Stevenson, “Optimized Perturbation Theory,” Phys. Rev. D 23, 2916 (1981).
  * Kleinert (2009) H. Kleinert,  _Path Integrals in Quantum Mechanics, Statistics, Polymer Physics, and Financial Markets_ , EBL-Schweitzer (World Scientific, 2009).
  * Jakovác and Patkós (2015) A. Jakovác and A. Patkós,  _Resummation and Renormalization in Effective Theories of Particle Physics_ , Lecture Notes in Physics (Springer International Publishing, 2015).
  * Green (1954) Melville S. Green, “Markoff random processes and the statistical mechanics of time-dependent phenomena. ii. irreversible processes in fluids,” The Journal of Chemical Physics 22, 398–413 (1954).
  * Nakano (1956) Huzio Nakano, “A method of calculation of electrical conductivity,” Prog. Theor. Phys. 15, 77–79 (1956).
  * Kubo (1957) Ryogo Kubo, “Statistical-mechanical theory of irreversible processes. i. general theory and simple applications to magnetic and conduction problems,” Journal of the Physical Society of Japan 12, 570–586 (1957), http://dx.doi.org/10.1143/JPSJ.12.570 .
  * Davidson (2002) Peter Alan Davidson,  _An introduction to magnetohydrodynamics_ (AAPT, 2002).
  * Vardhan  _et al._ (2022) Shreya Vardhan, Sašo Grozdanov, Samuel Leutheusser, and Hong Liu, “A new formulation of strong-field magnetohydrodynamics for neutron stars,” (2022), arXiv:2207.01636 [astro-ph.HE] .
  * de Groot  _et al._ (1980) S. R. de Groot, W. A. van Leeuwen, and Ch. G. van Weert,  _Relativistic kinetic theory: principles and applications_ (North-Holland Publishing, 1980).
  * Chapman and Cowling (1970) S. Chapman and T. G. Cowling,  _The mathematical theory of non-uniform gases: an account of the kinetic theory of viscosity, thermal conduction, and diffusion in gases_ , 3rd ed. (Cambridge University Press, 1970).
  * Grad (1949) H. Grad, “On the kinetic theory of rarefied gases,” Pure Appl. Math. 2, 331 (1949).
  * Denicol  _et al._ (2012a) G. S. Denicol, H. Niemi, E. Molnar, and D. H. Rischke, “Derivation of transient relativistic fluid dynamics from the Boltzmann equation,” Phys. Rev. D 85, 114047 (2012a), [Erratum: Phys.Rev.D 91, 039902 (2015)], arXiv:1202.4551 [nucl-th] .
  * Stewart (1971) John M. Stewart,  _Non-equilibrium relativistic kinetic theory_ (Springer Berlin Heidelberg, 1971).
  * Mohanty  _et al._ (2019) Payal Mohanty, Ashutosh Dash, and Victor Roy, “One particle distribution function and shear viscosity in magnetic field: a relaxation time approach,” Eur. Phys. J. A 55, 35 (2019), arXiv:1804.01788 [nucl-th] .
  * Das  _et al._ (2020) Arpan Das, Hiranmaya Mishra, and Ranjita K. Mohapatra, “Electrical conductivity and Hall conductivity of a hot and dense quark gluon plasma in a magnetic field: A quasiparticle approach,” Phys. Rev. D 101, 034027 (2020), arXiv:1907.05298 [hep-ph] .
  * Dey  _et al._ (2021b) Jayanta Dey, Sarthak Satapathy, Prasanta Murmu, and Sabyasachi Ghosh, “Shear viscosity and electrical conductivity of the relativistic fluid in the presence of a magnetic field: A massless case,” Pramana 95, 125 (2021b), arXiv:1907.11164 [hep-ph] .
  * Das  _et al._ (2019) Arpan Das, Hiranmaya Mishra, and Ranjita K. Mohapatra, “Transport coefficients of hot and dense hadron gas in a magnetic field: a relaxation time approach,” Phys. Rev. D 100, 114004 (2019), arXiv:1909.06202 [hep-ph] .
  * Rath and Patra (2021) Shubhalaxmi Rath and Binoy Krishna Patra, “Viscous properties of hot and dense QCD matter in the presence of a magnetic field,” Eur. Phys. J. C 81, 139 (2021), arXiv:2010.02886 [hep-ph] .
  * Panda  _et al._ (2021b) Ankit Kumar Panda, Ashutosh Dash, Rajesh Biswas, and Victor Roy, “Relativistic resistive dissipative magnetohydrodynamics from the relaxation time approximation,” Phys. Rev. D 104, 054004 (2021b), arXiv:2104.12179 [nucl-th] .
  * Rath and Dash (2021) Shubhalaxmi Rath and Sadhana Dash, “Effects of weak magnetic field and finite chemical potential on the transport of charge and heat in hot QCD matter,” (2021), arXiv:2112.11802 [hep-ph] .
  * Rath and Dash (2022) Shubhalaxmi Rath and Sadhana Dash, “Momentum transport properties of a hot and dense QCD matter in a weak magnetic field,” (2022), arXiv:2203.16199 [hep-ph] .
  * Satapathy  _et al._ (2021) Sarthak Satapathy, Snigdha Ghosh, and Sabyasachi Ghosh, “Kubo estimation of the electrical conductivity for a hot relativistic fluid in the presence of a magnetic field,” Phys. Rev. D 104, 056030 (2021), arXiv:2104.03917 [hep-ph] .
  * Ghosh and Ghosh (2021) Snigdha Ghosh and Sabyasachi Ghosh, “One-loop Kubo estimations of the shear and bulk viscous coefficients for hot and magnetized Bosonic and Fermionic systems,” Phys. Rev. D 103, 096015 (2021), arXiv:2011.04261 [hep-ph] .
  * Burnett (1935) D. Burnett, “The Distribution of Velocities in a Slightly Non-Uniform Gas,” Proc. Lond. Math. Soc. 39, 385 (1935).
  * Burnett (1936) D. Burnett, “The Distribution of Molecular Velocities and the Mean Motion in a Non-Uniform Gas,” Proc. Lond. Math. Soc. 40, 382 (1936).
  * Hiscock and Lindblom (1983) W. A. Hiscock and L. Lindblom, “Stability and causality in dissipative relativistic fluids,” Annals Phys. 151, 466–496 (1983).
  * Hiscock and Lindblom (1985) William A. Hiscock and Lee Lindblom, “Generic instabilities in first-order dissipative relativistic fluid theories,” Phys. Rev. D 31, 725–733 (1985).
  * Hiscock and Lindblom (1987) William A. Hiscock and Lee Lindblom, “Linear plane waves in dissipative relativistic fluids,” Phys. Rev. D 35, 3723–3732 (1987).
  * Denicol  _et al._ (2008) G. S. Denicol, T. Kodama, T. Koide, and Ph. Mota, “Stability and Causality in relativistic dissipative hydrodynamics,” J. Phys. G 35, 115102 (2008), arXiv:0807.3120 [hep-ph] .
  * Pu  _et al._ (2010) Shi Pu, Tomoi Koide, and Dirk H. Rischke, “Does stability of relativistic dissipative fluid dynamics imply causality?” Phys. Rev. D 81, 114039 (2010), arXiv:0907.3906 [hep-ph] .
  * Biswas  _et al._ (2020) Rajesh Biswas, Ashutosh Dash, Najmul Haque, Shi Pu, and Victor Roy, “Causality and stability in relativistic viscous non-resistive magneto-fluid dynamics,” JHEP 10, 171 (2020), arXiv:2007.05431 [nucl-th] .
  * Bemfica  _et al._ (2018) Fábio S. Bemfica, Marcelo M. Disconzi, and Jorge Noronha, “Causality and existence of solutions of relativistic viscous fluid dynamics with gravity,” Phys. Rev. D 98, 104064 (2018), arXiv:1708.06255 [gr-qc] .
  * Bemfica  _et al._ (2019) Fábio S. Bemfica, Fábio S. Bemfica, Marcelo M. Disconzi, Marcelo M. Disconzi, Jorge Noronha, and Jorge Noronha, “Nonlinear Causality of General First-Order Relativistic Viscous Hydrodynamics,” Phys. Rev. D 100, 104020 (2019), [Erratum: Phys.Rev.D 105, 069902 (2022)], arXiv:1907.12695 [gr-qc] .
  * Kovtun (2019) Pavel Kovtun, “First-order relativistic hydrodynamics is stable,” JHEP 10, 034 (2019), arXiv:1907.08191 [hep-th] .
  * Bemfica  _et al._ (2022) Fabio S. Bemfica, Marcelo M. Disconzi, and Jorge Noronha, “First-Order General-Relativistic Viscous Fluid Dynamics,” Phys. Rev. X 12, 021044 (2022), arXiv:2009.11388 [gr-qc] .
  * Hoult and Kovtun (2020) Raphael E. Hoult and Pavel Kovtun, “Stable and causal relativistic Navier-Stokes equations,” JHEP 06, 067 (2020), arXiv:2004.04102 [hep-th] .
  * Armas and Camilloni (2022) Jay Armas and Filippo Camilloni, “A stable and causal model of magnetohydrodynamics,” (2022), arXiv:2201.06847 [hep-th] .
  * Bobylev (1982) A. V. Bobylev, “The Chapman–Enskog and Grad methods for solving the Boltzmann equation,” Sov. Phys. Dokl. 27, 29 (1982).
  * Betz  _et al._ (2009) B. Betz, D. Henkel, and D. H. Rischke, “Complete second-order dissipative fluid dynamics,” J. Phys. G 36, 064029 (2009).
  * Denicol  _et al._ (2010) G. S. Denicol, T. Koide, and D. H. Rischke, “Dissipative relativistic fluid dynamics: a new way to derive the equations of motion from kinetic theory,” Phys. Rev. Lett. 105, 162501 (2010), arXiv:1004.5013 [nucl-th] .
  * Denicol  _et al._ (2012b) Gabriel S. Denicol, Xu-Guang Huang, Tomoi Koide, and Dirk H. Rischke, “Consistency of Field-Theoretical and Kinetic Calculations of Viscous Transport Coefficients for a Relativistic Fluid,” Phys. Lett. B 708, 174–178 (2012b), arXiv:1003.0780 [hep-th] .
  * Denicol  _et al._ (2012c) G. S. Denicol, E. Molnár, H. Niemi, and D. H. Rischke, “Derivation of fluid dynamics from kinetic theory with the 14-moment approximation,” Eur. Phys. J. A 48, 170 (2012c), arXiv:1206.1554 [nucl-th] .
  * Israel and Stewart (1976) W. Israel and J. M. Stewart, “Thermodynamics of nonstationary and transient effects in a relativistic gas,” Phys. Lett. A 58, 213–215 (1976).
  * Israel and Stewart (1979b) W. Israel and J. M. Stewart, “On transient relativistic thermodynamics and kinetic theory.II,” Proc. R. Soc. Lond. A 365, 43–52 (1979b).
  * Jaiswal (2013) Amaresh Jaiswal, “Relativistic dissipative hydrodynamics from kinetic theory with relaxation time approximation,” Phys. Rev. C 87, 051901 (2013), arXiv:1302.6311 [nucl-th] .
  * Bazow  _et al._ (2014) Dennis Bazow, Ulrich W. Heinz, and Michael Strickland, “Second-order (2+1)-dimensional anisotropic hydrodynamics,” Phys. Rev. C 90, 054910 (2014), arXiv:1311.6720 [nucl-th] .
  * Denicol  _et al._ (2014) G. S. Denicol, S. Jeon, and C. Gale, “Transport Coefficients of Bulk Viscous Pressure in the 14-moment approximation,” Phys. Rev. C 90, 024912 (2014), arXiv:1403.0962 [nucl-th] .
  * Molnar  _et al._ (2016) E. Molnar, H. Niemi, and D. H. Rischke, “Derivation of anisotropic dissipative fluid dynamics from the Boltzmann equation,” Phys. Rev. D 93, 114025 (2016), arXiv:1602.00573 [nucl-th] .
  * Molnár  _et al._ (2016) Etele Molnár, Harri Niemi, and Dirk H. Rischke, “Closing the equations of motion of anisotropic fluid dynamics by a judicious choice of a moment of the Boltzmann equation,” Phys. Rev. D 94, 125003 (2016), arXiv:1606.09019 [nucl-th] .
  * Tinti  _et al._ (2019) L. Tinti, G. Vujanovic, J. Noronha, and U. Heinz, “Resummed hydrodynamic expansion for a plasma of particles interacting with fields,” Phys. Rev. D 99, 016009 (2019), arXiv:1808.06436 [nucl-th] .
  * Most  _et al._ (2021) Elias R. Most, Jorge Noronha, and Alexander A. Philippov, “Modeling general-relativistic plasmas with collisionless moments and dissipative two-fluid magnetohydrodynamics,” (2021), arXiv:2111.05752 [astro-ph.HE] .
  * Wagner  _et al._ (2022) David Wagner, Andrea Palermo, and Victor E. Ambruş, “Inverse Reynolds-dominance approach to transient fluid dynamics,” (2022), arXiv:2203.12608 [nucl-th] .
  * Marcowith  _et al._ (2020) A. Marcowith, G. Ferrand, M. Grech, Z. Meliani, I. Plotnikov, and R. Walder, “Multi-scale simulations of particle acceleration in astrophysical systems,” Liv. Rev. Comput. Astrophys. 6, 1 (2020), arXiv:2002.09411 [astro-ph.HE] .
  * Pohl  _et al._ (2020) Martin Pohl, Masahiro Hoshino, and Jacek Niemiec, “PIC Simulation Methods for Cosmic Radiation and Plasma Instabilities,” Prog. Part. Nucl. Phys. 111, 103751 (2020), arXiv:1912.02673 [astro-ph.HE] .
  * Baumgarte and Shapiro (2010) Thomas W. Baumgarte and Stuart L. Shapiro,  _Numerical Relativity: Solving Einstein’s Equations on the Computer_ (Cambridge University Press, 2010).
  * Baumgarte and Shapiro (2021) Thomas W. Baumgarte and Stuart L. Shapiro,  _Numerical Relativity: Starting from Scratch_ (Cambridge University Press, 2021).
  * Fukushima and Hidaka (2018) Kenji Fukushima and Yoshimasa Hidaka, “Electric conductivity of hot and dense quark matter in a magnetic field with Landau level resummation via kinetic equations,” Phys. Rev. Lett. 120, 162301 (2018), arXiv:1711.01472 [hep-ph] .
  * Fukushima and Hidaka (2020) Kenji Fukushima and Yoshimasa Hidaka, “Resummation for the Field-theoretical Derivation of the Negative Magnetoresistance,” JHEP 04, 162 (2020), arXiv:1906.02683 [hep-ph] .
  * Arnold  _et al._ (2006) Peter Brockway Arnold, Caglar Dogan, and Guy D. Moore, “The Bulk Viscosity of High-Temperature QCD,” Phys. Rev. D 74, 085021 (2006), arXiv:hep-ph/0608012 .
  * Arnold  _et al._ (2000) Peter Brockway Arnold, Guy D. Moore, and Laurence G. Yaffe, “Transport coefficients in high temperature gauge theories. 1. Leading log results,” JHEP 11, 001 (2000), arXiv:hep-ph/0010177 .
  * Donoghue  _et al._ (2014) J. F. Donoghue, E. Golowich, and Barry R. Holstein,  _Dynamics of the standard model_, Vol. 2 (CUP, 2014).
  * Zyla  _et al._ (2020) P. A. Zyla _et al._ (Particle Data Group), “Review of Particle Physics,” PTEP 2020, 083C01 (2020).
  * Fukuda and Miyamoto (1949) Hiroshi Fukuda and Yoneji Miyamoto, “On the $\gamma$-Decay of Neutral Meson,” Progress of Theoretical Physics 4, 347–357 (1949), http://oup.prod.sis.lan/ptp/article-pdf/4/3/347/5335979/4-3-347.pdf .
  * Adler (1969) Stephen L. Adler, “Axial vector vertex in spinor electrodynamics,” Phys. Rev. 177, 2426–2438 (1969).
  * Bell and Jackiw (1969) J. S. Bell and R. Jackiw, “A PCAC puzzle: $\pi^{0}\to\gamma\gamma$ in the $\sigma$ model,” Nuovo Cim. A 60, 47–61 (1969).
  * Bertlmann (2000) Reinhold A Bertlmann,  _Anomalies in quantum field theory_ , Vol. 91 (Oxford University Press, 2000).
  * Fujikawa  _et al._ (2004) Kazuo Fujikawa, K Fujikawa, Hiroshi Suzuki, _et al._ ,  _Path integrals and quantum anomalies_ , Vol. 122 (Oxford University Press, 2004).
  * ’t Hooft (1980) Gerard ’t Hooft, “Naturalness, chiral symmetry, and spontaneous chiral symmetry breaking,” in  _Recent Developments in Gauge Theories. Proceedings, Nato Advanced Study Institute, Cargese, France, August 26 - September 8, 1979_, Vol. 59 (1980) pp. 135–157.
  * Frishman  _et al._ (1981) Y. Frishman, A. Schwimmer, Tom Banks, and S. Yankielowicz, “The Axial Anomaly and the Bound State Spectrum in Confining Theories,” Nucl. Phys. B177, 157–171 (1981).
  * Vilenkin (1980) A. Vilenkin, “Equilibrium parity violating current in a magnetic field,” Phys. Rev. D22, 3080–3084 (1980).
  * Nielsen and Ninomiya (1983) Holger Bech Nielsen and Masao Ninomiya, “The Adler-Bell-Jackiw anomaly and Weyl fermions in a crystal,” Phys. Lett. 130B, 389–396 (1983).
  * Alekseev  _et al._ (1998) Anton Yu. Alekseev, Vadim V. Cheianov, and Jurg Frohlich, “Universality of transport properties in equilibrium, Goldstone theorem and chiral anomaly,” Phys. Rev. Lett. 81, 3503–3506 (1998), arXiv:cond-mat/9803346 [cond-mat] .
  * Son and Surowka (2009) Dam T. Son and Piotr Surowka, “Hydrodynamics with Triangle Anomalies,” Phys. Rev. Lett. 103, 191601 (2009), arXiv:0906.5044 [hep-th] .
  * Neiman and Oz (2011) Yasha Neiman and Yaron Oz, “Relativistic Hydrodynamics with General Anomalous Charges,” JHEP 03, 023 (2011), arXiv:1011.5107 [hep-th] .
  * Sadofyev and Isachenkov (2011) A. V. Sadofyev and M. V. Isachenkov, “The Chiral magnetic effect in hydrodynamical approach,” Phys. Lett. B 697, 404–406 (2011), arXiv:1010.1550 [hep-th] .
  * Kharzeev and Yee (2011) Dmitri E. Kharzeev and Ho-Ung Yee, “Anomalies and time reversal invariance in relativistic hydrodynamics: the second order and higher dimensional formulations,” Phys. Rev. D 84, 045025 (2011), arXiv:1105.6360 [hep-th] .
  * Furusawa and Hongo (2021) Takuya Furusawa and Masaru Hongo, “Anomaly-induced edge currents in hydrodynamics with parity anomaly,” Phys. Rev. D 104, 125021 (2021), arXiv:2108.12192 [hep-th] .
  * Lin (2012) Shu Lin, “An anomalous hydrodynamics for chiral superfluid,” Phys. Rev. D 85, 045015 (2012), arXiv:1112.3215 [hep-ph] .
  * Banerjee  _et al._ (2012) Nabamita Banerjee, Jyotirmoy Bhattacharya, Sayantani Bhattacharyya, Sachin Jain, Shiraz Minwalla, and Tarun Sharma, “Constraints on Fluid Dynamics from Equilibrium Partition Functions,” JHEP 09, 046 (2012), arXiv:1203.3544 [hep-th] .
  * Jensen  _et al._ (2012) Kristan Jensen, Matthias Kaminski, Pavel Kovtun, Rene Meyer, Adam Ritz, and Amos Yarom, “Towards hydrodynamics without an entropy current,” Phys. Rev. Lett. 109, 101601 (2012), arXiv:1203.3556 [hep-th] .
  * Jensen (2012) Kristan Jensen, “Triangle Anomalies, Thermodynamics, and Hydrodynamics,” Phys. Rev. D 85, 125017 (2012), arXiv:1203.3599 [hep-th] .
  * Jensen  _et al._ (2013a) Kristan Jensen, R. Loganayagam, and Amos Yarom, “Thermodynamics, gravitational anomalies and cones,” JHEP 02, 088 (2013a), arXiv:1207.5824 [hep-th] .
  * Jensen  _et al._ (2013b) Kristan Jensen, Pavel Kovtun, and Adam Ritz, “Chiral conductivities and effective field theory,” JHEP 10, 186 (2013b), arXiv:1307.3234 [hep-th] .
  * Jensen  _et al._ (2014a) Kristan Jensen, R. Loganayagam, and Amos Yarom, “Anomaly inflow and thermal equilibrium,” JHEP 05, 134 (2014a), arXiv:1310.7024 [hep-th] .
  * Jensen  _et al._ (2014b) Kristan Jensen, R. Loganayagam, and Amos Yarom, “Chern-Simons terms from thermal circles and anomalies,” JHEP 05, 110 (2014b), arXiv:1311.2935 [hep-th] .
  * Haehl  _et al._ (2014) Felix M. Haehl, R. Loganayagam, and Mukund Rangamani, “Effective actions for anomalous hydrodynamics,” JHEP 03, 034 (2014), arXiv:1312.0610 [hep-th] .
  * Golkar and Sethi (2016) Siavash Golkar and Savdeep Sethi, “Global Anomalies and Effective Field Theory,” JHEP 05, 105 (2016), arXiv:1512.02607 [hep-th] .
  * Chowdhury and David (2016) Subham Dutta Chowdhury and Justin R. David, “Global gravitational anomalies and transport,” JHEP 12, 116 (2016), arXiv:1604.05003 [hep-th] .
  * Glorioso  _et al._ (2019) Paolo Glorioso, Hong Liu, and Srivatsan Rajagopal, “Global Anomalies, Discrete Symmetries, and Hydrodynamic Effective Actions,” JHEP 01, 043 (2019), arXiv:1710.03768 [hep-th] .
  * Mañes  _et al._ (2018) Juan L. Mañes, Eugenio Megías, Manuel Valle, and Miguel A. Vazquez-Mozo, “Non-Abelian Anomalous (Super)Fluids in Thermal Equilibrium from Differential Geometry,” JHEP 11, 076 (2018), arXiv:1806.07647 [hep-th] .
  * Mañes  _et al._ (2019) Juan L. Mañes, Eugenio Megías, Manuel Valle, and Miguel Á. Vázquez-Mozo, “Anomalous Currents and Constitutive Relations of a Chiral Hadronic Superfluid,” JHEP 12, 018 (2019), arXiv:1910.04013 [hep-th] .
  * Liao (2015) Jinfeng Liao, “Anomalous transport effects and possible environmental symmetry violation in heavy-ion collisions,” Pramana 84, 901–926 (2015), arXiv:1401.2500 [hep-ph] .
  * Skokov  _et al._ (2017) Vladimir Skokov, Paul Sorensen, Volker Koch, Soeren Schlichting, Jim Thomas, Sergei Voloshin, Gang Wang, and Ho-Ung Yee, “Chiral Magnetic Effect Task Force Report,” Chin. Phys. C41, 072001 (2017), arXiv:1608.00982 [nucl-th] .
  * Landsteiner (2016) Karl Landsteiner, “Notes on Anomaly Induced Transport,”  _Proceedings, 56th Cracow School of Theoretical Physics : A Panorama of Holography: Zakopane, Poland, May 24-June 1, 2016_ , Acta Phys. Polon. B47, 2617 (2016), arXiv:1610.04413 [hep-th] .
  * Armitage  _et al._ (2018) N. P. Armitage, E. J. Mele, and Ashvin Vishwanath, “Weyl and Dirac Semimetals in Three Dimensional Solids,” Rev. Mod. Phys. 90, 015001 (2018), arXiv:1705.01111 [cond-mat.str-el] .
  * Chernodub  _et al._ (2021) Maxim N. Chernodub, Yago Ferreiros, Adolfo G. Grushin, Karl Landsteiner, and María A. H. Vozmediano, “Thermal transport, geometry, and anomalies,” (2021), arXiv:2110.05471 [cond-mat.mes-hall] .
  * Biskamp (1997) Dieter Biskamp,  _Nonlinear magnetohydrodynamics_ , Vol. 1 (Cambridge University Press, 1997).
  * Gedalin (1993) M. Gedalin, “Linear waves in relativistic anisotropic magnetohydrodynamics,” Phys. Rev. E 47, 4354–4357 (1993).
  * Akamatsu and Yamamoto (2013) Yukinao Akamatsu and Naoki Yamamoto, “Chiral Plasma Instabilities,” Phys. Rev. Lett. 111, 052002 (2013), arXiv:1302.2125 [nucl-th] .
  * Akamatsu and Yamamoto (2014) Yukinao Akamatsu and Naoki Yamamoto, “Chiral Langevin theory for non-Abelian plasmas,” Phys. Rev. D 90, 125031 (2014), arXiv:1402.4174 [hep-th] .
  * Yamamoto (2016) Naoki Yamamoto, “Chiral transport of neutrinos in supernovae: Neutrino-induced fluid helicity and helical plasma instability,” Phys. Rev. D 93, 065017 (2016), arXiv:1511.00933 [astro-ph.HE] .
  * Kojima and Miura (2019) Yasufumi Kojima and Yuri Miura, “The growth of chiral magnetic instability in a large-scale magnetic field,” PTEP 2019, 043E01 (2019), arXiv:1902.10888 [astro-ph.HE] .
  * Liu and Huang (2020) Yu-Chen Liu and Xu-Guang Huang, “Anomalous chiral transports and spin polarization in heavy-ion collisions,” Nucl. Sci. Tech. 31, 56 (2020), arXiv:2003.12482 [nucl-th] .
  * Gao  _et al._ (2020) Jian-Hua Gao, Guo-Liang Ma, Shi Pu, and Qun Wang, “Recent developments in chiral and spin polarization effects in heavy-ion collisions,” Nucl. Sci. Tech. 31, 90 (2020), arXiv:2005.10432 [hep-ph] .
  * Huang (2021) Xu-Guang Huang, “Vorticity and Spin Polarization — A Theoretical Perspective,” Nucl. Phys. A 1005, 121752 (2021), arXiv:2002.07549 [nucl-th] .
  * Becattini and Lisa (2020) Francesco Becattini and Michael A. Lisa, “Polarization and Vorticity in the Quark–Gluon Plasma,” Ann. Rev. Nucl. Part. Sci. 70, 395–423 (2020), arXiv:2003.03640 [nucl-ex] .
  * Becattini (2022) Francesco Becattini, “Spin and polarization: a new direction in relativistic heavy ion physics,” (2022), arXiv:2204.01144 [nucl-th] .
  * Grozdanov  _et al._ (2019) Sašo Grozdanov, Andrew Lucas, and Napat Poovuttikul, “Holography and hydrodynamics with weakly broken symmetries,” Phys. Rev. D 99, 086012 (2019), arXiv:1810.10016 [hep-th] .
  * Stephanov and Yin (2018) M. Stephanov and Y. Yin, “Hydrodynamics with parametric slowing down and fluctuations near the critical point,” Phys. Rev. D 98, 036006 (2018), arXiv:1712.10305 [nucl-th] .
  * Belinfante (1939) F.J. Belinfante, “On the spin angular momentum of mesons,” Physica 6, 887 – 898 (1939).
  * Belinfante (1940) F.J. Belinfante, “On the current and the density of the electric charge, the energy, the linear momentum and the angular momentum of arbitrary fields,” Physica 7, 449 – 474 (1940).
  * Rosenfeld (1940) Léon Rosenfeld, “On the current and the density of the electric charge, the energy, the linear momentum and the angular momentum of arbitrary fields,” Mem. Acad. Roy. Belg. Cl. Sc. 18, 1 – 30 (1940).
  * Montenegro  _et al._ (2017) David Montenegro, Leonardo Tinti, and Giorgio Torrieri, “Ideal relativistic fluid limit for a medium with polarization,” Phys. Rev. D 96, 056012 (2017), [Addendum: Phys.Rev.D 96, 079901 (2017)], arXiv:1701.08263 [hep-th] .
  * Florkowski  _et al._ (2018) Wojciech Florkowski, Bengt Friman, Amaresh Jaiswal, and Enrico Speranza, “Relativistic fluid dynamics with spin,” Phys. Rev. C 97, 041901 (2018), arXiv:1705.00587 [nucl-th] .
  * Florkowski  _et al._ (2019) Wojciech Florkowski, Avdhesh Kumar, and Radoslaw Ryblewski, “Relativistic hydrodynamics for spin-polarized fluids,” Prog. Part. Nucl. Phys. 108, 103709 (2019), arXiv:1811.04409 [nucl-th] .
  * Montenegro and Torrieri (2019) David Montenegro and Giorgio Torrieri, “Causality and dissipation in relativistic polarizable fluids,” Phys. Rev. D 100, 056011 (2019), arXiv:1807.02796 [hep-th] .
  * Hattori  _et al._ (2019b) Koichi Hattori, Masaru Hongo, Xu-Guang Huang, Mamoru Matsuo, and Hidetoshi Taya, “Fate of spin polarization in a relativistic fluid: An entropy-current analysis,” Phys. Lett. B 795, 100–106 (2019b), arXiv:1901.06615 [hep-th] .
  * Fukushima and Pu (2021) Kenji Fukushima and Shi Pu, “Spin hydrodynamics and symmetric energy-momentum tensors – A current induced by the spin vorticity –,” Phys. Lett. B 817, 136346 (2021), arXiv:2010.01608 [hep-th] .
  * Bhadury  _et al._ (2021) Samapan Bhadury, Wojciech Florkowski, Amaresh Jaiswal, Avdhesh Kumar, and Radoslaw Ryblewski, “Relativistic dissipative spin dynamics in the relaxation time approximation,” Phys. Lett. B 814, 136096 (2021), arXiv:2002.03937 [hep-ph] .
  * Li  _et al._ (2021) Shiyong Li, Mikhail A. Stephanov, and Ho-Ung Yee, “Nondissipative Second-Order Transport, Spin, and Pseudogauge Transformations in Hydrodynamics,” Phys. Rev. Lett. 127, 082302 (2021), arXiv:2011.12318 [hep-th] .
  * She  _et al._ (2021) Duan She, Anping Huang, Defu Hou, and Jinfeng Liao, “Relativistic Viscous Hydrodynamics with Angular Momentum,” (2021), arXiv:2105.04060 [nucl-th] .
  * Gallegos  _et al._ (2021) A. D. Gallegos, U. Gürsoy, and A. Yarom, “Hydrodynamics of spin currents,” SciPost Phys. 11, 041 (2021), arXiv:2101.04759 [hep-th] .
  * Peng  _et al._ (2021) Hao-Hao Peng, Jun-Jie Zhang, Xin-Li Sheng, and Qun Wang, “Ideal Spin Hydrodynamics from the Wigner Function Approach,” Chin. Phys. Lett. 38, 116701 (2021), arXiv:2107.00448 [hep-th] .
  * Hu (2022) Jin Hu, “Relativistic first-order spin hydrodynamics via the Chapman-Enskog expansion,” Phys. Rev. D 105, 076009 (2022), arXiv:2111.03571 [hep-ph] .
  * Daher  _et al._ (2022) Asaad Daher, Arpan Das, Wojciech Florkowski, and Radoslaw Ryblewski, “Equivalence of canonical and phenomenological formulations of spin hydrodynamics,” (2022), arXiv:2202.12609 [nucl-th] .
  * Gallegos  _et al._ (2022) A. D. Gallegos, U. Gursoy, and A. Yarom, “Hydrodynamics, spin currents and torsion,” (2022), arXiv:2203.05044 [hep-th] .
  * Weickgenannt  _et al._ (2022) Nora Weickgenannt, David Wagner, Enrico Speranza, and Dirk Rischke, “Relativistic second-order dissipative spin hydrodynamics from the method of moments,” (2022), arXiv:2203.04766 [nucl-th] .
  * Singh  _et al._ (2022) Rajeev Singh, Masoud Shokri, and S. M. A. Tabatabaee Mehr, “Relativistic magnetohydrodynamics with spin,” (2022), arXiv:2202.11504 [hep-ph] .
  * Bhadury  _et al._ (2022) Samapan Bhadury, Wojciech Florkowski, Amaresh Jaiswal, Avdhesh Kumar, and Radoslaw Ryblewski, “Relativistic spin-magnetohydrodynamics,” (2022), arXiv:2204.01357 [nucl-th] .
  * Koide (2013) T. Koide, “Spin-electromagnetic hydrodynamics and magnetization induced by spin-magnetic interaction,” Phys. Rev. C 87, 034902 (2013), arXiv:1206.1976 [physics.flu-dyn] .
  * Grozdanov and Poovuttikul (2019) Sašo Grozdanov and Napat Poovuttikul, “Generalised global symmetries in holography: magnetohydrodynamic waves in a strongly interacting plasma,” JHEP 04, 141 (2019), arXiv:1707.04182 [hep-th] .
  * Bhattacharya  _et al._ (2014) Jyotirmoy Bhattacharya, Sayantani Bhattacharyya, Shiraz Minwalla, and Amos Yarom, “A Theory of first order dissipative superfluid dynamics,” JHEP 05, 147 (2014), arXiv:1105.3733 [hep-th] .
  * Kovtun (2012) Pavel Kovtun, “Lectures on hydrodynamic fluctuations in relativistic theories,” J. Phys. A 45, 473001 (2012), arXiv:1205.5040 [hep-th] .
  * Minami and Hidaka (2013) Yuki Minami and Yoshimasa Hidaka, “Relativistic hydrodynamics from the projection operator method,” Phys. Rev. E 87, 023007 (2013), arXiv:1210.1313 [hep-ph] .