# Beyond Active Engagement: The Significance of Lurkers in a Polarized Twitter
Debate

Anees Baqir
Ca’ Foscari University of Venice
Via Torino 155, 30172 Venice, Italy
& Yijing Chen
Central European University
Quellenstrasse 51, 1100 Wien, Austria
& Fernando Diaz-Diaz
Institute for Cross-Disciplinary Physics
and Complex Systems IFISC (UIB-CSIC)
07122 Palma de Mallorca, Spain
& Sercan Kiyak
Institute of Media Studies, KU Leuven
Parkstraat 45 box 3603, 3000 Leuven, Belgium
& Thomas Louf
Institute for Cross-Disciplinary Physics
and Complex Systems IFISC (UIB-CSIC)
07122 Palma de Mallorca, Spain
& Virginia Morini
University of Pisa, Department of Computer Science
Largo Bruno Pontecorvo, 3, 56127 Pisa PI, Italia
& Valentina Pansanella
Scuola Normale Superiore
P.za dei Cavalieri, 7, 56126 Pisa PI, Italia
& Maddalena Torricelli
City University of London, Department of Mathematics
London EC1V 0HB, United Kingdom
& Alessandro Galeazzi
Ca’ Foscari University of Venice
Via Torino 155, 30172 Venice, Italy

###### Abstract

The emergence of new public forums in the shape of online social media has
introduced unprecedented challenges to public discourse, including
polarization, misinformation, and the emergence of echo chambers. While
existing research has extensively studied the behavior of active users within
echo chambers, little attention has been given to the hidden audience, also
known as lurkers, who passively consume content without actively engaging.
This study aims to estimate the share of the hidden audience and investigate
their interplay with the echo chamber effect. Using Twitter as a case study,
we analyze a polarized political debate to understand the engagement patterns
and factors influencing the hidden audience’s presence. Our findings reveal a
relevant fraction of users that consume content without active interaction,
which underscores the importance of considering their presence in online
debates. Notably, our results indicate that the engagement of the hidden
audience is primarily influenced by factors such as the reliability of media
sources mentioned in tweets rather than the ideological stance of the user
that produced the content. These findings highlight the need for a
comprehensive understanding of the hidden audience’s role in online debates
and how they may influence public opinion.

_Keywords_ polarization $\cdot$ echo chamber $\cdot$ hidden audience $\cdot$
engagement $\cdot$ Twitter

##  1 Introduction

The advent of the digital age has ushered in an era of unprecedented and
instantaneous communication among members of society. While these
technological advancements promised faster and wider access to information,
their influence on the spread of information has turned out to be more
nuanced. Indeed, they have also fostered several pervasive issues, such as
polarization, misinformation, and the emergence of echo chambers that could
influence public opinion and negatively impact society [1, 32, 21]. While
these divergences could already be observed during the 20th century [32], the
introduction of social media networks may have increased the ideological
divide among opposite factions [22]. This radicalization in opinions has been
shown to be a clear obstacle to dialogue, consensus, and policy-making [1,
32], being also considered as “harmful to democracy and society” [34] and a
security risk for the UN [37]. Polarized debate is also a fertile environment
for the spread of misinformation that may harm society at different levels
[12]. Falsehoods and unsubstantiated claims have been shown to spread widely
in social media [39, 40, 28], and they may erode trust in reliable sources
[30]. One of the most insidious consequences of the digital age is the
emergence of echo chambers [11], which have been found in various domains,
including blogs [24], forums [20], and prominent social networks [11] like
Facebook and Twitter [16, 14]. While not intrinsically harmful, echo chambers
may reinforce individuals’ existing beliefs and perspectives, creating
segregated environments where alternative viewpoints are suppressed and
dissenting voices are silenced [35]. Moreover, the echo-chamber effect also
exacerbates polarization and misinformation [36], trapping individuals within
their own ideological bubbles and limiting the exploration of diverse
perspectives.

In light of these pressing challenges, a surge of academic research has
sparked over the last decade to understand the underlying mechanisms and real
extent of echo chambers. Scholars have dedicated substantial efforts to
characterize them within online social networks systematically [10, 11, 17,
31, 9], and developed indices to gauge their presence and strength [23, 18,
27]. Furthermore, various models have been proposed to elucidate the
mechanisms driving the emergence of echo chambers [6, 5, 18]. While these
models consistently emphasize the role of homophily as the primary catalyst
for the echo chamber effect, a diverse range of contributing factors has also
been proposed. Such factors include limited attention spans [10], selective
exposure [29], confirmation bias [33], the silencing effect [35], and even the
influence of feed algorithms [7]. Researchers have also explored methods to
mitigate the impact of echo chambers, like introducing counter-biases within
the feed algorithm [38]. Nonetheless, it is important to note that some
researchers contend that the influence of echo chambers may be overstated [19,
4, 8, 15], thereby fostering ongoing debates surrounding the magnitude of
their effects.

All the empirical studies mentioned above have one point in common: they focus
on active users, meaning those who directly took action to interact with the
content they were shown. On Twitter, for instance, those include liking a
tweet, replying to it, or reposting it – also known as retweeting. But this
might only be the tip of the iceberg: some users may actually belong to an
echo chamber without actively participating in it. These users are known as
_lurkers_. Although precise measurements of their relative prominence are not
generally available, the current estimates place them as the majority of users
on social networks, as they range from $75\text{\,}\mathrm{\char 37\relax}$ to
$90\text{\,}\mathrm{\char 37\relax}$ [25, 2]. Ignoring the presence of lurkers
can lead to inaccurate estimations of echo chamber sizes and their potential
impact on public debate. Consequently, an accurate estimation of their share
is a pressing issue.

In this work, we investigate the prominence of lurkers within the social
network Twitter. To do so, we rely on the recently introduced metric called
impression counts. An impression represents content appearing on a user’s
screen, reflecting visual engagement frequency. Notably, impressions quantify
appearances, not unique viewers. Accordingly, this metric can be employed to
estimate the share of the hidden audience, as well as the user engagement
generated by different types of content. Additionally, we use this metric to
explore whether the lurkers’ share is influenced by factors like the
ideological leaning of the content producer or the reliability and political
bias of the sources used, thereby gaining insights into the lurkers’
engagement patterns.

The remainder of this work is organized as follows: Section 2 describes the
dataset we collect, alongside the methods and the analyses conducted on them.
Section 3 presents the main findings, and Section 4 summarizes the strategy,
highlights the results, and suggests future directions.

##  2 Materials and methods

###  2.1 Data collection

We exploit two different datasets. The first aim is to investigate the
interaction between ideological stances and user engagement, with a specific
focus on assessing the share of hidden audiences in different communities of a
polarized discussion. Accordingly, we collect through the official Twitter API
all tweets related to the debate on whether countries should provide military
support to Ukraine or not in the current 2023 war. This dataset consists of
more than 17 million tweets posted by more than 5.2M users between November
22nd, 2022 and March 1st, 2023. The data collection process involved employing
a search query that included the following terms: “military aid”, “military
support”, “tanks”, “abrams”, “leopard”, “challenger”, “jet”, “aircraft”,
“munitions”, “HIMARS”, “rockets” and “missile”.

We use a second dataset assessing the political leaning and reliability of
news outlets employed in this debate. This dataset is created starting from
Media Bias/Fact Check (MBFC), an independent fact-checking organization that
classifies news outlets based on their reliability and political bias -
originally used in [11]. The dataset described above contains 2190 different
news outlets, their domain names, political tendencies and reliability. The
dataset was last updated in June 2019. These news outlets have been labeled
according to their political leaning, ranging from "extreme left" to "extreme
right." Additionally, some media sources are classified as "questionable" or
"conspiracy-pseudoscience" if they have a tendency to publish misinformation
or false content and endorse conspiracy theories. To ensure a comprehensive
analysis, we manually record the classification of these media outlets based
on the information provided by MBFC, resulting in the inclusion of 468 outlets
in addition to the existing pool of 1722 news outlets that already possess
clear political labels. To calculate the individual leaning of users, each
label is converted into a numerical value: -1 for Extreme Left, -0.66 for
Left, -0.33 for Left-Center, 0 for Least Biased, 0.33 for Right-Center, 0.66
for Right, and +1 for Extreme Right, and the political leaning of a user is
calculated as the average of the scores of all URLs it shared.

###  2.2 Data filtering

Considering our specific focus on engagement and hidden audience, we filter
the dataset in order to maintain only the tweets whit a valid impression
count. Firstly, we filter the dataset by date, retaining only the tweets
posted after the introduction of the impression count metric, which is not
available for tweets posted before the release of such metric (December 15th,
2022). Secondly, we restrict the dataset to tweets in English to avoid
conflating factors (such as geography) that may affect the detection of users’
ideological stances. Lastly, in the analysis of the hidden audience, we
exclusively included original tweets. We disregarded other forms of content
such as quotes, replies and retweets to accurately gauge the user’s ability to
engage their audience, as the impression on replies can be influenced by the
original tweet and may not serve as a reliable proxy for measuring engagement.

###  2.3 Interaction network

Using the dataset described in 2.2, we build the retweet interaction network.
This methodology aligns with prevailing practices in Twitter analysis research
[22, 21, 13], as retweets are regarded as endorsements of content. On the
other hand, quotes, retweets and replies are disregarded since they are less
likely to signify endorsement and are often used for expressing opposing
viewpoints or engaging in polemics [22]. Using the English retweets dataset,
we build a network by assigning a node to each unique user in the dataset -
this includes users who either authored an original English tweet or retweeted
an English tweet containing the specified keywords. We create directed edges
from node A to node B if user A retweeted a post authored by user B and the
weight of the edges is determined by the count of unique retweets between the
two users, reflecting the strength of their interaction. The final interaction
network counts 2.5 million nodes and 7.1 million edges.

###  2.4 Latent Ideology Estimation

To estimate the ideological stance of users in the debate, we start from the
latent ideology algorithm proposed in [3, 4]. Following the studies already
conducted in this field [22, 21], we consider retweets instead of
follower/following relationships as interaction since retweets have been found
to be good indicators of content endorsement [21, 22]. The latent ideology
algorithm requires the extraction of a subset of the influencer nodes which
critically affects the ideology estimation results. The method by which such
extraction is performed is the main topic of the following subsection. Once
the influencer set is known, we apply the Correspondence Analysis algorithm
[26], which follows three steps: (i) Construction of the interaction matrix
$A$, (ii) normalization of the matrix, and (iii) singular value decomposition.
For the first step, we construct a matrix $A$, whose elements $A_{ij}$
represent the number of retweets user $i$ directs toward influencer $j$. Once
$A$ is known, we normalize it as follows. First, we divide by the total number
of retweets, obtaining:

$P=\frac{A}{\sum_{ij}A_{ij}}.$ (1)

Then, we define the following quantities:

$\begin{cases}\textbf{r}=P\textbf{1},\\\ \textbf{c}=\textbf{1}^{T}P,\\\
D_{r}=\text{diag}(\textbf{r}),\\\ D_{c}=\text{diag}(\textbf{c}),\end{cases}$
(2)

and we perform the following normalization operation:

$S=D_{r}^{-1/2}(P-\textbf{r}\textbf{c})D_{c}^{-1/2}$ (3)

For the third step, we perform a singular value decomposition of the form
$S=U\Sigma V^{T}$, where $U,V$ are orthogonal matrices and $\Sigma$ is a
diagonal matrix containing the singular values of $S$. Given the polarized
nature of the networks under investigation, we can approximate the system by
taking the subspace associated with the first singular value of the
decomposition. Thus, we take the latent ideology of user $i$ to be the $i$-th
entry of the first column of the orthogonal matrix $U$, while the median
ideology of their retweeters represents the latent ideology of an influencer.

###  2.5 Influencers Selection

As mentioned above, to apply the ideology-scoring algorithm we first need to
extract a set of influencers from the retweet network. The influencer group
encompasses several subgroups: (i) Russian and Ukrainian politicians, (ii)
official accounts from information media sources such as journals and TV
channels, and (iii) political activists. Users in the retweet network are
ranked according to their in-degree, according to the number of unique users
who have retweeted them. This enables us to start from a manually selected set
of users pertaining to the three aforementioned categories with some of the
highest in-degree. This set then serves as a seed as we further select similar
accounts using the "Who to follow" recommendations made by Twitter on their
accounts’ page. We then refine the selection by excluding users with an in-
degree lower than 100 and those whose content is unrelated to the Ukrainian
conflict. These criteria yield a comprehensive set of 204 influencers.

###  2.6 Estimation of the hidden audience proportion

The estimation of hidden audience proportion leverages tweet-level metrics,
including the number of likes, replies, retweets, quote tweets and, crucially,
the number of impressions. We define the proportion of hidden audience as the
ratio of the number of active actions taken – namely, liking, replying,
retweeting, and quote retweeting – out of the number of impressions received
by a given tweet, a given user, or a given domain, depending on the comparison
unit of interest. We call this ratio Active Engagement (AE).

$AE=\frac{\text{\\# of actions}}{\text{\\# of impressions}}$ (4)

The measure of active engagement, coupled with users’ popularity level, latent
ideology, and the credibility and ideology of shared links, can give us
crucial insights into how the share of the hidden audience may vary along
these dimensions.

##  3 Results

###  3.1 Polarization in the debate around military support to Ukraine on
Twitter

Figure 1: Retweet Network and Ideology Distribution of users and influencers.
Panel (a): influencers and users retweet networks for nodes with a degree
greater than 100, with edges colored on the base of nodes’ ideological
stances. Panel (b): histogram of users’ and influencers’ ideology score (top)
and distributions of top influencers’ retweeters’ ideology obtained with the
latent ideology algorithm (lower). Negative values represent pro-military aid
alignment, while positive values correspond to military aid opponents. Bar
colors in the top panel of (b) represent the density of influencers (pink
color) and users (green color). Consistently with the color palette in panel
(a), the area below retweeter distributions in the bottom panel of (b) is
shaded in salmon if the influencer was inferred to be a supporter of Ukrainian
aid, and in black if the influencer is against providing weapons to Ukraine.

The ongoing debate on whether other countries should provide military
assistance to Ukraine during its conflict with Russia has generated
significant attention from influential figures such as politicians,
journalists, and committed citizens. As discussed above, the formation of echo
chambers, where users predominantly interact with like-minded peers, is a
common phenomenon observed in such controversial debates within social
networks. Examining the presence of echo chambers around this polarizing topic
is our first step to analyzing the dependence of hidden audience share on
ideological stance. We estimated users’ stances by computing ideology scores
based on the influencers they had retweeted. Our results demonstrate a highly
polarized discussion, with individuals advocating for military aid to Ukraine
disproportionately engaging with like-minded users and those against military
intervention engaging mainly with others holding similar views, as shown in
panel a of Fig. 1. The emergence of two distinct user clusters where they
primarily retweet ideologically congruent influencers indicates a fairly
limited volume of cross-ideology influence (see Fig. 1(a)). Also, the latent
ideology analysis shows a clear bimodal distribution of the users’ and
influencers’ opinions as shown in Fig. 1(b), further highlighting the presence
of echo chambers in this debate (see also Fig. S1).

###  3.2 Unveiling the hidden audience in the echo chambers

Having identified two opposing echo chambers, we now turn to the
characterization of the hidden audience of this debate and how they may be
distributed between these two groups.

action | average AE (%) | Pearson’s r
---|---|---
retweet | 0.2909 | -0.3469
reply | 0.2479 | -0.5649
like | 1.1154 | -0.2250
quote | 0.0612 | -0.5690
Table 1: Summary statistics for the active engagement by kind of action taken.
We compute the average AE for all tweets (with and without actions), as well
as Pearson’s r for tweets with actions (with log-scaled values) to show its
correlation with user popularity.

Across individual users. We first show the proportion of hidden audience
across individual users in Fig. 2(a). Overall, we see that the majority of the
audience in such a polarized discussion is “hidden”, as the average value of
AE peaks at around 1% and is particularly low for quote tweets (see Table 1).
Out of the four actions identified in our data, the most prevalent action
after viewing a tweet is liking (bottom left), the AE of which has the weakest
correlation with the number of followers a user has. Such a weakly-correlated
pattern extends to the AE of retweeting (top left), with a slightly higher
level of average AE. On the other hand, when we look at actions that require
textual inputs and may entail conversational interactions among users, such as
replies (top right) and quote retweets (bottom right), there are more
pronounced negative correlations between the AE level and the number of
followers a user has (both log-scaled), and such a visual impression can be
statistically verified by their Pearson’s r values.

Across user groups. Next, we turn our attention to the interplay between
users’ opinions and the hidden audience. Thus, we utilize the inferred
ideology of individual users and compare AE levels between two opposing groups
(i.e., UA aid supporters and opponents, see Fig. 2(b)). Across four actions,
there are some subtle differences in these distributions, like a slightly
higher AE for opponents’ retweeting, replying, and quoting. That being said,
we do not observe a qualitative difference overall, which means that these two
groups, though opposed along the spectrum of ideology, do not appear to behave
differently in terms of their engagement patterns.

Figure 2: User-level hidden audience in the Twitter discussion on military aid
to Ukraine. (a) Bivariate probability density of the number of followers and
the active engagement with respect to retweets (top left), replies (top
right), likes (bottom left), and quotes (bottom right). The active engagement
seems to generally decrease with the number of followers of the original
poster. (b) Boxplots of the active engagement for the same actions as in panel
(a) grouped by users’ ideologies, i.e., UA aid supporters (pink) and opponents
(grey).

Across domains. One possible factor influencing the hidden audience is the
sources used in the tweets. Here we focus on news sources shared in the debate
surrounding our topic of interest with respect to their reliability and
political leaning, as shown in Figs. 3 and 4. Results show that the number of
sharers does not have a strong influence on AE, as we observe across
individual users. Similarly, the order of magnitude of AE does vary across
different types of actions upon tweets sharing domain(s): the majority of
likes maintain a relatively higher AE level around $10^{-3}\sim 10^{-2}$,
while the AE of retweets and replies concentrates in a lower range of
$10^{-4}\sim 10^{-3}$, with the AE of quotes being the lowest around
$10^{-4}$. , requiring more effort and direct interaction, Additionally, when
analyzing the domains that predominantly report misleading information, we
observe a consistently higher level of AE across four types of actions. This
suggests that unreliable domains do not necessarily attract a larger number of
unique sharers, but they do trigger more visible user-content interactions
with smaller proportions of passive observers. Furthermore, among all
reliability groups, the extreme-right domains have the highest AE level
overall (Fig. 4), indicating that tweets sharing far-right domains engage a
more actively engaged audience pool with a relatively smaller proportion of
passive lurkers.

Figure 3: Analysis of the hidden audience at the domain level. Each scatter
plot represents the relation between the number of unique sharers and the
fraction of impressions that are followed by an action (either retweet, reply,
like, or quote). Each cross represents a domain, and their color indicates
their reporting quality. Figure 4: Influence of political ideology on the
share of the hidden audience. Each subplot shows box plots representing the
distribution of the fraction of impression that provokes an action (retweet,
reply, like or quote). For each subplot, we group domains according to their
political leaning into seven groups, ranging from extreme right to extreme
left.

As the next step, we combine the political leaning of domains shared by users
and their detected positioning in the debate. The ideology distribution of
groups of users depending on the leaning of the domains they share is shown in
Fig. 5. Results highlight that support for the supply of weapons to Ukraine
tends to drop the more extreme the political leaning. However, there is still
an important distinction between extreme left and extreme right, as the former
does have a wider distribution, but the majority of its users are still in
favor of military aid, while the latter opposes such aid in its majority.

Figure 5: Distributions of users by ideology segmented by political leaning of
shared domains. The distributions are shown for regular users and influencers
who have shared at least a class of domains twice.

##  4 Discussion

This study presents a novel approach to investigating the interplay between
echo chambers, misinformation, and the share of users that consume content
without visible interactions. The distinctive aspect of this approach lies in
estimating the prominence of passive users, commonly referred to as lurkers,
who refrain from actively engaging with tweets through actions such as liking,
quoting, replying, or retweeting. Our work exploits the impression count, the
newly introduced tweet-level metric from Twitter API, to estimate the
proportion of the hidden audience that would be otherwise disregarded in
analysis, inspiring a novel angle for analysis of polarization and echo
chamber on Twitter, for which the previous analysis only take into account
users with visible actions.

To address the presence of lurkers within the echo chamber, we compare the
number of actions and impressions in the original tweets related to the Russo-
Ukrainian conflict. This analysis allows us to determine the proportion of
lurkers relative to active users and investigate their dependency on various
factors. Our findings reveal that Twitter actions constitute a smaller portion
compared to the total impressions, indicating that passive users account for a
significant share of consumers. Furthermore, this share is even greater for
actions that require active engagement, such as quoting. Notably, the main
driver that influences the share of passive consumers is the presence of far-
right and misinformation-spreading news sources. These contents exhibited the
highest ratio of actions per impression, suggesting the dependence of active
engagement on the type of content more than the ideological stance of the
producers.

Although this study provides an initial understanding of the impact of passive
users, it also raises several unresolved questions that warrant further
investigations. Firstly, exploring whether lurkers are present in other
debates of interest and social networks, such as Facebook or Reddit, would be
valuable. However, we must consider that these platforms do not offer the same
impression count metric yet. Secondly, the temporal variability of the action-
per-impression rate remains unclear, necessitating a detailed analysis of
lurker behavior over time. Thirdly, which types of media (e.g., URLs, videos,
images) elicit the highest level of active user engagement is yet to be
determined. Lastly, developing a minimal mechanistic model capable of
reproducing the observed data would be instrumental in comprehending the
underlying mechanisms driving content engagement. Furthermore, extending our
exploration of lurkers to other debates and networks is crucial to gain a
comprehensive understanding of their role in shaping online discourse.

In summary, this research makes a step forward by exploring the presence of
passive users, who constitute a relevant part of social network users.
Additionally, our findings underscore the heightened activity of users within
domains with low factual reporting or far-right ideologies. By acknowledging
the significance of lurkers and their relationship to echo chambers,
polarization and misinformation, this study contributes to a comprehensive
understanding of social network dynamics specifically related to the
amplification of polarization on social media. Furthermore, it opens new
avenues for future research and interventions aimed at addressing these
challenges.

## Acknowledgments

This work is the output of the Complexity72h workshop, held at the IFISC in
Palma, Spain, 26-30 June 2023. https://www.complexity72h.com

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## Supplementary Information

We report here the additional figures and analyses discussed in the main text.
Specifically, we show in Fig. S1 the probability density function of the
bivariate distribution of the opinion of users (influencers excluded) and the
average opinions of their neighbors to visualize the presence of echo chambers
in the analyzed retweet network. The heatmap shows two opposing communities
concentrated around the two extremes of the discussion, with different sizes.

Figure S1: Polarization and echo chambers in the interaction network. The
heatmap shows the users’ opinion with respect to the average opinion of their
neighbors; the colors represent the density of users: the lighter the color,
the greater the number of users in that area. The values are computed in
logarithmic scale in order to account for size differences between the two
subpopulations. The heatmap clearly shows the presence of two subpopulations
forming echo chambers, i.e., retweeting content posted by users with a similar
ideology to theirs, in the considered discussion and time period. The density
is computed on a logarithmic scale to account for such differences.

A similar analysis to the one shown in Fig. 1(b) can be found in the data
repository related to this project111https://osf.io/5m3vr/. This figure shows
the analysis conducted on all the 190 influencers of our dataset. The top
panel of the figure represents the probability distribution of ideologies
obtained using the latent ideology algorithm; the lower panel of the figure
represents the opinion distribution of the retweeters of all influencers in
the network. We hid the Twitter handles of the active users with fewer than
30,000 followers and labeled them as @Influencer$\\_n$. In the same data
repository, we also share the Twitter IDs of all the tweets used in this
study.