APAppendix References

# Linking Online Attention to Offline Opinions: [an iconic finding]

JooYoung Lee,1 Siqi Wu,3,1 Ali Mert Ertugrul,2 Yu-Ru Lin,2 Lexing Xie1

# Linking Cross-Platform Attention to Offline Opinions: When Do Promotions Not
Work?

JooYoung Lee,1 Siqi Wu,3,1 Ali Mert Ertugrul,2 Yu-Ru Lin,2 Lexing Xie1

# Linking Cross-Platform Attention to Offline Opinions: Do Groups Tweeting
More Get More Video Views?

JooYoung Lee,1 Siqi Wu,3,1 Ali Mert Ertugrul,2 Yu-Ru Lin,2 Lexing Xie1

# Linking Cross-Platform Attention to Offline Attitudes: The Two Tales of
Viral Content

JooYoung Lee,1 Siqi Wu,3,1 Ali Mert Ertugrul,2 Yu-Ru Lin,2 Lexing Xie1

# Linking Cross-Platform Online Attention to Offline Attitudes and Behaviors

JooYoung Lee,1 Siqi Wu,3,1 Ali Mert Ertugrul,2 Yu-Ru Lin,2 Lexing Xie1

# Measuring Cross-Platform Attention on Twitter and YouTube:
Case Studies on Abortion, Gun Control, and Black Lives Matter

JooYoung Lee,1 Siqi Wu,3,1 Ali Mert Ertugrul,2 Yu-Ru Lin,2 Lexing Xie1

# Measuring Cross-Platform Attention on Twitter and YouTube

JooYoung Lee,1 Siqi Wu,3,1 Ali Mert Ertugrul,2 Yu-Ru Lin,2 Lexing Xie1

# Measuring Cross-Platform Attention across Political Spectrum

JooYoung Lee,1 Siqi Wu,3,1 Ali Mert Ertugrul,2 Yu-Ru Lin,2 Lexing Xie1

# Measuring Cross-Platform Attention on Controversial Topics

JooYoung Lee,1 Siqi Wu,3,1 Ali Mert Ertugrul,2 Yu-Ru Lin,2 Lexing Xie1

# Measuring Attention Across YouTube and Twitter on Controversial Topics

JooYoung Lee,1 Siqi Wu,3,1 Ali Mert Ertugrul,2 Yu-Ru Lin,2 Lexing Xie1

# Whose Advantage? Measuring Attention Dynamics
across YouTube and Twitter on Controversial Topics

JooYoung Lee,1 Siqi Wu,3,1 Ali Mert Ertugrul,2 Yu-Ru Lin,2 Lexing Xie1

###### Abstract

The ideological asymmetries have been recently observed in contested online
spaces, where conservative voices seem to be relatively more pronounced even
though liberals are known to have the population advantage on digital
platforms. Most prior research, however, focused on either one single platform
or one single political topic. Whether an ideological group garners more
attention across platforms and/or topics, and how the attention dynamics
evolve over time, have not been explored. In this work, we present a
quantitative study that links collective attention across two social platforms
– YouTube and Twitter, centered on online activities surrounding popular
videos of three controversial political topics including Abortion, Gun
control, and Black Lives Matter over 16 months. We propose several sets of
video-centric metrics to characterize how online attention is accumulated for
different ideological groups. We find that neither side is on a winning
streak: left-leaning videos are overall more viewed, more engaging, but less
tweeted than right-leaning videos. The attention time series unfold quicker
for left-leaning videos, but span a longer time for right-leaning videos.
Network analysis on the early adopters and tweet cascades show that the
information diffusion for left-leaning videos tends to involve centralized
actors; while that for right-leaning videos starts earlier in the attention
lifecycle. In sum, our findings go beyond the static picture of ideological
asymmetries in digital spaces and provide a set of methods to quantify
attention dynamics across different social platforms.

##  1 Introduction

Several recent studies have documented the ideological asymmetries between the
left-wing and right-wing activism (Brady et al. 2019; Schradie 2019; Freelon,
Marwick, and Kreiss 2020; Waller and Anderson 2021). Some highlight the
dominance of conservative voices on social media (Brady et al. 2019); others
portray the widespread symbolic support for progressive social movements
(Jackson, Bailey, and Welles 2020). The term “conservative advantage” is
coined to describe the strategic dissemination of right-wing users to spread
their messages (Schradie 2019). However, most of the existing research bases
on the analysis of a single platform or a single political topic. Relatively
little is known about how different ideological groups garner attention across
platforms, and whether the group advantage of gaining visibility remains
across topics and over time. To answer these questions, this work designs
several sets of cross-platform measurements on the collective attention
dynamics of two different ideological groups across three controversial
political topics.

Online platforms, such as Twitter, YouTube, Reddit, and Facebook, are social-
technological artifacts that segregate online attention into silos defined by
the underlying software and hardware systems. Video views on YouTube are known
to be driven by discussions outside the platform (Rizoiu et al. 2017), and to
be part of users’ broader information diet (Hosseinmardi et al. 2021). What is
not known, however, is how groups of related content comparatively evolve
across different social platforms. Collective attention on political content
have been studied on one topic, such as the Occupy Movement (Thorson et al.
2013), Gun Control/Rights (Zhang et al. 2019), and Black Lives Matters (De
Choudhury et al. 2016; Stewart et al. 2017). Yet, cross-cutting studies that
compare different movements are rare. With data from three long-running
controversial topics, this work seeks to provide measures across YouTube and
Twitter and paint a nuanced picture about the temporal patterns of attention
from left to right.

Figure 1:  Attention time series (top: daily view count; bottom: daily tweet
count) related to BLM throughout 2017. The view count dynamics of both left-
and right-leaning videos have a handful of sharp peaks, while the tweet count
dynamics of right-leaning videos peak more frequently. Notable video releases
(with the most views and tweets) are labeled. YouTube channel titles are
italicized and colored by video leanings. Best viewed in colors.

We choose three topics: Abortion, Gun Control, and Black Lives Matter (BLM).
We rely on video hyperlinks to connect the content from YouTube to Twitter. A
motivating example is given in Figure 1. We plot the time series of daily view
count for the collected BLM videos from YouTube (top panel) and daily volume
of tweets mentioning these BLM videos from Twitter (bottom panel). Both time
series are further disaggregated by video uploaders’ political leanings.
Visually, the view count dynamics of both left- and right-leaning videos are
relatively stable in year 2017, except a sharp spike caused by the “Unite the
Right rally”111https://en.wikipedia.org/wiki/Unite˙the˙Right˙rally event in
Charlottesville, USA. On the bottom panel, the tweet count dynamics of right-
leaning videos have many spikes, which can be attributed to the upload of new
videos from far-right YouTube political commentators. The measures on YouTube
and Twitter present a contrasting story here: if we focus on the two weeks
period after the rally, left-leaning videos attracted more attention on
YouTube (measured by views, left: 27.2M, right: 13.9M) while right-leaning
videos had higher exposure on Twitter (measured by tweets, left: 37.5K, right:
52.3K). This example demonstrates the need for cross-platform analysis –
findings on one platform may not generalize to another.

We design a set of metrics from publicly available data on YouTube and
Twitter, which include total views, video watch engagement, tweet reactions,
the evolution of attention over time, and early adopter networks among tweets
and Twitter users. On YouTube, we find that left-leaning videos accumulate
more views, are more engaging, and have higher viral potential than right-
leaning videos. In contrast, right-leaning videos have higher numbers of total
tweets and retweets on Twitter. Statistics on the unfolding speed for views
and tweets show that the attention on left-leaning videos attenuates faster,
while that on right-leaning videos persists for longer. Note that these
observations are not generalized unanimously across topics, e.g., for some
metrics, we observe significant differences for Abortion and Gun Control, but
not for BLM. These findings expand current wisdom on ideological asymmetries
in two ways: the first is exposing the novel facet that left-leaning content
attracts more attention in a shorter period of time; the second is the need of
contrasting temporal attention statistics between platforms, such as right-
leaning tweet cascades tend to start earlier and YouTube views on right-
leaning content sustain longer. In sum, our observations paint a richer
picture of attention patterns across the political spectrum, provide a basis
for further studying political framing and group behavior, and supply
fundamental metrics for understanding influences that transcend platforms.

The main contributions of this work include:

  * •

a data curation procedure linking content on YouTube and Twitter for
longitudinal topic monitoring.222Our datasets and analysis code are publicly
available at https://github.com/picsolab/Measuring-Online-Information-
Campaigns

  * •

several sets of cross-platform metrics that support statistical comparisons
for different ideological groups, encompassing the volume and quality of
attention, networks of tweets and users, as well as relative temporal
evolution.

  * •

adding the temporal and cross-platform dimensions to recent observations on
ideological asymmetries. We find that polarized content engages users in
distinct ways – more views, more engagement, and faster reactions for videos
on the left; comparing to more tweets, more sustained attention for videos on
the right.

##  2 Related Work

#### Online behavior of political groups.

Measurement studies have quantified different aspects of users, contents, and
their interactions under political polarization on social media. Conover et
al. (2011) presented one of the first profiling studies of polarized political
groups on Twitter. There have also been evidences that liberal and
conservative groups attract online attention in different manners. Abisheva et
al. (2014) focused on a set of influential Twitter users who promoted YouTube
videos, and they found that conservatives tweeted more diverse topics than
liberals and that conservatives shared new videos faster. Bakshy, Messing, and
Adamic (2015) quantified the extent to which Facebook users were exposed to
politically opposing contents, and they found that conservatives tended to
seek out more cross-partisan content. Lin and Chung (2020) distinguished
online behavioral signals, such as linguistic and narrative characteristics,
of two ideology groups in response to mass shooting events. Garimella et al.
(2018) defined several consumption and production metrics and profiled key
user behavior patterns. Ottoni et al. (2018) showed that conservatives used
more specific language to discuss political topics and showed more negative
emotions in the language. On YouTube, a recent study from Wu and Resnick
(2021) found that left-leaning videos attracted more comments from
conservatives than right-leaning videos from liberals. However, all of these
works are conducted platform-wide, and are not specialized into particular
topics or movements.

Online activism, also known as online social movement has been actively
studied as a form of digital political campaigns. For example, De Choudhury et
al. (2016) presented one of the first studies on the BLM movement, measuring
geographical differences in participation, and relationships to offline
protests. Stewart et al. (2017) constructed a shared audience network of users
who talked about BLM on Twitter and found the existence of superclusters among
liberals and conservatives. Zhang and Counts (2015) performed policy decision
prediction based on tweet texts analysis on same-sex marriage. In a follow-up
work, Zhang and Counts (2016) discussed gender disparity by linking tweet
texts to the state-level Abortion policy events. Ertugrul et al. (2019)
examined the relation between offline protest events and their social and
geographical contexts. Freelon, Marwick, and Kreiss (2020) explained different
tactics of liberals and conservatives when approaching audience on social
media and articulated the asymmetries of measured behavior between
conservatives and liberals. However, all of these works focus on a single
controversial topic. In contrast, we examine three political topics and assess
consistency of findings across the topics.

#### Cross-platform measurement studies.

One early attempt in linking Twitter and YouTube data is from Abisheva et al.
(2014), in which the authors found that the features of early adopters on
Twitter were predictive for the video view counts on YouTube. Rizoiu et al.
(2017) proposed the Hawkes Intensity Process that linked the time series of
tweets and views, which led to a metric called viral potential for measuring
the expected number of views that a video would obtain if mentioned by an
average tweet (Rizoiu and Xie 2017). Zannettou et al. (2017) measured the
sharing of alternative and mainstream news articles on three different
platforms – Twitter, Reddit, and 4chan. This seminal study characterized the
role of fringe communities in spreading news. Hosseinmardi et al. (2021) used
browsing histories to infer video watch behavior, and quantified the
consumption and driver of extreme content with respect to users’ information
diet. However, all of these works cover a breadth of content sources and
categories, but none is focused around social movements or consistent
political topics.

This presented work bridges the gap of cross-platform measurement studies on
multiple social movements, aiming to provide richer understanding that
balances the ideological asymmetry between the left and the right.

##  3 Curating Tweeted Video Datasets

We constructed three new cross-platform datasets by tracking videos on YouTube
and posts on Twitter over three controversial topics: Abortion, Gun Control,
and BLM. Those topics have been studied extensively by social and political
scientists (Zhang and Counts 2016; De Choudhury et al. 2016; Stewart et al.
2017; Garimella et al. 2018). In this section, we first describe the data
collection strategy. We then introduce our methods for estimating the
political leanings of Twitter users and YouTube videos. Table 1 summarizes the
overall statistics of the three topical datasets.

| YouTube video | Tweet | User | 20% Early Adopters (Twitter user) | (Tweet)
---|---|---|---|---|---
| L | C | R | Total | Total | Total | Lib. | Neu. | Con. | Total | Total
Abortion | 58 | 10 | 111 | 179 | 106,776 | 76,337 | 2,534 | 1,708 | 8,202 | 12,444 | 15,843
Gun Control | 81 | 33 | 154 | 268 | 270,543 | 156,145 | 10,987 | 3,371 | 13,591 | 27,949 | 37,264
BLM | 297 | 84 | 396 | 777 | 593,574 | 262,580 | 10,419 | 5,745 | 33,934 | 50,098 | 78,969
Table 1: Summary of cross-platform datasets on three controversial topics. L:
Left-leaning video; C: Center video; R: Right-leaning video. Lib.: Liberal
user; Neu.: Neutral user; Con.: Conservative user. See Section A of (Appendix
2022) for methods of curating tweeted video datasets across YouTube and
Twitter, and Section B&C for methods of estimating the political leanings of
Twitter users and YouTube videos. The ratios of early adopters are slightly
below 20% due to banned and protected users.

###  3.1 Finding YouTube Videos and Twitter Posts of Controversial Topics

We are interested in topical YouTube videos and the discussions about them on
Twitter. Following the approach used in (Wu, Rizoiu, and Xie 2018), we
collected public tweets that mentioned any YouTube URLs via the Twitter
filtered streaming API. Our raw Twitter stream spanned 16 months (2017-01-01
to 2018-04-30), and contained more than 1.8 billion tweets. To subsample
videos that attracted a reasonable amount of attention before the end of the
observation period, we required that the videos must be published in 2017,
receive at least 100 tweets and at least 100 views within the first 120 days
after upload. We make this filtering choice because (a) analyzing videos with
little attention (${<}1$ view per day) is bound to generate noises when
comparing different groups; (b) characterizing the timing and structure of a
video’s tweeting cascades requires a non-trivial number of tweets. This
yielded 328,557 videos, which were mentioned in 242M tweets by 29.9M users.
For each video, we collected its metadata, daily time series of view count and
watch time, and all tweets mentioning it.

To identify topic-relevant videos and tweets, we first curated three separate
keyword lists for three controversial topics – Abortion, Gun Control, and BLM.
We consider a video is potentially relevant if (a) it contains at least one
keyword in the video title or description; or (b) it is mentioned in a tweet
that contains at least one keyword in the tweet text. Next, we used a mix of
manual and semi-automated approaches to annotate the potentially relevant
videos. Section A of (Appendix 2022) details the topical keywords, their
curation, and our video annotation protocol. In total, we obtained 179
Abortion, 268 Gun Control, and 777 BLM videos, which were mentioned in 970K+
tweets.

Figure 2:  Time series of daily view count (top panel, in log scale) and tweet
cascades (bottom panel) for an example video (YouTube video id: PTjMngQwGh8).
A tweet cascade is placed at the time of its first tweet, with y-axis and size
proportional to the number of tweets in the cascade (see Section 4.3). The
viewing dynamic unfolds for months, but tweets only happen sporadically after
the first week.

Figure 2 shows the daily view count series and tweet cascades for an example
video. A notable contrast between the two sources is that YouTube attention
data is only available in daily aggregates, i.e., without individual user
logs, while tweets have precise timestamp, user information, and relations
between tweets and users. Section 4 is dedicated to designing measures for
such cross-platform multi-relational temporal data. We bootstrapped the
political information about Twitter users and YouTube videos. In particular,
we gathered more information about videos’ early adopters, defined as the
first 20% users who tweeted about each video. We collected the follower lists
for all early adopters. Network measures of early adopters are found to
indicate future popularity (Romero, Tan, and Ugander 2013). The threshold
(first 20%) is chosen to balance the need for data and the burden of
collecting network information within practical API limits. After filtering
out banned and protected users, we extracted 132K early adopter tweets posted
by tens of thousands of users across the three topics (see Table 1).

###  3.2 Estimating Political Leanings of Twitter Users and YouTube Videos

We classified the political leanings of early adopters on Twitter into
liberal, neutral, and conservative. Meanwhile, we classified the video
leanings into left, center, and right. Twitter users and YouTube videos are
related in that left-leaning contents (e.g., Gun Control videos) are generally
shared by liberal users while right-leaning contents (e.g., Gun Rights videos)
are shared by conservative users.

We estimated Twitter users’ political leanings by first identifying a group of
seed users who included political hashtags in their profile descriptions, and
then by using a label propagation algorithm (Zhou et al. 2004) to propagate
the labels of seed users to other users based on the shared follower network.
This is a common approach for classifying user leaning on Twitter (Stewart et
al. 2017) and the follow relation is found to be the most important in
predicting user ideology (Xiao et al. 2020). We performed 10-fold cross-
validation to evaluate the classification performance. We observed very high
scores in precision, recall, and F-score across all three topics ($>95\%$ in
all metrics). Section B of (Appendix 2022) describes our classification and
evaluation methods for Twitter users in more detail.

We estimated YouTube videos’ political leanings by first averaging the leaning
scores of videos’ early adopters on Twitter. We then used an external YouTube
media bias dataset (Ledwich and Zaitsev 2020) to label the video leanings and
identified optimal classification thresholds. We were able to find 58/10/111
left, center, and right-leaning videos for Abortion (analogously, 81/33/154
for Gun Control and 297/84/396 for BLM). To validate our estimation, we
performed one round of manual annotation for videos in Gun Control. We used
stratified sampling to sample 50 videos based on the video leaning scores.
These videos were annotated independently by three authors. The Fleiss’ Kappa
was 0.69, suggesting a moderate inter-rater agreement. Section C of (Appendix
2022) details our classification and evaluation methods for YouTube videos.

##  4 Measures for Cross-Platform Attention

This section designs several sets of metrics for the cross-platform data, in
order to compare content across different political ideologies, and examine
whether the differences are consistent across topics, across platforms, and
over time.

###  4.1 Aggregate Attention on YouTube and Twitter

We present four metrics for the total video attention on YouTube.

Total view count sums up a video’s view count time series until day 120.

Relative engagement is a metric proposed in (Wu, Rizoiu, and Xie 2018) for
quantifying the average video watching behavior. Specifically, for each video,
we first compute average watch percentage, defined as the total watch time
divided by total number of views (both at 120 days) and then normalized by the
video length (in seconds). The relative engagement score is the percentile
ranking of average watch percentage among videos of similar lengths. It is a
normalized score between 0 and 1. A higher score means more engaging, e.g., a
score of 0.8 suggests that this video is on average watched for longer time
than 80% videos of similar length. Note that relative engagement is shown to
be stable over time, hence there is no need to examine the temporal variations
of watch time, as it would strongly correlate with view counts. In this work,
relative engagement is computed based on a publicly available collection of
5.3M YouTube videos (Wu, Rizoiu, and Xie 2018), with details described in
Section D of (Appendix 2022).

Fraction of likes measures the video reaction – provided by YouTube as the
total counts of likes and dislikes and collected via the thumb-up and thumb-
down icon on the video page. A relatively lower fraction of likes indicates a
more diverse audience reaction to the video content. Note that the majority of
videos receive a lot more likes than dislikes.333YouTube announced that the
dislike count will no longer be available to the public on Nov 10, 2021.
https://blog.youtube/news-and-events/update-to-youtube/

Viral potential is a positive number, representing the expected number of
views that a YouTube video will obtain if mentioned by an average tweet on
Twitter (Rizoiu and Xie 2017). More specifically, it is the area under the
impulse response function of an integral equation known as Hawkes Intensity
Process (HIP) (Rizoiu et al. 2017), which is learned for each video by using
the first 120 days of tweeting and viewing history. We choose this quantity
rather than simply dividing the number of views by the number of tweets,
because the model takes into account views that are yet to unfold due to its
sustained circulation via sharing and tweeting. A self-contained summary about
HIP and viral potential computation is given in Section E of (Appendix 2022).

On Twitter, tweets can be categorized into four types: original tweets,
retweets, quotes, and replies. This leads to five counting metrics: the total
number of tweets, original tweets, retweets, quoted tweets, and replies.

###  4.2 Views and Tweets over Time

Viewing half-life is computed as the number of days to achieve half of its
total views at day 120.

Tweeting half-life is computed as the number of days to achieve half of its
total tweets at day 120.

Tweeting lifetime is time gap between the first and the last tweets. We do not
measure lifetime on viewing because the view count of a video rarely becomes
zero even towards the end of the measurement period, but tweets tend to
exhaust much sooner.

Tweeting inter-arrival time is the average time difference between every two
consecutive tweets about each video.

Accumulation of views and tweets. In addition to the summary metrics above, we
also compare the attention accumulation on the left- and right-leaning content
on a daily basis. On each day $t$, we compute the fraction of the total views
that each video has achieved. This leads to two sets of samples
$\\{v^{(L)}_{i}\\}_{i=1}^{n}$ and $\\{v^{(R)}_{j}\\}_{j=1}^{m}$, where $n$ is
the number of left-leaning videos and $m$ is the number of right-leaning
videos. We then compute the normalized Mann-Whitney U (MWU) statistic (Mann
and Whitney 1947),

$\bar{U}_{t}=\frac{1}{nm}\sum_{i}\sum_{j}\\{I[v_{i}^{(L)}>v^{(R)}_{j}]+0.5\,I[v_{i}^{(L)}=v^{(R)}_{j}]\\}$

Here $I[\cdot]$ is the indicator function that takes value 1 when the argument
is true, 0 otherwise. The U statistic intuitively corresponds to the fraction
of sample pairs $(v^{(L)}_{i},v^{(R)}_{j})$ where the sample from left-leaning
distribution is larger, accounting for ties. If the distributions of $v^{(L)}$
and $v^{(R)}$ are indistinguishable, then $\bar{U}$ would be around 0.5. We
compute the statistic $\bar{U}_{t}$ on tweets in the same fashion, and both
statistics are computed for each day. These two series of statistics allows us
to quantify the differences between left- and right-leaning content, and
compare the trends on the accumulation of views and tweets over time.

###  4.3 Videos’ Tweet Cascades

Cascade size. We define that a cascade consists of a root tweet and all of its
retweets, replies, and quotes. It is well-known that the vast majority of
cascades in online diffusion networks are very small and only a very small
fraction of cascades would become very big (Goel, Watts, and Goldstein 2012).
Based on the number of tweets in a cascade, we divide the cascades into
isolated (only root tweet), small (2-4 tweets), and large ($\geq 5$ tweets)
groups. For videos of each leaning on each topic, we compute the fractions of
isolated/small/large cascades and the fraction of tweets in each cascade
group. These metrics quantify the structure of online diffusion and allow us
to compare behavior on controversial political topics with what was known
about tweeted videos in general.

Cascade start time is the percentage of accumulated views of the video when
the root tweet of the cascade is posted. It measures how much view attention
is accumulated on YouTube before the infusion on Twitter starts. We choose to
describe cascade timing relative to the accumulation of view, rather than in
absolute number of days since upload, because (a) such relative time more
directly correlates the amount of cascades with respect to the views they can
potentially drive (rather than through another variable, days); and (b) the
percentage of views provides more granularity, since many videos have all
views and tweets unfold within a few days after upload.

###  4.4 Networks among Early Adopters on Twitter

For each video, we obtain its follower network among the early adopters. If
there exists a following relationship between a pair of users, a directed edge
is established. This results in one network for each shared video. We compute
a set of metrics per video, and then compare their distributions on each topic
for left- and right-leaning videos. We describe two key metrics here, and
discuss four additional metrics in Section H of (Appendix 2022).

Gini coefficient of indegree centrality. We calculate the indegree centrality
for each node in the network. To have a video-level metric, we use the Gini
coefficient, which ranges from 0 to 1 and measures the distribution
inequality. Specifically, the Gini coefficient of indegree centrality
quantifies the degree of inequality of the indegree distribution. A higher
value indicates that a few early adopters are followed more by other early
adopters, and a lower value indicates that the indegree distribution is more
equal.

Gini coefficient of closeness centrality captures the dispersion in inverse of
average shortest path length from one early adopter to all other early
adopters of a given video. Higher coefficient implies that a few early
adopters can reach the rest of the early adopters within a few hops.

##  5 Observations on Cross-Platform Attention

We report the results on all metrics described in Section 4, in mirroring
subsections to aid navigation. Many results in this section are presented as
violin plots. The outlines are kernel density estimates for the left-leaning
(blue) and right-leaning (red) videos, respectively. The center dashed line is
the median, whereas the two outer lines denote the inter-quartile range. To
compare the distributions of each metric for the left- and right-leaning
videos, we adopt the one-sided Mann–Whitney U test. We summarize our results
in Table 2 at the end of this paper.

###  5.1 Aggregate Attention on YouTube and Twitter

Total view count. Figure 3(a) shows the distribution of video views at day 120
after upload. Using the view count at the same day removes the effects of
video age, so that the videos published for longer time are not taking an
unfair advantage. In Abortion and Gun Control, the median, as well as 25th and
75th percentile of views of left-leaning videos are higher than that of right-
leaning videos. The median views for left-leaning videos are 107,346 for
Abortion and 153,482 for Gun Control, versus 62,780 and 103,373 for right-
leaning ones. The differences in view distribution are statistically
significant ($p<0.01$, Table 2 row 1). For BLM, right-leaning videos have
higher median and 75th of views, but the effect is not significant.

Figure 3:  Violin plots comparing the left-leaning (blue) and right-leaning
(red) videos in (a) total number of views at 120 days after upload, (b)
relative engagement, (c) fraction of likes, (d) viral potential, (e) total
number of tweets, and (f) total number of retweets at 120 days after the video
upload. Left-leaning videos are more viewed, more engaging, having more
diverse reactions, and more viral than right-leaning videos across all three
topics (except views in BLM). In Abortion and BLM, right-leaning videos are
significantly more tweeted, especially with more retweets.

Relative engagement. From Figure 3(b), we can see that videos in all three
topics are highly engaging, with mean relative engagement at 0.834 for
Abortion, 0.824 for Gun Control, and 0.831 for BLM. This is because our data
processing procedure requires videos to have at least 100 tweets and 100
views, which tends to select videos with significant amount of interests.
Left-leaning videos are significantly more engaging than their right-leaning
counterparts across all three topics ($p<0.05$, Table 2 row 2).

Fraction of likes. Figure 3(c) presents the proportion of likes in videos’
reactions. Left-leaning videos across all topics have significantly smaller
fraction of likes than right-leaning videos ($p<0.001$, Table 2 row 3). This
may be explained by the observation that there are far more cross-partisan
talks on left-leaning videos (Wu and Resnick 2021).

Viral potential. Figure 3(d) shows the distributions of viral potential. We
find that the left-leaning videos have significantly higher viral scores than
the right-leaning videos across all three topics ($p<0.05$, Table 2 row 4),
meaning that given the same amount of tweets exposing the video on Twitter, an
average left-leaning video can effectively attracts more views than an average
right-leaning video. The difference is most notable in Abortion: a typical
left-leaning video receives 224 views from an average tweet, whereas a typical
right-leaning video receives only 63 views.

Tweet counts. Figure 3(e) and (f) show the distributions of total tweets and
retweets. Contrasting to the observation that left-leaning videos are more
viewed, here we find that right-leaning videos are significantly more tweeted,
especially with more retweets and more replies ($p<0.001$, Table 2 row 5-7) in
Abortion and BLM. On the other hand, we do not observe a significant
difference in original tweets and quotes, except for BLM where right-leaning
videos have prevailing volume across all tweet types.

To examine the robustness of presented results in this section, we
bootstrapped videos for each topic and for each ideological group.
Specifically, for each group, we created $1,000$ bootstrapped sets of videos
that are of the same size as the original group (shown in Table 1). Next, we
computed the mean of proposed metrics (shown in Figure 3) for each
bootstrapped set. Lastly, we used the independent t-test to check the
statistical significance between left- and right-leaning groups. The results
of the t-tests support all reported relations in Table 2 row 1-6 with
$p<0.001$.

###  5.2 Views and Tweets over Time

We measure how quickly left- and right-leaning videos attract views and
tweets. We find that left-leaning videos are reacted on YouTube and Twitter
quicker across all topics.

Viewing half-life and Tweeting half-life. We notice that there are significant
differences in the attention consumption patterns: right-leaning videos have
more prolonged attention spans on YouTube across all topics ($p<0.01$, Table 2
row 10). Right-leaning videos also have longer attention spans on Twitter for
Abortion and Gun Control ($p<0.05$, Table 2 row 11). For example, Figure 4(a)
shows that right-leaning videos for Abortion have the longest attention span –
taking 9 days for 75% videos to achieve viewing half-life, while left-leaning
videos only take 3 days. Comparing Figure 4(a) to Figure 4(b), we find that
attention spans on Twitter are shorter than that on YouTube. In Abortion,
left-leaning videos take 2 days for 75% of videos to reach tweeting half-life
(vs. 3 days for views) and right-leaning videos take 5 days for 75% of videos
to reach tweeting half-life (vs. 9 days for views).

Figure 4: The number of days to receive 50% (a) views and (b) tweets. The time
gaps (in minutes) between tweets measured as (c) tweeting lifetime and (d)
tweeting inter-arrival time. Left-leaning videos are mentioned more quickly on
Twitter and have shorter circulation duration for all metrics in Abortion and
Gun Control.

Tweeting lifetime and Tweeting inter-arrival time. Figure 4 (c) and (d) shows
the distributions of tweeting lifetime and inter-arrival time. The results are
mixed across topics. For Abortion and Gun Control, the MWU test results show
that left-leaning videos have significantly less scores in both metrics than
right-leaning videos ($p<0.05$, Table 2 row 12-13). But for BLM, both metrics
show similar distributions between left- and right-leaning videos.

These results suggest that left-leaning videos have shorter circulation
duration and are mentioned more quickly (except BLM). The most notable
difference is in Abortion where the median of tweeting lifetime and inter-
arrival time for right-leaning videos are more than three times of those for
left-leaning videos. For instance, the median tweeting lifetime is 1811.6
minutes for left-leaning videos, while the median is 8453.8 minutes for right-
leaning videos.

Accumulation of views and tweets. We examine how much views and tweets are
accumulated each day. Figure 5(a) and (b) show the Complementary Cumulative
Distribution Function (CCDF) of views and tweets percentages accumulated for
the first day (video published date) and $30^{th}$ day for Abortion videos. We
observe that left-leaning videos tend to achieve views/tweets faster than
right-leaning videos, which is consistent with Figure 4. For example, after
day 1, 56.9% left-leaning videos have achieved viewing half-life, but only
26.1% right-leaning videos achieved the same. For tweet accumulation, after 1
day, the gap of tweeting half-life between left- and right- leaning videos is
11.4% (46.6% and 35.1%, respectively). By day 30, only 3 left-leaning videos
have not accumulated 80% of views.

Figure 5(c) compares the normalized MWU statistic values of left- and right-
leaning videos in views $\bar{U}_{v}$ and tweets $\bar{U}_{t}$ on each of the
120 days since upload. It shows that the difference between left- and right-
leaning videos is larger in the beginning and decreases towards 0.5 over time.
At the $120^{th}$ day (as the observation period ends), both will be 0.5 by
definition, we thus truncate the plot at day 30. We also observe that the
differences in views is larger than that of tweets across left- and right-
leaning videos initially, whereas the discrepancy between views and tweets
narrows as videos get older. This is because more videos have already fully
achieved all the views and tweets.

Figure 5: The distribution of accumulated views and tweets in Abortion videos
on the first day of video upload and at $30^{th}$ day: (a) percentage of views
achieved, (b) percentage of tweets achieved, (c) MWU statistic values for each
day from the first day to day 30. The area marked with (1,2,3,4) in (a) and
(b) are shown in (c) as a normalized MWU statistic. For example, (1) shows
difference between left- and right-leaning videos in terms of view
accumulation. This difference is measured using MWU test and shown in (c)
where (1) is marked. The difference between left- and right-leaning videos, in
both views and tweets accumulation, tend to become smaller in later days.
Section F of (Appendix 2022) includes the same set of plots for Gun Control
and BLM.

###  5.3 Videos’ Tweet Cascades

Cascade size. Goel, Watts, and Goldstein (2012) found that one-node-cascades
(isolated cascades) account for $96\%$ of all cascades in their Twitter Videos
dataset. Figure 6(a) shows that the proportions of isolated cascades in our
datasets are lower, measured at $91.5\%$, $91.2\%$, and $91.9\%$ respectively
for Abortion, Gun Control, and BLM. Notwithstanding a confounder that the
dynamics on Twitter has changed significantly since (Goel, Watts, and
Goldstein 2012), this may still suggest that tweets on controversial topics
are less isolated than tweets of videos about any topics. Figure 6(b) shows
the volume of tweets involved in each cascade group. It is interesting to find
that most tweets belong to either isolated cascades or large cascades.
Aggregated over left and right-leaning videos in Abortion, Gun Control, and
BLM, 47%, 43.4%, and 47.3% of tweets are isolated, whereas 44.7%, 48.4%, and
44.3% are in large cascades of size 5 and above, dominated by a handful of
cascades over 1,000 tweets.

Figure 6:  Comparing the left- and right-leaning videos in (a) proportions of
cascade sizes – isolated (1 tweet), small (2-4 tweets) and large (5+ tweets);
(b) volume of tweets in each cascade type. (c) and (d) show the density of
cascade start time in relation to accumulated view percentage for Abortion
videos. Because the left-leaning videos accumulate views quicker (90% of left-
leaning videos reach viewing half-life within 3 days after upload while 59.4%
of right-leaning videos do), more left-leaning tweet cascades are shown to
start after viewing half-life. Section G of (Appendix 2022) includes the same
set of plots for Gun Control and BLM.

Cascade start time. We compare when tweet cascades start in the process of
view accumulation, grouped by different cascade sizes. Figure 6(c) and (d)
show the distribution of cascade start times of left- and right-leaning videos
in Abortion relative to percentages of views. For right-leaning videos, there
is a peak of isolated cascades started at the end of the videos’ viewing
lifetime. We also observe that for left-leaning videos, the peaks for
isolated, small and large cascades are concentrated near $70\%$ of view
accumulation while peaks for right-leaning videos are more distributed over
different stages of view accumulation. Moreover, in all topics, more right-
leaning tweet cascades start before viewing half-life regardless of cascades
size. For example, 41% of right-leaning isolated cascades started before
viewing half-life while 25% of left-leaning isolated cascades started before
viewing half-life in Abortion. This is consistent with the observation that
views of right-leaning videos unfold much slower (See Figure 4(a) and Figure
5(a)), allowing tweet cascades to start at earlier stages of view accumulation
process. The difference in cascade start time is significant between left- and
right-leaning videos in all topics for isolated and small cascades ($p<0.001$,
Table 2 row 14-15) and is significant for Abortion and Gun Control for large
cascades ($p<0.001$, Table 2 row 16).

###  5.4 Networks among Early Adopters on Twitter

(a) aeFjqdiVAuY (Left-leaning)

(b) w5zN1Af7JXQ (Right-leaning)

Figure 7: Follower networks of early adopters for (a) a left-leaning and (b) a
right-leaning video with similar network size in Abortion. We compare the
left- and right-leaning videos in (c) Gini coefficient of indegree centrality,
and (d) Gini coefficient of closeness centrality.

Figure 7(c) and (d) show the distributions of the Gini coefficient of indegree
centrality and the Gini coefficient of closeness centrality. Gini coef. of
indegree centrality of left-leaning videos’ networks is significantly larger
in Gun Control and BLM ($p<0.001$, Table 2 row 17). For Gini coefficient of
closeness centrality, the MWU test results indicate that left-leaning videos’
networks have significantly greater Gini index than those of right-leaning
videos’ networks across all topics ($p<0.05$, Table 2 row 18).

This suggests that the networks of early adopters for left-leaning videos have
more users serving as hubs, i.e., who are followed by more early adopters and
have shorter path to other early adopters. This also suggests that in the
networks of early adopters for right-leaning videos, users are more equally
facilitating dissemination of political information which is consistent with
the findings shown in (Conover et al. 2012). As an example of this, we present
the follower networks of early adopters of one left-leaning video and one
right-leaning video in Abortion in Figure 7(a,b). To have a fair comparison we
sample two videos having similar network size (57 and 59 for left and right-
leaning videos, respectively). The left-leaning video has Gini coef. of
indegree centrality: 0.918, Gini coef. of closeness centrality: 0.90. The
right-leaning video has Gini coef. of indegree centrality: 0.748, Gini coef.
of closeness centrality: 0.536. It can be observed that the sharing of this
left-leaning video relies more on central users who are followed by more early
adopters and have shorter path to others. On the other hand, in the follower
network of the early adopters of this right-leaning video, indegree and
closeness centrality distributions are more equal.

Apart from the reported metrics, we have also performed preliminary
examination on the correlation and trends between two and more metrics. An
example on linking relative engagement to the view and tweet counts is
presented in Section I of (Appendix 2022). We have not seen consistent and
salient patterns that are not already captured by individual measures.

##  6 Conclusion and Discussion

This work presents a quantitative study that links collective attention
towards online videos across YouTube and Twitter over three political topics:
Abortion, Gun Control, and BLM. For each topic, we curated a cross-platform
datatset that contained hundreds of videos and hundreds of thousands of tweets
spanning 16 months. The extracted videos all have a non-trivial amount of
views and tweets. The key contributions include several sets of video-centric
metrics for comparing attention consumption patterns between left-leaning and
right-leaning videos across two platforms. We find that left-leaning videos
are more viewed and more engaging, while right-leaning videos are more tweeted
and have longer attention spans. We also found that the follower networks of
early adopters on left-leaning videos are of higher centrality, whereas tweet
cascades for right-leaning videos start earlier in the attention lifecycle.
This study enriches the current understanding of ideological asymmetries by
adding a set of temporal and cross-platform analyses.

Limitations. Extensive discussions about social data biases are presented in
(Olteanu et al. 2019). The biases can be introduced due to the choice of
social platforms, data (un)availability, sampling methods, etc. Here we
discuss three limitations in our data collection process.

A recent study found that Twitter filtered streaming API subsamples high-
volume data streams that consist of more than 1% of all tweets (Wu, Rizoiu,
and Xie 2020). The authors proposed a method of using Twitter rate limit
messages to quantify the data loss. Based on this method, we find our 16-month
Twitter stream has a sampling rate of 79.4 % – we collected 1,802,230,572 out
of 2,270,223,254 estimated total tweets. Under a Bernoulli process assumption,
the chance of collecting a video tweeted more than once in our tweet stream is
95.8%. Since most missing videos are tweeted sporadically, the sampling loss
from Twitter APIs is small, which minimally affects the measures on tweeting
activities, including attention volumes, timing, and cascade sizes. Confidence
intervals for simple measures such as volume can be derived (Wu, Rizoiu, and
Xie 2020).

In this paper, we present various measurements focused on YouTube videos,
which are the main entities that link the two platforms. YouTube viewers are
unknowable (via publicly available data) and Twitter users are hard to track
consistently over time. Therefore, we track videos which attract views and
tweets. All the presented metrics are video-centric and we do not assume that
the viewers or tweeters of the videos represent specific groups of users. We
believe that each set of videos (Abortion, Gun Control, and BLM) represent
YouTube videos that are relevant to the topic, curated by keyword queries and
semi-manual coding. The number of videos belonging to each topic is not large
but we attempted to include all relevant videos shared on Twitter which have
non-trivial activities. Thus our results intend to explain attention gathering
behavior of topic-relevant videos. Nevertheless, it is unclear that our
observations about ideological asymmetries can generalize to videos with less
attention and/or videos about other topics. We leave this generalization
validation as future work.

One data integrity limitation is the time gap between tweet collection in
2017–2018 and early adopters’ follower networks collected in early 2020. Our
data collection is limited by the Twitter search API quota, which restrains
collecting tweets and Twitter user followers simultaneously. The tweeted
videos stream has on average 3.7M tweets in each day. Collecting the follower
network for all these tweets far exceeds the capacity of Twitter API, focusing
on the early-adopter network is a practical trade-off between still having
informative results and making data collection feasible. A related issue comes
from unavailable YouTube videos and Twitter users since content publicly
available in 2017 may be deleted, banned, or protected in 2020. We found that
between 17% to 19% candidate videos become unavailable in our dataset.

Practical implications and future work. We believe this work adds a new
dimension to the understanding of online political behavior and discourse –
cross platform links. Further examination in this direction could bear
theoretical and empirical fruits. The measurements presented in this work are
mostly quantitative. One direction of future work is to complement qualitative
analysis. For example, to gain deeper insight into our observations about how
the user attention to left-leaning YouTube videos was driven by a group of
elite early adopters, one can examine typical diffusion networks from both
left- and right-leaning groups and study the diffusion process of the video
spreading. One could also examine the framing of left- and right-leaning
content in both video descriptions and tweets about them. For example, Lin and
Chung (2020) used mixed-methods approaches to identify the primary framing and
rhetorics in online conversations related to gun control, which can be
expanded to enrich the quantitative analyses, such as investigating the
linguistic features of YouTube descriptions and tweet cascades, and their
relationships to the changes in collective attitudes. Finally, understanding
the collective attention across multiple social platforms is important for
content producers, who could devise better strategies in promoting their
content in another domain.

## Ethical Statement

All data that we obtained was publicly available at the time of data
collection. We discarded deleted, protected, and private content at the time
of analysis. In our released dataset, we anonymized user identities.
Therefore, the analyses reported in this work do not compromise any user
privacy.

row | crossref | metric |  definition | significance
---|---|---|---|---
|  |  |  | Abortion | Gun Control | BLM
YouTube and Twitter aggregate attention metrics (Section 5.1)
1 |  fig. 3a | view_x |  Total number of views at day x |  L $>$ R** |  L $>$ R*** | $\times$
2 |  fig. 3b | relative_engagement |  Rank percentile of watch percentage among videos of similar lengths |  L $>$ R* |  L $>$ R*** |  L $>$ R**
3 |  fig. 3c | fraction of likes |  Number of likes divided by total number of reactions | L $<$ R*** | L $<$ R*** | L $<$ R***
4 |  fig. 3d | viral_potential |  Number of views potentially excited by one tweet |  L $>$ R*** |  L $>$ R** |  L $>$ R*
5 |  fig. 3e | tweet_x |  Total number of tweets at day x | L $<$ R*** | $\times$ | L $<$ R***
6 |  fig. 3f | retweet_x |  Total number of retweets at day x | L $<$ R*** | $\times$ | L $<$ R***
7 | – | reply_x |  Total number of replies at day x | L $<$ R*** | $\times$ | L $<$ R***
8 | – | original_tweet_x |  Total number of original tweets at day x | $\times$ | $\times$ | L $<$ R***
9 | – | quote_x |  Total number of quotes at day x | $\times$ | $\times$ | L $<$ R***
Temporal metrics of views and tweets (Section 5.2)
10 |  fig. 4a | viewing half-life |  Number of days to reach 50% views | L $<$ R*** | L $<$ R** | L $<$ R***
11 |  fig. 4b | tweeting half-life |  Number of days to reach 50% tweets | L $<$ R** | L $<$ R* | $\times$
12 |  fig. 4c | tweeting lifetime |  Time gap between the first and the last tweets in minutes | L $<$ R*** | L $<$ R* | $\times$
13 |  fig. 4d | tweeting inter-arrival time |  Average time gap between every two consecutive tweets in minutes | L $<$ R** | L $<$ R* | $\times$
Tweet cascades measures (Section 5.3)
|  | cascade start time |  Percentage of accumulated views of the video when the root of the cascade is tweeted |  |  |
14 |  fig. 6c,d | (isolated cascades) |  |  L $>$ R*** |  L $>$ R*** |  L $>$ R***
15 |  fig. 6c,d | (small cascades) |  |  L $>$ R*** |  L $>$ R*** |  L $>$ R***
16 |  fig. 6c,d | (large cascades) |  |  L $>$ R*** |  L $>$ R*** | $\times$
Network metrics of early adopters (Section 5.4)
17 |  fig. 7c | Gini_indegree |  Gini coefficient of indegree centrality | $\times$ |  L $>$ R*** |  L $>$ R***
18 |  fig. 7d | Gini_closeness |  Gini coefficient of closeness centrality |  L $>$ R* |  L $>$ R*** |  L $>$ R***
19 | – | Gini_betweenness |  Gini coefficient of betweenness centrality | $\times$ |  L $>$ R* | $\times$
20 | – | network_density |  Density of early adopters’ follower network | $\times$ | $\times$ |  L $>$ R***
21 | – | max_indegree |  Max indegree value in early adopters’ follower network | L $<$ R** | $\times$ | $\times$
22 | – | global_efficiency |  Average efficiency over all pairs of distinct early adopters | L $<$ R* | L $<$ R** | $\times$

Table 2:  Summary of all metrics and comparison across political leanings. L
$>$ R* means that the metric of a randomly selected left-leaning video is
significantly larger than that of a randomly selected right-leaning video.
Significance is measured by one-sided Mann–Whitney U test. *$p<0.05$,
**$p<0.01$, ***$p<0.001$. The significantly larger leaning is boldfaced.
“$\times$” sign indicates non-significant relation. Sample size: Abortion (L:
58; R: 111), Gun Control (L: 81; R: 154), BLM (L: 297; R: 396). See the
corresponding cross-referenced figures and discussions in Section 5 for more
details, “–” sign means the metric is not presented in a figure.

## Acknowledgments

We thank Xian Teng and Muheng Yan for their help in data annotation. We would
like to acknowledge the support from NSF #2027713, ARC DP180101985, and AFOSR
projects 19IOA078, 19RT0797, 20IOA064, and 20IOA065. Any opinions, findings,
and conclusions or recommendations expressed in this material do not
necessarily reflect the views of the funding sources.

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Supplementary materials accompanying the paper Whose Advantage? Measuring
Attention Dynamics across YouTube and Twitter on Controversial Topics.

##  Appendix A Identifying Topical Videos and Tweets

We curated a keyword list for each of the three controversial topics –
Abortion, Gun Control, and BLM. Keywords for Abortion and Gun Control came
from another recent work (guo2020inflating) and were expanded using contextual
expansion with sample tweets. Keywords for BLM were manually curated,
separating into pro- and anti-BLM keywords respectively. We expanded the
latter with white-supremacy related keywords. Table 3 lists all keywords.

We consider a video is potentially relevant if (a) it contains at least one
keyword in the video title or description; or (b) it is mentioned in a tweet
that contains the keywords in the tweet text. We obtained 815 candidate videos
for Abortion, 974 candidate videos for Gun Control, and 8,571 candidate videos
for BLM.

For Abortion and Gun Control, we manually annotated all candidate videos. All
videos were randomly divided into five buckets (with 163 or 195 videos in
each) and each bucket was labeled by an annotator. The five authors of this
paper, along with two postgraduate students who had extensive research
experience in computational social science, consisted of the annotator team.
Annotators were instructed to watch a video for at least five minutes before
making a decision of whether or not this video is relevant to the target
topic. To measure the inter-rater reliability, we randomly selected a bucket,
and had two more annotators labeling the same bucket. We computed the Fleiss’
Kappa. Abortion had Fleiss’ Kappa of 0.830 and Gun Control had that of 0.826,
suggesting a high agreement among annotators.

For BLM, we used a semi-automatic approach because the number of candidate
videos (8,571) is much larger. Based on our observations on Abortion and Gun
Control, we designed the following protocol:

  * •

For videos that contained any topic-relevant keywords in the video titles or
descriptions,

    * –

if the videos were also mentioned by tweets with topic-relevant keywords,
89.0%/86.7% of the videos were labeled relevant (in Abortion and Gun Control
respectively, same below). These videos consisted of 16.1%/12.5% of all
candidate videos. We labeled such videos as relevant, there were 418 in BLM.

    * –

if the videos were not mentioned by tweets with topic-relevant keywords,
77.8%/89.4% of the videos were labeled irrelevant. These videos consisted of
6.7%/11.9% of all candidate videos. We labeled such videos as irrelevant.

  * •

For videos that did not contain any topic-relevant keywords in the titles nor
descriptions, but had been mentioned by some topic-relevant tweets,

    * –

if the videos were tagged as “Music”, or did not have English captions, or did
not contain any topic-relevant keywords in the captions, 95.2%/80.6% of the
videos were labeled irrelevant. We labeled such videos as irrelevant.

    * –

we used the manual annotation steps for Abortion and Gun Control to label the
remaining 749 videos, out of which 359 videos were labeled relevant. The
Fleiss’ Kappa was 0.711.

In total, we identified 179 Abortion videos, 268 Gun Control videos, and 777
BLM videos. We extracted all tweets and Twitter users that mentioned any of
these topic-relevant videos from our 16 months data stream.

Topic |  Keywords
---|---
Abortion |  abortion, pro choice, pro life, ru486, planned parenthood, unborn baby, unborn babies, unborn child, roe v wade, roe versus wade, reproductive health care, birth control
Gun Control |  gun violence, gun reform, gun safety, gun regulation, gun death, gun law, gun right, gun grab, gun owner, gun control, gun free, gun sense, guns kill people, pro gun, firearm, no bill no break, disarm hate, second amendment, moms demand, wear orange, molon labe
BLM |  blm, black lives matter, all lives matter, blue lives matter, white lives matter, michael brown, mike brown, eric garner, freddie gray, walter scott, tamir rice, john crafford, tony robinson, eric harris, ezell ford, akai gurley, kajimeme powell, tanisha anderson, victor white, jordan baker, jerame reid, yvette smith, philip white, dante parker, mckenzie cochran, tyree woodson, jocques clemmons, fire bannon, charlottesville, kkk, ku klux klan, klansman, klansmen, domestic terrorism, white supremacist
Table 3: Keyword list for the three controversial topics.

##  Appendix B Estimating and Validating the Leanings of Twitter Users

We estimated the political leanings of early adopters on Twitter in four steps
with increasing coverage.

1) Curating seed hashtags. Twitter users are known to use political hashtags
in their user profiles as a marker of identity and a tool of network
gatekeeping, such as marking participation in an ongoing activism,
facilitating the establishment of social network ties around specific topics,
or expressing community identities (kulshrestha2017quantifying). Based on this
insight, we started by identifying users who clearly signaled political
ideologies in their Twitter profile descriptions. We curated two sets of
ideologically descriptive hashtags that were typically used by liberal and
conservative users. We described a hashtag as ideologically descriptive if it
either supported or criticized political leaders, their statements, and their
campaigns (e.g., #trump, #america1st, #maga). We called these hashtags as
political seed hashtags. The whole list can be found in Table 4. There are 25
left-leaning hashtags used by 260, 552, 817 users, and 35 right-leaning
hashtags used by 1023, 2065, 3798 users in Abortion, Gun Control, and BLM,
respectively.

Leaning |  Hashtags
---|---
Left-leaning |  theresistance, resist, resistance, fbr, impeachtrump, impeachtrumpnow, stillwithher, bluewave, uniteblue, bluewave2018, resisttrump, nevertrump, fucktrump, imwithher, demforce, voteblue, followbackresistance, bernie2020, resistanceunited, impeach45, imstillwithher, democrat, democrats, alwayswithher, flipitblue.
Right-leaning |  maga, trump, americafirst, draintheswamp, trumptrain, trump2020, kag, makeamericagreatagain, lockherup, presidenttrump, cruzcrew, trumppence2020, trumparmy, womenfortrump, istandwithtrump, keepamericagreat, neverhillary, trump2016, trumpworld, republican, republicans, hillaryforprison, kag2020, gop, trumpsupporter, trumpville, trumppence16, alwaystrump, donaldtrump, america1st, trumpismypresident, trump45, women4trump, teamtrump.
Table 4: Political seed hashtags.

2) Expanding seed hashtags. To obtain new political hashtags, we followed an
expansion protocol based on co-occurrence with the political seed hashtags in
the user profile descriptions. Firstly, we required that a candidate hashtag
must co-occur with the seed hashtags in at least 0.1% of all users’ profile
descriptions. Secondly, the candidate hashtag should be relevant to only one
ideology (either left or right). To achieve this, we computed the Shannon
entropy based on the co-occurrence of the candidate hashtag with the seed
left- and right-leaning hashtags. We selected the hashtags with entropy less
than or equal to 0.1. Table 5 presents the expanded political hashtags. The
number of users using left- and right-leaning hashtags are 264 and 1250 for
Abortion, 677 and 2375 for Gun Control, 970 and 4412 for BLM, respectively. We
noticed that the number of conservative users are about four times that of
liberal users across the three topics.

| Left-leaning hashtags | Right-leaning hashtags
---|---|---
Abortion | notmypresident |  | 2a, nra, conservative, 1a, patriot, prolife,
---
buildthewall, tcot, ccot, military, bluelivesmatter,
usa, qanon, deplorable
Gun Control | blacklivesmatter, blm, notmypresident |  | 2a, nra, 1a, conservative, patriot, prolife, christian,
---
buildthewall, qanon, deplorable, backtheblue, vets,
bluelivesmatter, usa
BLM |  | blacklivesmatter, blm, notmypresident
---
progressive, metoo
| 2a, nra, 1a, conservative, prolife, buildthewall,
---
christian, bluelivesmatter, tcot, ccot, qanon,
deplorable, israel, constitution, usa, backtheblue,
vets, covfefe, banislam, codeofvets, bansharia
Table 5: Expanded political hashtags from political seed hashtags.

3) Identifying seed users. After curating and expanding the left- and right-
leaning hashtags (jointly called political hashtags), we assigned the
political ideologies to the Twitter users. For users whose profile
descriptions included at least one political hashtag, we counted the numbers
of left- and right-leaning hashtags in their profile descriptions. The average
numbers of political hashtags per such user were 1.96, 1.97, and 2.04 for
Abortion, Gun Control and BLM, respectively. If the ratio of one leaning was
greater than or equal to 0.9, we assigned the corresponding ideology to the
user. We called the users seed users, since their labels were later used to
estimate the ideologies of other users. The numbers of liberal and
conservative seed users are given in Table 6.

| #Seed users |  |  Ideology propagation (10-fold CV performance)
---|---|---|---
| Liberal | Conservative | Total |  | Precision (Lib-Con) | Recall (Lib-Con) | F-score (Lib-Con)
Abortion | 258 (2.07%) | 1244 (9.99%) | 1502 (12.06%) |  | 0.99 - 0.99 | 0.96 - 1.00 | 0.98 - 1.00
Gun Control | 664 (2.38%) | 2362 (8.45%) | 3026 (10.83%) |  | 0.98 - 0.99 | 0.97 - 0.99 | 0.98 - 0.99
BLM | 950 (1.90%) | 4392 (8.76%) | 5342 (10.66%) |  | 0.98 - 0.99 | 0.96 - 1.00 | 0.97 - 0.99
Table 6: Political seed users and ideology propagation results. See Appendix B
for methods.

4) Propagating labels to other users. We aimed to infer the ideologies of
users who had no explicit political hashtags in their profile descriptions. We
first created a user-to-user network where the edge weight between two users
$(u,v)$ represented their shared audience, calculated using the Jaccard
similarity of their follower lists:

$J(u,v)=\dfrac{\text{followers}(u)\cap\text{followers}(v)}{\text{followers}(u)\cup\text{followers}(v)}$
(1)

The shared audience network is shown to be an effective method for splitting
users with different political behaviors (Stewart et al. 2017). When
estimating the user ideologies, our assumption is that users with the same
ideology share similar audience. The shared audience network was a dense
network since we created edges between any two users regardless of whether
they had a direct follower-following relation. The initial networks consisted
of approximately $28M$, $119M$, and $394M$ edges for Abortion, Gun Control,
and BLM, respectively. The corresponding network densities were 18.1%, 15.2%,
and 15.7%. To reduce the network complexity, we used a disparity filtering
algorithm (serrano2009extracting) to extract the network backbone. The
disparity filtering algorithm can identify important edges in a network. It
has a hyperparameter $\alpha$ to control the degree of edge importance. We
used $\alpha=0.05$ as suggested in the initial work (serrano2009extracting).
After disparity filtering, the resulting networks contained approximately
$1.9M$, $7.8M$, and $25.5M$ edges for Abortion, Gun Control, and BLM,
respectively. The densities of these networks ranged between 1% and 1.2%.

Next, we applied a label propagation algorithm (Zhou et al. 2004) on the
filtered network to infer the political leanings of the unknown users from
those of the seed users. Label propagation is a semi-supervised algorithm that
builds on two intuitions: (a) the seed labels should not change too much after
propagation; (b) the inferred labels should not be too different with their
neighbours. A teleport factor $\beta$ controls the trade-off between these two
intuitions. We used $\beta=0.85$ as suggested in the prior work
(yan2020mimicprop). The label propagation algorithm returned a liberal score
and a conservative score for each user in the network, which we then re-
normalized into a score between 0 and 1, where 0 indicated strong liberal and
1 indicated strong conservative. Re-normalization was done by computing the
ratio of conservative score to the summation of liberal and conservative
scores.

We stratified one-tenth of seed users in each fold and hided their labels.
Next, we compared the inferred political leanings to actual leanings of these
users. Note that we employed whole network during the label propagation
validation. Table 6 shows the evaluation results for the label propagation.

##  Appendix C Estimating and Validating the Leanings of YouTube Videos

Once we obtained the ideology probabilities of early adopters on Twitter, we
computed the leaning score for each YouTube video by averaging its promoters’
ideology scores. To assign discrete leaning labels (i.e. Left, Right and
Center) to the videos, we leveraged the Recfluence dataset (Ledwich and
Zaitsev 2020) as an external source to annotate YouTube videos’ leanings. This
dataset includes 816 YouTube channels where each channel has 10k+ subscribers
and more than 30% of its content is relevant to US politics, or cultural news,
or cultural commentary. It consists of channels of YouTubers in addition to
those of traditional news outlets. In this dataset, each channel is assigned
to Left (L), Center (C) or Right (R) leaning. The annotations were conducted
based on two well known media bias resources444Media Bias/Fact Check:
https://mediabiasfactcheck.com/; Ad Fontes Media:
https://www.adfontesmedia.com/ as well as manual labeling.

The videos whose channels’ leanings are available in the Recfluence dataset
are assigned to the same leaning. We were able to label 109 videos (L: 38, R:
66, C:5) from 38 channels for Abortion, 153 videos (L: 52, R: 88, C: 13) from
62 channels for Gun Control, and 521 videos (L: 194, R: 285, C:42) from 120
channels for BLM. To label the videos whose channels are not available in the
Recfluence dataset, we identified two thresholds for each topic –
$thr_{(L,C)}$ and $thr_{(C,R)}$ where $thr_{(L,C)}<thr_{(C,R)}$. For a given
video $\nu$, we assigned a discrete leaning label, $leaning\\_label(\nu)$,
based on its leaning score, $leaning\\_score(\nu)$, as follows:

$leaning\\_label(\nu)=\begin{cases}L&\text{if
$leaning\\_score(\nu)<thr_{(L,C)}$}\\\ R&\text{if
$leaning\\_score(\nu)>thr_{(C,R)}$}\\\ C&\text{otherwise.}\end{cases}$

We used videos with labels from Recfluence to find optimum thresholds
$thr_{(L,C)}$ and $thr_{(C,R)}$. We obtained a leaning score distribution for
each group (i.e., L, R and C). We identified outliers for each group using
inter-quartile rule and filtered them out. The leaning score distributions by
groups for each topic are given in Figure 8. Next, we found the thresholds
$thr_{(L,C)}$ and $thr_{(C,R)}$ using posterior probabilities as follows:

$\displaystyle P(L|leaning\\_score(\nu))=P(C|leaning\\_score(\nu)),$
$\displaystyle\text{where}~{}leaning\\_score(\nu)=thr_{(L,C)}$ (2)
$\displaystyle P(C|leaning\\_score(\nu))=P(R|leaning\\_score(\nu)),$
$\displaystyle\text{where}~{}leaning\\_score(\nu)=thr_{(C,R)}$ (3)

(a) Abortion

(b) Gun Control

(c) BLM

Figure 8: Distribution of leaning scores of videos (generated with kernel
density estimation after removal of outliers) whose channels are available in
Recfluence dataset (Ledwich and Zaitsev 2020). Each rod in the figures
corresponds to a video, and its color represents the leaning label of the
video obtained from Recfluence dataset.

The resulting threshold pairs ($thr_{(L,C)}$, $thr_{(C,R)}$) are
$(0.538,0.749)$ for Abortion, $(0.478,0.716)$ for Gun Control and
$(0.524,0.695)$ for BLM, respectively. Based on these thresholds, we inferred
the leaning labels of 70 videos (L: 20, R: 45, C: 5) from 58 channels for
Abortion, 115 videos (L: 29, R: 66, C: 20) from 98 channels for Gun Control,
256 videos (L: 103, R: 111, C: 42) from 197 channels for BLM.

To validate the video leaning estimation, we conducted a manual leaning
annotation for videos in Gun Control. We used stratified sampling where we
sampled 5 videos from each 10 percentile bin of their leaning scores. The
leaning of these 50 videos were annotated independently by three authors
considering three categories (left-leaning, right-leaning, unknown). The
Fleiss’ Kappa is 0.691, suggesting a moderate agreement among annotators. We
used the majority vote to assign a video label. Table 7 summarizes the
annotation results.

We observed that the only decile bin with both manually annotated left- and
right-leaning video is the 30th-40th percentile of leaning scores
$(0.465-0.665)$. Suggesting that the annotator had high agreement with the
estimated leaning scores, and that the threshold should be in this interval –
which is indeed the case for Gun Control with $thr_{(L,C)}$ lies within its
left boundary, and $thr_{(C,R)}$ only 0.05 away from its right boundary. In
addition, most of the videos which were assigned unknown by the raters are the
footage of shooting of Philando Castile and mass shooting events including Las
Vegas shooting and TX massacre. These videos do not include any stance on
their contents. However, we observed that the leaning scores of these videos
align well with the leaning of channels. One example is “Comments on TX church
shooting” where the video leaning score belongs to the second bin
$(80th-90th)$ percentile. The channel owner is a far-right YouTuber. The video
leaning score matches with the channel owner’s leaning, our approach is thus
successful for solving such ambiguities.

Table 7 shows manual leaning annotation results of 50 Gun Control videos.
Compared to the leaning score range in the second column (which is estimated),
the first three rows of 14 videos (which are labeled as left-leaning) are
consistently labeled (the result of manual labeling and estimated leanings
agree) as well as the last five rows of 19 videos (which are labeled as right-
leaning). The computed thresholds for Gun Control videos, as presented in
Figure 8, are $0.478$ and $0.716$.

Percentiles |  | Leaning score
---
range
| #left-
---
leaning
videos
| #right-
---
leaning
videos
| #unknown
---
videos
Videos annotated as unknown
0th - 10th | (0.138 - 0.289) | 4 | 0 | 1 |  | (1) Shooting footage of Philando Castile
---
(by CBC News (left-center))
10th - 20th | (0.289 - 0.364) | 5 | 0 | 0 | -
20th - 30th | (0.364 - 0.465) | 5 | 0 | 0 | -
30th - 40th | (0.465 - 0.665) | 2 | 1 | 2 |  | (1) Shooting footage of Philando Castile
---
(2) Las Vegas shooting footage
40th - 50th | (0.665 - 0.789) | 0 | 4 | 1 |  | (1) Discussion on gun violence in Chicago
---
(by Ron Paul)
50th - 60th | (0.789 - 0.837) | 0 | 3 | 2 |  | (1) Las Vegas shooting footage.
---
(2) Las Vegas shooting footage
(multiple shooters claim).
60th - 70th | (0.837 - 0.875) | 0 | 4 | 1 |  | (1) Comments on Las Vegas shooting
---
(by a Trump supporter.)
70th - 80th | (0.875 - 0.895) | 0 | 4 | 1 | (1) Las Vegas shooting footage.
80th - 90th | (0.895 - 0.924) | 0 | 3 | 2 |  | (1) Comments on TX church shooting
---
(by a far-right YouTuber).
(2) Comments on Las Vegas shooting
(by Milo).
90th - 100th | (0.924 - 0.982) | 0 | 5 | 0 | -
Total | - | 16 | 24 | 10 | -
Table 7: Manual leaning annotation of Gun Control videos.

##  Appendix D Computing Relative Engagement of a Video

From the collected videos we extracted topic (Abortion, Gun Control and BLM)
relevant videos for our topical analysis, but all collected videos were used
to compute relative engagement scores. We filter out videos that receive less
than 100 views within their first 30 days after upload, which is the same
filter used by brodersen2012youtube. We summarize how relative engagement
score is computed. For a given video, first we compute two aggregate metrics:

  * •

average watch time ($\bar{\omega}_{t}$): the total watch time $x_{w}[1:t]$
divided by the total view count $x_{v}[1:t]$ up to day $t$

$\bar{\omega}_{t}=\frac{\Sigma^{t}_{i=1}x_{w}[i]}{\Sigma^{t}_{i=1}x_{v}[i]}$

  * •

average watch percentage ($\bar{\mu}_{t}$): the average watch time
$\bar{\omega}_{t}$ normalized by video duration D

$\bar{\mu}_{t}=\frac{\bar{\omega}_{t}}{D}$

Then Engagement map of tweeted videos is constructed as follows. Two maps are
created; the first where x-axis shows video duration D, and the y-axis shows
average watch time over the first 120 days ($\bar{\omega}_{120}$) (Figure
9(a)) and the second with the same axis with average watch percentage over the
first 120 days ($\bar{\mu}_{120}$) (Figure 9(b)) as y-axis. All videos in the
tweeted videos dataset are projected onto both maps. The x-axis is split into
1,000 equally wide bins in log scale. These 2 maps are logically identical but
Figure 9(b) is easier to read as y-axis is bounded between [0,1]. This second
map is denoted as Engagement map.

Based on this Engagement map, relative engagement $\bar{\eta}_{t}\in[0,1]$
defined as the rank percentile of video in its duration bin. This is an
average engagement measure in the first t days. Figure 9(b) illustrates the
relation between video duration D, watch percentage $\bar{\mu}_{120}$ and
relative engagement $\bar{\eta}_{120}$ for three example videos. $V_{1}$ is a
left-leaning video in Abortion topic with duration of 136 seconds. $V_{2}$ is
a right-leaning video in Abortion topic with duration of 141 seconds. $V_{1}$
and $V_{2}$ have similar lengths but different watch percentages,
$\bar{\mu}_{120}(V_{1})=0.83$ and $\bar{\mu}_{120}(V_{2})=0.59$. The
difference becomes more apparent with relative engagement scores,
$\bar{\eta}_{120}(V_{1})=0.94$ and $\bar{\eta}_{120}(V_{2})=0.27$ as shown in
Figure 9(b). $V_{3}$ is also a right-leaning video in Abortion which is
significantly long than the others. Although $V_{3}$ is not as much watched as
the previous two, $\bar{\mu}_{120}(V_{3})=0.40$, relative engagement of
$V_{3}$ is much higher than $V_{2}$, $\bar{\eta}_{120}(V_{3})=0.86$ as longer
videos tend to have lower average watch percentages.

(a) Duration vs watch time

(b) Engagement map

Figure 9: Video engagement in the tweeted videos dataset at the age of 120
days. (a) video duration D vs average watch time $\bar{\omega}_{120}$ (b) the
engagement map: video duration D vs average watch percentage
$\bar{\eta}_{120}$. This figure originally appeared in (Wu, Rizoiu, and Xie
2018).

##  Appendix E Computing Viral Potential of a Video via the Hawkes Intensity
Process

Viral potential estimates the expected number of views a video will obtain if
mentioned by an average tweet (Rizoiu and Xie 2017). More specifically, it is
the area under the impulse response function of an integral equation known as
Hawkes Intensity Process (HIP) (Rizoiu et al. 2017), which are learned for
each video by using the first 120 days of tweeting and viewing history. The
HIP model is designed to describe the phenomena of tweets driving or
attracting video views including factors such as network effect (such as views
beget views), system memory (such as interests in news videos wane within a
week, but interests on music videos sustain for several years), and video
quality (including factors such as sensitivity to tweet mentions and inherent
attractiveness). We estimate the number of views per tweet via the HIP model
rather than simply dividing the number of views by the number of tweets,
because the model takes into account future views that are expected to unfold
after the 120-day cut-off. The same reason motivates the name viral potential
rather than viral score. One limitation of HIP and viral potential is the
notion of an “average” tweet being implemented by marginalizing over power-law
distributed number of followers. This means network size variations across
different topics are not taken into account. A self-contained summary of HIP
and viral potential computation is included below. The viral potential is a
positive number, since views cannot be negative (i.e., no video loses views).
However, it can take values less than one, corresponding to the effect of
tweets being dampened (one view per several tweets) rather than amplified, as
often seen in videos linked by spam tweets.

Hawkes Intensity Process (HIP) (Rizoiu et al. 2017) extends the well-known
Hawkes (self-exciting) process (hawkes1971spectra) to describe the volume of
activities within a fixed time interval (e.g. daily). This is done by taking
expectations over the stochastic event history. Specifically, this model
describes a self-exciting phenomenon that is commonly observed in online
social network (Rizoiu and Xie 2017). It models the target quantity $x[t]$ as
a self-consistent equation into three parts: the unobserved external
influence, the effects of external promotions, and the influence from
historical events. Formally, it can be written as

$x[t]=\gamma\mathds{1}[t=0]+\eta\mathds{1}[t>0]+\alpha
s[t]+C\sum_{\tau=1}^{t}x[t-\tau](\tau+c)^{-(1+\theta)}$ (4)

The first two terms represent unobserved external influences. $\gamma$ and
$\eta$ model the strengths of an initial impulse and a constant background
rate, respectively. In the middle component, $\alpha$ is the sensitivity to
external promotion, $s[t]$ is the volume of promotion, and $\alpha s(t)$ is
the instantaneous response to promotion. In the last component, $\theta$ is
the exponent of a power-law memory kernel $(\tau+c)^{-(1+\theta)}$. $c$ is a
nuisance parameter for keeping the kernel bounded, and $C$ accounts for the
latent content quality. Overall, this last component models the impact over
its own event history $x[\tau]$ for $\tau{=}1:t{-}1$.

In our case, $x[t]$ is the time series of daily views $x_{v}[t]$ on YouTube.
$s[t]$ is the daily number of tweets on Twitter. The parameter set
$\\{\gamma,\eta,\alpha,C,c,\theta\\}$ is estimated from the first 90-day
interval of each video using the constrained L-BFGS algorithm in SciPy Python
package.555https://github.com/andrei-rizoiu/hip-popularity

##  Appendix F View and Tweets over time: additional plots

Figure 10 and Figure 11 visualize the attention accumulation for Gun Control
and BLM topic.

Figure 10: Comparing view and tweet accumulations of left- and right-leaning
videos in Gun Control. (a) and (b) show CCDF of views and tweets accumulated
for Day 1 and Day 30, respectively. As seen in (c), the differences between
left- and right-leaning videos are larger than the differences of Abortion
videos, but the differences between accumulation of views and tweets are
smaller than Abortion videos. Figure 11: Comparing view and tweet
accumulations of left- and right-leaning videos in BLM. (a) and (b) show CCDF
of views and tweets accumulated for Day 1 and Day 30, respectively. (c) shows
that the differences between left- and right-leaning videos are the largest
among all topics.

##  Appendix G Tweet cascades: additional plots

Figure 12 visualizes attention distribution of the isolated, small, and large
cascades for Gun Control and BLM topic.

(a) Tweet cascades in Gun Control left-leaning videos.

(b) Tweet cascades in Gun Control right-leaning videos.

(c) Tweet cascades in BLM left-leaning videos.

(d) Tweet cascades in BLM right-leaning videos.

Figure 12: Cascade start times measured at view accumulation process. Table 2
reports that retweet cascades for left-leaning videos start significantly
later in view accumulation process for all topics.

##  Appendix H Additional metrics on early adopter networks

We measured additional metrics on early-adopter networks, the comparative
findings are summarized in Table 2.

Network density is the fraction of existing edges among all possible pair-wise
edges. It quantifies the connectivity of the follower network. A higher value
indicates the early adopters are more cohesively followed among themselves.

Maximum indegree is the maximum indegree value in the network. It provides
information about the most influential user in the network.

Global efficiency is computed as the number of closed triplets over the total
number of triplets. It measures the average efficiency over all pairs of
distinct users. Higher global efficiency reflects higher capacity to diffuse
information over the network.

Gini coefficient of betweenness centrality measures the dispersion in fraction
of all shortest paths in the network that pass through a given user. Higher
coefficient implies that a few early adopters are on the shortest path for
most node-pairs, but the rest of the early adopters are not.

##  Appendix I Intersecting relative engagement with view and tweet count

Figure 13: (Rows) Boxplots of three attention metrics – number of Twitter
followers, the number of tweets, and the number of video views at 120 days –
broken down by the relative engagement of each video in five groups of
20-percent each, (Columns) for each of the three topics - Abortion, Gun
Control and BLM. Population means are denoted by a dashed green line ending
with stars. Left-leaning videos are promoted by less tweets overall (except
the most engaging group of Gun Control, and the least engaging groups for
Abortion and Gun Control), but on average attracts more views (except the 40,
60 and 100 percentile group for BLM).

We link the measure of relative engagement of YouTube videos to the number of
tweets, the number of followers, and the number of views. The number of tweets
and views are accumulated for the first 120 days after upload. Rather than
looking at the overall correlation, which is dominated by large variations, we
choose to group videos according to relative engagement. This allows us to
interpret the change in Twitter user base, tweeting behavior, and YouTube
attention across videos of different engagement levels.

We compute relative engagement score for each video and rank the videos in
descending order of relative engagement score. Then, the videos are divided
into 5 groups, each including the top 20% videos, top 20%~40% videos, top
40%~60% videos, top 60%~80% videos and top 80%~100% videos. For each group,
left and right-leaning videos are shown as boxplots of Tweet120, Follower
count, and View120 (Figure 13).

As seen earlier, left-leaning videos have less number of tweets on average
which can be seen with Tweet120. Especially, in the case of Abortion, the
middle buckets (20%, 40% and 60%) show that left-leaning videos have
significantly less tweets but have more views on average. The same pattern
holds for Gun Control. This is consistent with the previous finding that left-
leaning videos are more effectively promoted (higher viral potential) with
fewer number of tweets.

We can observe that higher numbers of followers does not imply higher numbers
of tweets on average as witnessed by most of middle buckets. However, we note
that the top three outliers in left-leaning videos in 40% bucket of Follower
count boxplot for Gun Control corresponds to the top three outliers of left-
leaning videos in the same bucket of Tweet120.

In terms of View120, the average trends support findings in Section 5.1 in
that left-leaning videos in Abortion and Gun Control have higher views whereas
left-leaning videos in BLM have lower views. Although view count is considered
as an important measure of video popularity in general, no noticeable
correlation is shown between relative engagement and View120. This is expected
as engagement metric and popularity metric capture very different perspective
of online attention (wu2019estimating). * aaai22 xplatform-ap-ref