4,771 research outputs found
Analyzing the Digital Traces of Political Manipulation: The 2016 Russian Interference Twitter Campaign
Until recently, social media was seen to promote democratic discourse on
social and political issues. However, this powerful communication platform has
come under scrutiny for allowing hostile actors to exploit online discussions
in an attempt to manipulate public opinion. A case in point is the ongoing U.S.
Congress' investigation of Russian interference in the 2016 U.S. election
campaign, with Russia accused of using trolls (malicious accounts created to
manipulate) and bots to spread misinformation and politically biased
information. In this study, we explore the effects of this manipulation
campaign, taking a closer look at users who re-shared the posts produced on
Twitter by the Russian troll accounts publicly disclosed by U.S. Congress
investigation. We collected a dataset with over 43 million election-related
posts shared on Twitter between September 16 and October 21, 2016, by about 5.7
million distinct users. This dataset included accounts associated with the
identified Russian trolls. We use label propagation to infer the ideology of
all users based on the news sources they shared. This method enables us to
classify a large number of users as liberal or conservative with precision and
recall above 90%. Conservatives retweeted Russian trolls about 31 times more
often than liberals and produced 36x more tweets. Additionally, most retweets
of troll content originated from two Southern states: Tennessee and Texas.
Using state-of-the-art bot detection techniques, we estimated that about 4.9%
and 6.2% of liberal and conservative users respectively were bots. Text
analysis on the content shared by trolls reveals that they had a mostly
conservative, pro-Trump agenda. Although an ideologically broad swath of
Twitter users was exposed to Russian Trolls in the period leading up to the
2016 U.S. Presidential election, it was mainly conservatives who helped amplify
their message
How Polarized Have We Become? A Multimodal Classification of Trump Followers and Clinton Followers
Polarization in American politics has been extensively documented and
analyzed for decades, and the phenomenon became all the more apparent during
the 2016 presidential election, where Trump and Clinton depicted two radically
different pictures of America. Inspired by this gaping polarization and the
extensive utilization of Twitter during the 2016 presidential campaign, in this
paper we take the first step in measuring polarization in social media and we
attempt to predict individuals' Twitter following behavior through analyzing
ones' everyday tweets, profile images and posted pictures. As such, we treat
polarization as a classification problem and study to what extent Trump
followers and Clinton followers on Twitter can be distinguished, which in turn
serves as a metric of polarization in general. We apply LSTM to processing
tweet features and we extract visual features using the VGG neural network.
Integrating these two sets of features boosts the overall performance. We are
able to achieve an accuracy of 69%, suggesting that the high degree of
polarization recorded in the literature has started to manifest itself in
social media as well.Comment: 16 pages, SocInfo 2017, 9th International Conference on Social
Informatic
Analyzing Ideological Communities in Congressional Voting Networks
We here study the behavior of political party members aiming at identifying
how ideological communities are created and evolve over time in diverse
(fragmented and non-fragmented) party systems. Using public voting data of both
Brazil and the US, we propose a methodology to identify and characterize
ideological communities, their member polarization, and how such communities
evolve over time, covering a 15-year period. Our results reveal very distinct
patterns across the two case studies, in terms of both structural and dynamic
properties
Jointly they edit: examining the impact of community identification on political interaction in Wikipedia
In their 2005 study, Adamic and Glance coined the memorable phrase "divided
they blog", referring to a trend of cyberbalkanization in the political
blogosphere, with liberal and conservative blogs tending to link to other blogs
with a similar political slant, and not to one another. As political discussion
and activity increasingly moves online, the power of framing political
discourses is shifting from mass media to social media. Continued examination
of political interactions online is critical, and we extend this line of
research by examining the activities of political users within the Wikipedia
community. First, we examined how users in Wikipedia choose to display (or not
to display) their political affiliation. Next, we more closely examined the
patterns of cross-party interaction and community participation among those
users proclaiming a political affiliation. In contrast to previous analyses of
other social media, we did not find strong trends indicating a preference to
interact with members of the same political party within the Wikipedia
community. Our results indicate that users who proclaim their political
affiliation within the community tend to proclaim their identity as a
"Wikipedian" even more loudly. It seems that the shared identity of "being
Wikipedian" may be strong enough to triumph over other potentially divisive
facets of personal identity, such as political affiliation.Comment: 33 pages, 5 figure
Dancing to the Partisan Beat: A First Analysis of Political Communication on TikTok
TikTok is a video-sharing social networking service, whose popularity is
increasing rapidly. It was the world's second-most downloaded app in 2019.
Although the platform is known for having users posting videos of themselves
dancing, lip-syncing, or showcasing other talents, user-videos expressing
political views have seen a recent spurt. This study aims to perform a primary
evaluation of political communication on TikTok. We collect a set of US
partisan Republican and Democratic videos to investigate how users communicated
with each other about political issues. With the help of computer vision,
natural language processing, and statistical tools, we illustrate that
political communication on TikTok is much more interactive in comparison to
other social media platforms, with users combining multiple information
channels to spread their messages. We show that political communication takes
place in the form of communication trees since users generate branches of
responses to existing content. In terms of user demographics, we find that
users belonging to both the US parties are young and behave similarly on the
platform. However, Republican users generated more political content and their
videos received more responses; on the other hand, Democratic users engaged
significantly more in cross-partisan discussions.Comment: Accepted as a full paper at the 12th International ACM Web Science
Conference (WebSci 2020). Please cite the WebSci version; Second version
includes corrected typo
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