5,923 research outputs found
To Polarize or Not: Comparing Networks of News Consumption
This is the final version. Available via the link in this record.We use individual data on browsing histories combined with survey data to examine whether online news exposure exhibits signs of segregation and selectivity. By using online news behaviour combined with survey reports of attitudes, we can capture exposure to both traditional news sources and news shared via social media platforms. Most importantly, we can also examine what types of individuals (e.g. partisans, educated) are more likely to exhibit selective tendencies. We find, consistent with recent empirical work, the extent of segregation in exposure may be overstated. Furthermore, the degree of segregation and selectivity varies across groups that are defined by holding shared political preferences. For example, in the case of Brexit, those who supported the ‘Leave’ side were more selective in their news exposure. Our approach allows comparison of news exposure patterns by domains versus news exposure to topics. To our knowledge, this is the first analysis to allow this comparison.This
work was supported by the Economic and Social Research Council ES/N012283/1 "Measuring
Information Exposure in Dynamic and Dependent Networks (ExpoNet)
Social Media and Fake News in the 2016 Election
Following the 2016 U.S. presidential election, many have expressed concern about the effects of false stories ("fake news"), circulated largely through social media. We discuss the economics of fake news and present new data on its consumption prior to the election. Drawing on web browsing data, archives of fact-checking websites, and results from a new online sur-vey, we find:(i) social media was an important but not dominant source of election news, with14 percent of Americans calling social media their "most important" source;(ii) of the known false news stories that appeared in the three months before the election, those favoring Trump were shared a total of 30 million times on Facebook, while those favoring Clinton were shared8 million times;(iii) the average American adult saw on the order of one or perhaps several fake news stories in the months around the election, with just over half of those who recalled seeing them believing them; and(iv) people are much more likely to believe stories that favor their preferred candidate, especially if they have ideologically segregated social media networks
Network segregation in a model of misinformation and fact checking
Misinformation under the form of rumor, hoaxes, and conspiracy theories
spreads on social media at alarming rates. One hypothesis is that, since social
media are shaped by homophily, belief in misinformation may be more likely to
thrive on those social circles that are segregated from the rest of the
network. One possible antidote is fact checking which, in some cases, is known
to stop rumors from spreading further. However, fact checking may also backfire
and reinforce the belief in a hoax. Here we take into account the combination
of network segregation, finite memory and attention, and fact-checking efforts.
We consider a compartmental model of two interacting epidemic processes over a
network that is segregated between gullible and skeptic users. Extensive
simulation and mean-field analysis show that a more segregated network
facilitates the spread of a hoax only at low forgetting rates, but has no
effect when agents forget at faster rates. This finding may inform the
development of mitigation techniques and overall inform on the risks of
uncontrolled misinformation online
Network polarization, filter bubbles, and echo chambers: An annotated review of measures and reduction methods
Polarization arises when the underlying network connecting the members of a
community or society becomes characterized by highly connected groups with weak
inter-group connectivity. The increasing polarization, the strengthening of
echo chambers, and the isolation caused by information filters in social
networks are increasingly attracting the attention of researchers from
different areas of knowledge such as computer science, economics, social and
political sciences. This work presents an annotated review of network
polarization measures and models used to handle the polarization. Several
approaches for measuring polarization in graphs and networks were identified,
including those based on homophily, modularity, random walks, and balance
theory. The strategies used for reducing polarization include methods that
propose edge or node editions (including insertions or deletions, as well as
edge weight modifications), changes in social network design, or changes in the
recommendation systems embedded in these networks.Comment: Corrected a typo in Section 3.2; the rest remains unchange
Algorithmic bias amplifies opinion polarization: A bounded confidence model
The flow of information reaching us via the online media platforms is
optimized not by the information content or relevance but by popularity and
proximity to the target. This is typically performed in order to maximise
platform usage. As a side effect, this introduces an algorithmic bias that is
believed to enhance polarization of the societal debate. To study this
phenomenon, we modify the well-known continuous opinion dynamics model of
bounded confidence in order to account for the algorithmic bias and investigate
its consequences. In the simplest version of the original model the pairs of
discussion participants are chosen at random and their opinions get closer to
each other if they are within a fixed tolerance level. We modify the selection
rule of the discussion partners: there is an enhanced probability to choose
individuals whose opinions are already close to each other, thus mimicking the
behavior of online media which suggest interaction with similar peers. As a
result we observe: a) an increased tendency towards polarization, which emerges
also in conditions where the original model would predict convergence, and b) a
dramatic slowing down of the speed at which the convergence at the asymptotic
state is reached, which makes the system highly unstable. Polarization is
augmented by a fragmented initial population
Does Campaigning on Social Media Make a Difference? Evidence from candidate use of Twitter during the 2015 and 2017 UK Elections
Social media are now a routine part of political campaigns all over the
world. However, studies of the impact of campaigning on social platform have
thus far been limited to cross-sectional datasets from one election period
which are vulnerable to unobserved variable bias. Hence empirical evidence on
the effectiveness of political social media activity is thin. We address this
deficit by analysing a novel panel dataset of political Twitter activity in the
2015 and 2017 elections in the United Kingdom. We find that Twitter based
campaigning does seem to help win votes, a finding which is consistent across a
variety of different model specifications including a first difference
regression. The impact of Twitter use is small in absolute terms, though
comparable with that of campaign spending. Our data also support the idea that
effects are mediated through other communication channels, hence challenging
the relevance of engaging in an interactive fashion
Of Echo Chambers and Contrarian Clubs:Exposure to Political Disagreement Among German and Italian Users of Twitter
Scholars have debated whether social media platforms, by allowing users to select the information to which they are exposed, may lead people to isolate themselves from viewpoints with which they disagree, thereby serving as political “echo chambers.” We investigate hypotheses concerning the circumstances under which Twitter users who communicate about elections would engage with (a) supportive, (b) oppositional, and (c) mixed political networks. Based on online surveys of representative samples of Italian and German individuals who posted at least one Twitter message about elections in 2013, we find substantial differences in the extent to which social media facilitates exposure to similar versus dissimilar political views. Our results suggest that exposure to supportive, oppositional, or mixed political networks on social media can be explained by broader patterns of political conversation (i.e., structure of offline networks) and specific habits in the political use of social media (i.e., the intensity of political discussion). These findings suggest that disagreement persists on social media even when ideological homophily is the modal outcome, and that scholars should pay more attention to specific situational and dispositional factors when evaluating the implications of social media for political communication
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