49 research outputs found
Factors in Recommending Contrarian Content on Social Media
Polarization is a troubling phenomenon that can lead to societal divisions
and hurt the democratic process. It is therefore important to develop methods
to reduce it.
We propose an algorithmic solution to the problem of reducing polarization.
The core idea is to expose users to content that challenges their point of
view, with the hope broadening their perspective, and thus reduce their
polarity. Our method takes into account several aspects of the problem, such as
the estimated polarity of the user, the probability of accepting the
recommendation, the polarity of the content, and popularity of the content
being recommended.
We evaluate our recommendations via a large-scale user study on Twitter users
that were actively involved in the discussion of the US elections results.
Results shows that, in most cases, the factors taken into account in the
recommendation affect the users as expected, and thus capture the essential
features of the problem.Comment: accepted as a short paper at ACM WebScience 2017. arXiv admin note:
substantial text overlap with arXiv:1703.1093
Hot Streaks on Social Media
Measuring the impact and success of human performance is common in various
disciplines, including art, science, and sports. Quantifying impact also plays
a key role on social media, where impact is usually defined as the reach of a
user's content as captured by metrics such as the number of views, likes,
retweets, or shares. In this paper, we study entire careers of Twitter users to
understand properties of impact. We show that user impact tends to have certain
characteristics: First, impact is clustered in time, such that the most
impactful tweets of a user appear close to each other. Second, users commonly
have 'hot streaks' of impact, i.e., extended periods of high-impact tweets.
Third, impact tends to gradually build up before, and fall off after, a user's
most impactful tweet. We attempt to explain these characteristics using various
properties measured on social media, including the user's network, content,
activity, and experience, and find that changes in impact are associated with
significant changes in these properties. Our findings open interesting avenues
for future research on virality and influence on social media.Comment: Accepted as a full paper at ICWSM 2019. Please cite the ICWSM versio
The Effect of Collective Attention on Controversial Debates on Social Media
We study the evolution of long-lived controversial debates as manifested on
Twitter from 2011 to 2016. Specifically, we explore how the structure of
interactions and content of discussion varies with the level of collective
attention, as evidenced by the number of users discussing a topic. Spikes in
the volume of users typically correspond to external events that increase the
public attention on the topic -- as, for instance, discussions about `gun
control' often erupt after a mass shooting.
This work is the first to study the dynamic evolution of polarized online
debates at such scale. By employing a wide array of network and content
analysis measures, we find consistent evidence that increased collective
attention is associated with increased network polarization and network
concentration within each side of the debate; and overall more uniform lexicon
usage across all users.Comment: accepted at ACM WebScience 201