11,688 research outputs found
The Ebb and Flow of Controversial Debates on Social Media
We explore how the polarization around controversial topics evolves on
Twitter - over a long period of time (2011 to 2016), and also as a response to
major external events that lead to increased related activity. We find that
increased activity is typically associated with increased polarization;
however, we find no consistent long-term trend in polarization over time among
the topics we study.Comment: Accepted as a short paper at ICWSM 2017. Please cite the ICWSM
version and not the ArXiv 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
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
Minimizing Polarization and Disagreement in Social Networks
The rise of social media and online social networks has been a disruptive
force in society. Opinions are increasingly shaped by interactions on online
social media, and social phenomena including disagreement and polarization are
now tightly woven into everyday life. In this work we initiate the study of the
following question: given agents, each with its own initial opinion that
reflects its core value on a topic, and an opinion dynamics model, what is the
structure of a social network that minimizes {\em polarization} and {\em
disagreement} simultaneously?
This question is central to recommender systems: should a recommender system
prefer a link suggestion between two online users with similar mindsets in
order to keep disagreement low, or between two users with different opinions in
order to expose each to the other's viewpoint of the world, and decrease
overall levels of polarization? Our contributions include a mathematical
formalization of this question as an optimization problem and an exact,
time-efficient algorithm. We also prove that there always exists a network with
edges that is a approximation to the optimum.
For a fixed graph, we additionally show how to optimize our objective function
over the agents' innate opinions in polynomial time.
We perform an empirical study of our proposed methods on synthetic and
real-world data that verify their value as mining tools to better understand
the trade-off between of disagreement and polarization. We find that there is a
lot of space to reduce both polarization and disagreement in real-world
networks; for instance, on a Reddit network where users exchange comments on
politics, our methods achieve a -fold reduction in polarization
and disagreement.Comment: 19 pages (accepted, WWW 2018
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