23,243 research outputs found
Quantifying and minimizing risk of conflict in social networks
Controversy, disagreement, conflict, polarization and opinion divergence in social networks have been the subject of much recent research. In particular, researchers have addressed the question of how such concepts can be quantified given people’s prior opinions, and how they can be optimized by influencing the opinion of a small number of people or by editing the network’s connectivity.
Here, rather than optimizing such concepts given a specific set of prior opinions, we study whether they can be optimized in the average case and in the worst case over all sets of prior opinions. In particular, we derive the worst-case and average-case conflict risk of networks, and we propose algorithms for optimizing these.
For some measures of conflict, these are non-convex optimization problems with many local minima. We provide a theoretical and empirical analysis of the nature of some of these local minima, and show how they are related to existing organizational structures.
Empirical results show how a small number of edits quickly decreases its conflict risk, both average-case and worst-case. Furthermore, it shows that minimizing average-case conflict risk often does not reduce worst-case conflict risk. Minimizing worst-case conflict risk on the other hand, while computationally more challenging, is generally effective at minimizing both worst-case as well as average-case conflict risk
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
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
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