21,783 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
Ideological and Temporal Components of Network Polarization in Online Political Participatory Media
Political polarization is traditionally analyzed through the ideological
stances of groups and parties, but it also has a behavioral component that
manifests in the interactions between individuals. We present an empirical
analysis of the digital traces of politicians in politnetz.ch, a Swiss online
platform focused on political activity, in which politicians interact by
creating support links, comments, and likes. We analyze network polarization as
the level of intra- party cohesion with respect to inter-party connectivity,
finding that supports show a very strongly polarized structure with respect to
party alignment. The analysis of this multiplex network shows that each layer
of interaction contains relevant information, where comment groups follow
topics related to Swiss politics. Our analysis reveals that polarization in the
layer of likes evolves in time, increasing close to the federal elections of
2011. Furthermore, we analyze the internal social network of each party through
metrics related to hierarchical structures, information efficiency, and social
resilience. Our results suggest that the online social structure of a party is
related to its ideology, and reveal that the degree of connectivity across two
parties increases when they are close in the ideological space of a multi-party
system.Comment: 35 pages, 11 figures, Internet, Policy & Politics Conference,
University of Oxford, Oxford, UK, 25-26 September 201
Influence of augmented humans in online interactions during voting events
The advent of the digital era provided a fertile ground for the development
of virtual societies, complex systems influencing real-world dynamics.
Understanding online human behavior and its relevance beyond the digital
boundaries is still an open challenge. Here we show that online social
interactions during a massive voting event can be used to build an accurate map
of real-world political parties and electoral ranks. We provide evidence that
information flow and collective attention are often driven by a special class
of highly influential users, that we name "augmented humans", who exploit
thousands of automated agents, also known as bots, for enhancing their online
influence. We show that augmented humans generate deep information cascades, to
the same extent of news media and other broadcasters, while they uniformly
infiltrate across the full range of identified groups. Digital augmentation
represents the cyber-physical counterpart of the human desire to acquire power
within social systems.Comment: 11 page
Community Structure in Congressional Cosponsorship Networks
We study the United States Congress by constructing networks between Members
of Congress based on the legislation that they cosponsor. Using the concept of
modularity, we identify the community structure of Congressmen, as connected
via sponsorship/cosponsorship of the same legislation, to investigate the
collaborative communities of legislators in both chambers of Congress. This
analysis yields an explicit and conceptually clear measure of political
polarization, demonstrating a sharp increase in partisan polarization which
preceded and then culminated in the 104th Congress (1995-1996), when
Republicans took control of both chambers. Although polarization has since
waned in the U.S. Senate, it remains at historically high levels in the House
of Representatives.Comment: 8 pages, 4 figures (some with multiple parts), to appear in Physica
A; additional background info and explanations added from last versio
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