7,355 research outputs found
Viewpoint Discovery and Understanding in Social Networks
The Web has evolved to a dominant platform where everyone has the opportunity
to express their opinions, to interact with other users, and to debate on
emerging events happening around the world. On the one hand, this has enabled
the presence of different viewpoints and opinions about a - usually
controversial - topic (like Brexit), but at the same time, it has led to
phenomena like media bias, echo chambers and filter bubbles, where users are
exposed to only one point of view on the same topic. Therefore, there is the
need for methods that are able to detect and explain the different viewpoints.
In this paper, we propose a graph partitioning method that exploits social
interactions to enable the discovery of different communities (representing
different viewpoints) discussing about a controversial topic in a social
network like Twitter. To explain the discovered viewpoints, we describe a
method, called Iterative Rank Difference (IRD), which allows detecting
descriptive terms that characterize the different viewpoints as well as
understanding how a specific term is related to a viewpoint (by detecting other
related descriptive terms). The results of an experimental evaluation showed
that our approach outperforms state-of-the-art methods on viewpoint discovery,
while a qualitative analysis of the proposed IRD method on three different
controversial topics showed that IRD provides comprehensive and deep
representations of the different viewpoints
Vulnerability in Social Epistemic Networks
Social epistemologists should be well-equipped to explain and evaluate the growing vulnerabilities associated with filter bubbles, echo chambers, and group polarization in social media. However, almost all social epistemology has been built for social contexts that involve merely a speaker-hearer dyad. Filter bubbles, echo chambers, and group polarization all presuppose much larger and more complex network structures. In this paper, we lay the groundwork for a properly social epistemology that gives the role and structure of networks their due. In particular, we formally define epistemic constructs that quantify the structural epistemic position of each node within an interconnected network. We argue for the epistemic value of a structure that we call the (m,k)-observer. We then present empirical evidence that (m,k)-observers are rare in social media discussions of controversial topics, which suggests that people suffer from serious problems of epistemic vulnerability. We conclude by arguing that social epistemologists and computer scientists should work together to develop minimal interventions that improve the structure of epistemic networks
Modeling and Analyzing Collective Behavior Captured by Many-to-Many Networks
L'abstract è presente nell'allegato / the abstract is in the attachmen
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
The role of bot squads in the political propaganda on Twitter
Social Media are nowadays the privileged channel for information spreading
and news checking. Unexpectedly for most of the users, automated accounts, also
known as social bots, contribute more and more to this process of news
spreading. Using Twitter as a benchmark, we consider the traffic exchanged,
over one month of observation, on a specific topic, namely the migration flux
from Northern Africa to Italy. We measure the significant traffic of tweets
only, by implementing an entropy-based null model that discounts the activity
of users and the virality of tweets. Results show that social bots play a
central role in the exchange of significant content. Indeed, not only the
strongest hubs have a number of bots among their followers higher than
expected, but furthermore a group of them, that can be assigned to the same
political tendency, share a common set of bots as followers. The retwitting
activity of such automated accounts amplifies the presence on the platform of
the hubs' messages.Comment: Under Submissio
Cognitive network structure: an experimental study
In this paper we present first experimental results about a small group of
people exchanging private and public messages in a virtual community. Our goal
is the study of the cognitive network that emerges during a chat seance. We
used the Derrida coefficient and the triangle structure under the working
assumption that moods and perceived mutual affinity can produce results
complementary to a full semantic analysis. The most outstanding outcome is the
difference between the network obtained considering publicly exchanged messages
and the one considering only privately exchanged messages: in the former case,
the network is very homogeneous, in the sense that each individual interacts in
the same way with all the participants, whilst in the latter the interactions
among different agents are very heterogeneous, and are based on "the enemy of
my enemy is my friend" strategy. Finally a recent characterization of the
triangular cliques has been considered in order to describe the intimate
structure of the network. Experimental results confirm recent theoretical
studies indicating that certain 3-vertex structures can be used as indicators
for the network aging and some relevant dynamical features.Comment: 15 pages, 5 figures, 3 table
Examining Polarized COVID-19 Twitter Discussion Using Inverse Reinforcement Learning
In this work, we model users\u27 behavior on Twitter in discussion of the COVID-19 outbreak using inverse reinforcement learning to better understand the underlying forces that drive the observed pattern of polarization. In doing so, we address the largely untapped potential of inverse reinforcement learning to model users\u27 behavior on social media, and contribute to the body of sociology, psychology, and communication research seeking to elucidate the causes of socio-cultural polarization. We hypothesize that structural characteristics of each week\u27s retweet network as well as COVID-19 data on cases, hospitalizations, and outcomes are related to the Twitter users\u27 reward function which leads to polarized discussion of COVID-19 on the platform. To derive the state space of our inverse reinforcement learning model, we compute the relative modularity of retweet networks formed from retweets about COVID-19. The action space is determined by the distribution of mask-wearing sentiment in tweets about COVID-19. We build a fine-tune a BERT text classifier to determine mask-wearing sentiment in tweet. We design state features which reflect both structural characteristics of the retweet networks and COVID-19 data on cases, hospitalizations, and outcomes. Our results indicate that polarized Twitter discussion about COVID-19 weighs more heavily on features relating to the severity of the COVID-19 outbreak and less heavily on features relating to the structure of retweet networks. Overall, our results demonstrate the aptitude of inverse reinforcement learning in helping understand user behavior on social media
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