38,072 research outputs found
Reading the Source Code of Social Ties
Though online social network research has exploded during the past years, not
much thought has been given to the exploration of the nature of social links.
Online interactions have been interpreted as indicative of one social process
or another (e.g., status exchange or trust), often with little systematic
justification regarding the relation between observed data and theoretical
concept. Our research aims to breach this gap in computational social science
by proposing an unsupervised, parameter-free method to discover, with high
accuracy, the fundamental domains of interaction occurring in social networks.
By applying this method on two online datasets different by scope and type of
interaction (aNobii and Flickr) we observe the spontaneous emergence of three
domains of interaction representing the exchange of status, knowledge and
social support. By finding significant relations between the domains of
interaction and classic social network analysis issues (e.g., tie strength,
dyadic interaction over time) we show how the network of interactions induced
by the extracted domains can be used as a starting point for more nuanced
analysis of online social data that may one day incorporate the normative
grammar of social interaction. Our methods finds applications in online social
media services ranging from recommendation to visual link summarization.Comment: 10 pages, 8 figures, Proceedings of the 2014 ACM conference on Web
(WebSci'14
CONTENT-BASED COMMUNITY DETECTION IN SOCIAL CORPORA
Electronic communication media are a widespread means of interaction. They effect network relationships among people. Such networks provide connectivity but are often structured in clusters. Current cluster analysis in social corpora is mainly based on structural properties. This paper extends existing approaches with content-based cluster identification and community detection in social corpora. Following a design science methodology, we demonstrate our approach using a corporate e-mail dataset. After analyzing relationships between structural and content-based groups we conclude that our method contributes to detecting online communities, especially for large structural or smaller but dispersed topical groups
Does the public discuss other topics on climate change than researchers? A comparison of explorative networks based on author keywords and hashtags
Twitter accounts have already been used in many scientometric studies, but
the meaningfulness of the data for societal impact measurements in research
evaluation has been questioned. Earlier research focused on social media counts
and neglected the interactive nature of the data. We explore a new network
approach based on Twitter data in which we compare author keywords to hashtags
as indicators of topics. We analyze the topics of tweeted publications and
compare them with the topics of all publications (tweeted and not tweeted). Our
exploratory study is based on a comprehensive publication set of climate change
research. We are interested in whether Twitter data are able to reveal topics
of public discussions which can be separated from research-focused topics. We
find that the most tweeted topics regarding climate change research focus on
the consequences of climate change for humans. Twitter users are interested in
climate change publications which forecast effects of a changing climate on the
environment and to adaptation, mitigation and management issues rather than in
the methodology of climate-change research and causes of climate change. Our
results indicate that publications using scientific jargon are less likely to
be tweeted than publications using more general keywords. Twitter networks seem
to be able to visualize public discussions about specific topics.Comment: 31 pages, 1 table, and 7 figure
Dancing to the Partisan Beat: A First Analysis of Political Communication on TikTok
TikTok is a video-sharing social networking service, whose popularity is
increasing rapidly. It was the world's second-most downloaded app in 2019.
Although the platform is known for having users posting videos of themselves
dancing, lip-syncing, or showcasing other talents, user-videos expressing
political views have seen a recent spurt. This study aims to perform a primary
evaluation of political communication on TikTok. We collect a set of US
partisan Republican and Democratic videos to investigate how users communicated
with each other about political issues. With the help of computer vision,
natural language processing, and statistical tools, we illustrate that
political communication on TikTok is much more interactive in comparison to
other social media platforms, with users combining multiple information
channels to spread their messages. We show that political communication takes
place in the form of communication trees since users generate branches of
responses to existing content. In terms of user demographics, we find that
users belonging to both the US parties are young and behave similarly on the
platform. However, Republican users generated more political content and their
videos received more responses; on the other hand, Democratic users engaged
significantly more in cross-partisan discussions.Comment: Accepted as a full paper at the 12th International ACM Web Science
Conference (WebSci 2020). Please cite the WebSci version; Second version
includes corrected typo
Detecting Community Structure in Dynamic Social Networks Using the Concept of Leadership
Detecting community structure in social networks is a fundamental problem
empowering us to identify groups of actors with similar interests. There have
been extensive works focusing on finding communities in static networks,
however, in reality, due to dynamic nature of social networks, they are
evolving continuously. Ignoring the dynamic aspect of social networks, neither
allows us to capture evolutionary behavior of the network nor to predict the
future status of individuals. Aside from being dynamic, another significant
characteristic of real-world social networks is the presence of leaders, i.e.
nodes with high degree centrality having a high attraction to absorb other
members and hence to form a local community. In this paper, we devised an
efficient method to incrementally detect communities in highly dynamic social
networks using the intuitive idea of importance and persistence of community
leaders over time. Our proposed method is able to find new communities based on
the previous structure of the network without recomputing them from scratch.
This unique feature, enables us to efficiently detect and track communities
over time rapidly. Experimental results on the synthetic and real-world social
networks demonstrate that our method is both effective and efficient in
discovering communities in dynamic social networks
Dissemination of Health Information within Social Networks
In this paper, we investigate, how information about a common food born
health hazard, known as Campylobacter, spreads once it was delivered to a
random sample of individuals in France. The central question addressed here is
how individual characteristics and the various aspects of social network
influence the spread of information. A key claim of our paper is that
information diffusion processes occur in a patterned network of social ties of
heterogeneous actors. Our percolation models show that the characteristics of
the recipients of the information matter as much if not more than the
characteristics of the sender of the information in deciding whether the
information will be transmitted through a particular tie. We also found that at
least for this particular advisory, it is not the perceived need of the
recipients for the information that matters but their general interest in the
topic
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