2,479 research outputs found
Social influence analysis in microblogging platforms - a topic-sensitive based approach
The use of Social Media, particularly microblogging platforms such as Twitter, has proven to be an effective channel for promoting ideas to online audiences. In a world where information can bias public opinion it is essential to analyse the propagation and influence of information in large-scale networks. Recent research studying social media data to rank users by topical relevance have largely focused on the “retweet", “following" and “mention" relations. In this paper we propose the use of semantic profiles for deriving influential users based on the retweet subgraph of the Twitter graph. We introduce a variation of the PageRank algorithm for analysing users’ topical and entity influence based on the topical/entity relevance of a retweet relation. Experimental results show that our approach outperforms related algorithms including HITS, InDegree and Topic-Sensitive PageRank. We also introduce VisInfluence, a visualisation platform for presenting top influential users based on a topical query need
On the Predictability of Talk Attendance at Academic Conferences
This paper focuses on the prediction of real-world talk attendances at
academic conferences with respect to different influence factors. We study the
predictability of talk attendances using real-world tracked face-to-face
contacts. Furthermore, we investigate and discuss the predictive power of user
interests extracted from the users' previous publications. We apply Hybrid
Rooted PageRank, a state-of-the-art unsupervised machine learning method that
combines information from different sources. Using this method, we analyze and
discuss the predictive power of contact and interest networks separately and in
combination. We find that contact and similarity networks achieve comparable
results, and that combinations of different networks can only to a limited
extend help to improve the prediction quality. For our experiments, we analyze
the predictability of talk attendance at the ACM Conference on Hypertext and
Hypermedia 2011 collected using the conference management system Conferator
Bots increase exposure to negative and inflammatory content in online social systems
Societies are complex systems which tend to polarize into sub-groups of
individuals with dramatically opposite perspectives. This phenomenon is
reflected -- and often amplified -- in online social networks where, however,
humans are no more the only players, and co-exist alongside with social bots,
i.e., software-controlled accounts. Analyzing large-scale social data collected
during the Catalan referendum for independence on October 1, 2017, consisting
of nearly 4 millions Twitter posts generated by almost 1 million users, we
identify the two polarized groups of Independentists and Constitutionalists and
quantify the structural and emotional roles played by social bots. We show that
bots act from peripheral areas of the social system to target influential
humans of both groups, bombarding Independentists with violent contents,
increasing their exposure to negative and inflammatory narratives and
exacerbating social conflict online. Our findings stress the importance of
developing countermeasures to unmask these forms of automated social
manipulation.Comment: 8 pages, 5 figure
Network-based ranking in social systems: three challenges
Ranking algorithms are pervasive in our increasingly digitized societies,
with important real-world applications including recommender systems, search
engines, and influencer marketing practices. From a network science
perspective, network-based ranking algorithms solve fundamental problems
related to the identification of vital nodes for the stability and dynamics of
a complex system. Despite the ubiquitous and successful applications of these
algorithms, we argue that our understanding of their performance and their
applications to real-world problems face three fundamental challenges: (i)
Rankings might be biased by various factors; (2) their effectiveness might be
limited to specific problems; and (3) agents' decisions driven by rankings
might result in potentially vicious feedback mechanisms and unhealthy systemic
consequences. Methods rooted in network science and agent-based modeling can
help us to understand and overcome these challenges.Comment: Perspective article. 9 pages, 3 figure
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