2,140 research outputs found
Followers Are Not Enough: A Question-Oriented Approach to Community Detection in Online Social Networks
Community detection in online social networks is typically based on the
analysis of the explicit connections between users, such as "friends" on
Facebook and "followers" on Twitter. But online users often have hundreds or
even thousands of such connections, and many of these connections do not
correspond to real friendships or more generally to accounts that users
interact with. We claim that community detection in online social networks
should be question-oriented and rely on additional information beyond the
simple structure of the network. The concept of 'community' is very general,
and different questions such as "whom do we interact with?" and "with whom do
we share similar interests?" can lead to the discovery of different social
groups. In this paper we focus on three types of communities beyond structural
communities: activity-based, topic-based, and interaction-based. We analyze a
Twitter dataset using three different weightings of the structural network
meant to highlight these three community types, and then infer the communities
associated with these weightings. We show that the communities obtained in the
three weighted cases are highly different from each other, and from the
communities obtained by considering only the unweighted structural network. Our
results confirm that asking a precise question is an unavoidable first step in
community detection in online social networks, and that different questions can
lead to different insights about the network under study.Comment: 22 pages, 4 figures, 1 table
Analysis of Retweeting Behavior Using Topic Models
Igapäevase eluga põimunud virtuaalsed sotsiaalvõrgustikud omavad üha kasvavat rolli
sotsiaalsetes ja ärilistes nähtustes. Microblogging teenused nagu Twitter mängivad
olulist rolli Interneti infovahetuses, muutes võimalikuks sõnumite leviku minutitega.
Käesolevas uurimuses analüüsitakse korduvalt edastatavate sõnumite (retweet) levikut
Twitteris. Kasutades Latent Dirichlet Allocation mudelit teemade eristamiseks näitame,
et kasutajate ja sõnumites sisalduvate teemade vaheline suhteline kaugus on lühem
korduvalt edastatavatel sõnumitel. Kasutades otsustuspuid hindame teemapõhise retweet
mudeli täpsust ja kasulikkust. Töö tulemusena näitame, et teemapõhine mudel on
tugevama ennustusvõimega võrreldes baseline mudelitega, millest lähtuvalt väidame, et
antud lähenemine on sobiv korduvalt edastavate sõnumite ennustamiseks ning edasiseks
arenduseks.Social networks are nowadays a constant presence in our lives and increasingly have a role in
important social and commercial phenomena. Microblogging services such as Twitter appear to
play an important role in the process of information dissemination on the Internet making it
possible for messages to spread virally in a matter of minutes. In this research work we study the
mechanism of re-broadcasting (called “retweeting”) information on Twitter; specifically we use
Latent Dirichlet Allocation to analyze users and messages in terms of the topics that compose
their text bodies and by means of ANOVA we are able to show that the topical distance between
users and messages is shorter for tweets that are retweeted than for those that are not. Using
Decision Tree learning we build several models in order to assess the accuracy and usefulness of
our topic-based model of retweeting. Our results show that our topic-based model slightly
outperforms a baseline prediction measure, so we conclude that such model is indeed a valid
option to consider for predicting retweet behavior with possibilities open for improvement
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