Characterizing the Structure of Twitter Network Through Socially-Aware Clustering of Users
Datum
2020
Autor:innen
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Verlag
University of Oregon
Zusammenfassung
Popular online social networks (OSN) such as Twitter form a networked system where millions of users interconnect and change information. Characterizing the structural properties of the resulting"relationship graph" among the OSN users is very informative but inherently challenging because of its huge size and complex connectivity patterns. This project explores a novel "socially-aware" approach to classify Twitter users and thus partition the structure of the Twitter relationship graph. To this end, we consider the top 10K most-followed Twitter users, called Twitter elite, and show that these users form coherent and socially meaningful communities, called Twitter elite communities. We define a "social interest vector" for each regular (i.e. non-elite) Twitter user where each element of this vector captures the user's relative level of interest to a specific elite community based on the fraction of her followings in that elite community. We then rely on this multi-dimensional measure of user's social interest to cluster millions of randomly selected Twitter users. We collect profile information, list of friends and followers along with available tweets for selected Twitter users in each cluster to assess (i) whether the resulting clusters of users are socially coherent, (ii) relatively degree of connectivity between different pairs of clusters, and (iii) the key social attributes of each cluster. Overall, our analysis will illustrate if elite communities can serve as "landmarks" to meaningfully classify regular Twitter users and characterize the structure of the Twitter network.
Beschreibung
Project files are comprised of 1 page pdf and presentation recording in mp4 format.
Schlagwörter
Computer Information Science, Data Mining, Twitter Networking, Social Network Analysis, Social Network Mining