Detecting anomalies in social network data consumption

As the popularity and usage of social media exploded over the years, understanding how social network users’ interests evolve gained importance in diverse fields, ranging from sociological studies to marketing. In this paper, we use two snapshots from the Twitter network and analyze data interest pa...

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Bibliographic Details
Published in:Social network analysis and mining 2014-12, Vol.4 (1), p.231, Article 231
Main Authors: Akcora, Cuneyt Gurcan, Carminati, Barbara, Ferrari, Elena, Kantarcioglu, Murat
Format: Article
Language:English
Subjects:
Applications of Graph Theory and Complex Networks
Collective behavior
Computer Science
Data Mining and Knowledge Discovery
Datasets
Economics
Evolution
Friendship
Game Theory
Humanities
Law
Marketing
Methodology of the Social Sciences
Original Article
Popularity
Social and Behav. Sciences
Social media
Social networks
Sociology
Statistics for Social Sciences
User behavior
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Summary:As the popularity and usage of social media exploded over the years, understanding how social network users’ interests evolve gained importance in diverse fields, ranging from sociological studies to marketing. In this paper, we use two snapshots from the Twitter network and analyze data interest patterns of users in time to understand individual and collective user behavior on social networks. Building topical profiles of users, we propose novel metrics to identify anomalous friendships, and validate our results with Amazon Mechanical Turk experiments. We show that although more than 80 % of all friendships on Twitter are created due to data interests, 83 % of all users have at least one friendship that can be explained neither by users’ past interest nor collective behavior of other similar users.
ISSN:1869-5450
1869-5469
DOI:10.1007/s13278-014-0231-3