paper

Nepotistic Relationships in Twitter and their Impact on Rank Prestige Algorithms

arXiv:1004.0816 · doi:10.1016/j.ipm.2013.06.003

Abstract

Micro-blogging services such as Twitter allow anyone to publish anything, anytime. Needless to say, many of the available contents can be diminished as babble or spam. However, given the number and diversity of users, some valuable pieces of information should arise from the stream of tweets. Thus, such services can develop into valuable sources of up-to-date information (the so-called real-time web) provided a way to find the most relevant/trustworthy/authoritative users is available. Hence, this makes a highly pertinent question for which graph centrality methods can provide an answer. In this paper the author offers a comprehensive survey of feasible algorithms for ranking users in social networks, he examines their vulnerabilities to linking malpractice in such networks, and suggests an objective criterion against which to compare such algorithms. Additionally, he suggests a first step towards "desensitizing" prestige algorithms against cheating by spammers and other abusive users.

40 pages, 17 tables, 14 figures. Paper has been restructured, new section "3.2. The importance of reciprocal linking in Twitter spam" was added, experiments with verified accounts in addition to spammers have bee conducted to show performance with relevant users and not only regarding spam demotion

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