Nepotistic relationships in Twitter and their impact on rank prestige algorithms

Citation: Daniel Gayo-Avello (2010) Nepotistic relationships in Twitter and their impact on rank prestige algorithms. arxiv (RSS)

Download: http://arxiv.org/abs/1004.0816

Tagged: Computer Science (RSS) spam (RSS), expert-finding (RSS), Twitter (RSS), spam-detection (RSS), influence metrics (RSS)

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Summary:

Compared 5 "prestige algorithms": PageRank, HITS, NodeRanking, TunkRank, TwitterRank on a large dataset: 28 million English tweets from 5 million users. TunkRank (description) (implementation) (API) (slides about), which discounts reciprocal follows, is best.

Theoretical and practical relevance:

Spam is an ongoing problem in social networks. Evaluating algorithms for filtering it is useful! Presented at CERI2010 as "Overcoming Spammers in Twitter" http://slidesha.re/a2RKs8 This is a paper with both extensive related work/perspective on the literature and significant quantitative analysis -- a rare combination! Methodology for identifying abusive users in Twitter may be useful elsewhere.

This was published in an open access journal.