Nepotistic relationships in Twitter and their impact on rank prestige algorithms

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Citation: Daniel Gayo-Avello (2010) Nepotistic relationships in Twitter and their impact on rank prestige algorithms. arxiv (RSS)
Internet Archive Scholar (search for fulltext): Nepotistic relationships in Twitter and their impact on rank prestige algorithms
Download: http://arxiv.org/abs/1004.0816
Tagged: Computer Science (RSS) spam (RSS), expert-finding (RSS), Twitter (RSS), spam-detection (RSS), influence metrics (RSS)

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.

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