Nepotistic Relationships in Twitter and their Impact on Rank Prestige Algorithms

TL;DR

This paper surveys user ranking algorithms on Twitter, examines their vulnerabilities to manipulation, and proposes methods to make these algorithms more resistant to spam and abuse, enhancing the reliability of influence measurement.

Contribution

It introduces a comprehensive review of ranking algorithms, analyzes their susceptibility to nepotistic linking, and proposes initial strategies to mitigate manipulation in social network prestige algorithms.

Findings

Identifies vulnerabilities of existing ranking algorithms to nepotistic relationships.

Proposes a criterion for evaluating algorithm robustness against manipulation.

Suggests initial approaches to reduce susceptibility to spam and abuse.

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.