A Framework for Unsupervised Spam Detection in Social Networking Sites

A Framework for Unsupervised Spam Detection in Social Networking Sites

Title A Framework for Unsupervised Spam Detection in Social Networking Sites
Publication Type Conference Paper
Year of Publication 2012
Date Published 04/2012
Authors Bosma M, Meij E, Weerkamp W
Conference Name ECIR 2012: 34th European Conference on Information Retrieval
Pagination 364-375
Abstract

Social networking sites offer users the option to submit user spam reports for a given message, indicating this message is inappropriate. In this paper we present a framework that uses these user spam reports for spam detection. The framework is based on the HITS web link analysis framework and is instantiated in three models. The models subsequently introduce propagation between messages reported by the same user, messages authored by the same user, and messages with similar content. Each of the models can also be converted to a simple semi-supervised scheme. We test our models on data from a popular social network and compare the models to two baselines, based on message content and raw report counts. We find that our models outperform both baselines and that each of the additions (reporters, authors, and similar messages) further improves the performance of the framework.

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