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Published in:
2011 | OriginalPaper | Chapter
Identifying Automatic Posting Systems in Microblogs
Authors : Gustavo Laboreiro, Luís Sarmento, Eugénio Oliveira
Published in: Progress in Artificial Intelligence
Publisher: Springer Berlin Heidelberg
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In this paper we study the problem of identifying systems that automatically inject non-personal messages in micro-blogging message streams, thus potentially biasing results of certain information extraction procedures, such as opinion-mining and trend analysis. We also study several classes of features, namely features based on the time of posting, the client used to post, the presence of links, the user interaction and the writing style. This last class of features, that we introduce here for the first time, is proved to be a top performer, achieving accuracy near the 90%, on par with the best features previously used for this task.