Fake followers are those Twitter accounts created to inflate the number of followers of a target account. Fake followers are dangerous to the social platform and beyond, since they may alter concepts like popularity and influence in the Twittersphere-hence impacting on economy, politics, and Society. In this paper, we provide several contributions. First, we review the most relevant existing criteria (proposed by Academia and Media) for anomalous Twitter accounts detection, and later we assess their capability to detect fake followers. In particular, we contribute with the creation of a gold standard of verified human, as well as with a set of known fake accounts. We test the above cited criteria against these two data sets, showing that the analyzed mechanisms provide unsatisfactory performance in revealing fake followers. Moreover, building upon these results, we also introduce a novel taxonomy to discriminate fake followers from legitimate ones and spammers. The findings reported in this paper, other than being supported by a thorough experimental methodology and being interesting on their own, also pave the way for further investigation.
Fake accounts detection on Twitter
R Di Pietro;S Cresci;M Petrocchi;A Spognardi;M Tesconi
2013
Abstract
Fake followers are those Twitter accounts created to inflate the number of followers of a target account. Fake followers are dangerous to the social platform and beyond, since they may alter concepts like popularity and influence in the Twittersphere-hence impacting on economy, politics, and Society. In this paper, we provide several contributions. First, we review the most relevant existing criteria (proposed by Academia and Media) for anomalous Twitter accounts detection, and later we assess their capability to detect fake followers. In particular, we contribute with the creation of a gold standard of verified human, as well as with a set of known fake accounts. We test the above cited criteria against these two data sets, showing that the analyzed mechanisms provide unsatisfactory performance in revealing fake followers. Moreover, building upon these results, we also introduce a novel taxonomy to discriminate fake followers from legitimate ones and spammers. The findings reported in this paper, other than being supported by a thorough experimental methodology and being interesting on their own, also pave the way for further investigation.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.