Fame for sale: Efficient detection of fake Twitter followers

By Stefano Cresci, Roberto Di Pietro, Marinella Petrocchi, Angelo Spognardi, Maurizio Tesconi. Decision Support Systems, 2015

460 citations recorded by the source corpus.

Fake followers are those Twitter accounts specifically created to inflate the number of followers of a target account. Fake followers are dangerous for 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 contribute along different dimensions. First, we review some of the most relevant existing features and rules (proposed by Academia and Media) for anomalous Twitter accounts detection. Second, we create a baseline dataset of verified human and fake follower accounts. Such baseline dataset is publicly available to the scientific community. Then, we exploit the baseline dataset to train a set of machine-learning classifiers built over the reviewed rules and features. Our results show that most of the rules proposed by Media provide unsatisfactory performance in revealing fake followers, while features proposed in the past by Academia for spam detection provide good results. Building on the most promising features, we revise the classifiers both in terms of reduction of overfitting and cost for gathering the data needed to compute the features. The final result is a novel Class A classifier, general enough to thwart overfitting, lightweight thanks to the usage of the less costly features, and still able to correctly classify more than 95% of the accounts of the original training set. We ultimately perform an information fusion-based sensitivity analysis, to assess the global sensitivity of each of the features employed by the classifier.The findings reported in this paper, other than being supported by a thorough experimental methodology and interesting on their own, also pave the way for further investigation on the novel issue of fake Twitter followers.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/c8b71ccf612feb28e4c6d4d0115b4ce16569354c

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Citations in the corpus

The role of engagement in travel influencer marketing: the perspectives of dual process theory and the source credibility model (2020, methodology): “Particularly, Experiment 2 used the concept of fake followers to temporarily increase the skeptical mindset of people, as fake followers have become a major data fraud issue in the traveller influencer marketing industry (Cresci et al., 2015).”
The Paradigm-Shift of Social Spambots: Evidence, Theories, and Tools for the Arms Race (2017, background): “Fake followers are another kind of malicious accounts that recently gained interest both from platform administrators and from the scientific world [13].”
Combating Fake News (2019, background): “Although these are extremely important topics in their own right, much like the detection of scam email [98], fake followers [23], or false web links [57], we chose to keep the discussion focused on fake news and provide detailed discussions of the techniques and their limitations.”
Finding and Characterizing Information Warfare Campaigns (2020, methodology): “Cresci 2015 Manually annotated by Cresci et al in 2015 [70] X X X”
Fake Twitter followers detection by denoising autoencoder (2017, methodology): “In [8], authors reported the results of a project aimed to collect data of Twitter verified real users.”
Arming the public with artificial intelligence to counter social bots (2019, background): “A more extreme example of these kinds of bots are identity thieves: they copy usernames, proﬁle information, and pictures of other accounts and use them as their own, making only small changes (Cresci et al., 2015).”