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Do You Really Follow Them? Automatic Detection of Credulous Twitter Users

  • Alessandro BalestrucciEmail author
  • Rocco De Nicola
  • Marinella Petrocchi
  • Catia Trubiani
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11871)

Abstract

Online Social Media represent a pervasive source of information able to reach a huge audience. Sadly, recent studies show how online social bots (automated, often malicious accounts, populating social networks and mimicking genuine users) are able to amplify the dissemination of (fake) information by orders of magnitude. Using Twitter as a benchmark, in this work we focus on what we define credulous users, i.e., human-operated accounts with a high percentage of bots among their followings. Being more exposed to the harmful activities of social bots, credulous users may run the risk of being more influenced than other users; even worse, although unknowingly, they could become spreaders of misleading information (e.g., by retweeting bots). We design and develop a supervised classifier to automatically recognize credulous users. The best tested configuration achieves an accuracy of 93.27% and AUC-ROC of 0.93, thus leading to positive and encouraging results.

Keywords

Twitter Humans-bots interactions Gullibility Disinformation Social networks Data Mining Supervised learning 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Alessandro Balestrucci
    • 1
    Email author
  • Rocco De Nicola
    • 2
  • Marinella Petrocchi
    • 2
    • 3
  • Catia Trubiani
    • 1
  1. 1.Gran Sasso Science InstituteL’AquilaItaly
  2. 2.IMT School for Advanced StudiesLuccaItaly
  3. 3.Institute of Informatics and Telematics (IIT-CNR)PisaItaly

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