Papers › How many bots are you following?

How many bots are you following?

15 Jan 2020arXiv:2001.05222links table onlyarchive 2025-07-28

Alessandro Balestrucci

The archive published only this paper's code-link row. Authors, date and abstract are from arXiv's metadata (CC0), read from the Kaggle arXiv metadata snapshot of 2026-09-12 where its title matched the archive's; the title is the archive's.

Social Media are evolving as a pervasive source of news able to reach a larger audience through their spreading power. The main drawback is given by the presence of malicious accounts, known as social bots, which are often used to diffuse misleading information. Social bots are automated accounts whose goal is to interact with humans and influence them. Starting from the definition of credulous (i.e., human accounts with a high percentage of bot friends among their followees, in this work we aim to single out a regression model to derive, with an acceptable margin of error, the percentage of bot-followees of a human-operated account. The advantage lies in knowing, as a preventive measure, which users may be the target of bots' activities, hence more exposed to the misleading/unreliable content. Our results showed that the best regression model achieves a Mean Absolute Error of 3.62% and a Root Mean Squared Error of 5.96%, thus encouraging further research in this direction.

PaperPDFCode

Code

Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.

Code Syntology ran Syntology

Not run by Syntology. Nothing on this page verifies that the listed code works.

Results from the paper archive 2025-07-28

No leaderboard rows for this paper in the archive.

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections