From Online Behaviours to Images: A Novel Approach to Social Bot Detection

Autor:	Di Paolo, Edoardo, Petrocchi, Marinella, Spognardi, Angelo
Rok vydání:	2023
Předmět:	Computer Science - Social and Information Networks Computer Science - Artificial Intelligence Computer Science - Computers and Society Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Online Social Networks have revolutionized how we consume and share information, but they have also led to a proliferation of content not always reliable and accurate. One particular type of social accounts is known to promote unreputable content, hyperpartisan, and propagandistic information. They are automated accounts, commonly called bots. Focusing on Twitter accounts, we propose a novel approach to bot detection: we first propose a new algorithm that transforms the sequence of actions that an account performs into an image; then, we leverage the strength of Convolutional Neural Networks to proceed with image classification. We compare our performances with state-of-the-art results for bot detection on genuine accounts / bot accounts datasets well known in the literature. The results confirm the effectiveness of the proposal, because the detection capability is on par with the state of the art, if not better in some cases. Comment: Accepted @ICCS2023, 23th International Conference on Computational Science, 3-5 July, 2023. The present version is a preprint
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2304.07535 Zobrazit plný text záznamu View this record from Arxiv