SIN 2020: 13th International Conference on Security of Information and Networks, Article No.: 19. pp. 1–8.
ISSN/ISBN: Not available at this time. DOI: 10.1145/3433174.3433589
Abstract: The paper considers the task of bot detection in social networks. It checks the hypothesis that bots break Benford’s law much more often than users, so one can identify them. A bot detection approach is proposed based on experiments where the test results for bot datasets of different classes and real-user datasets of different communities are evaluated and compared. The experiments show that automatically controlled bots possibly can be identified by disagreement with Benford’s law, while human-orchestrated bots are not.
Bibtex:
@inproceedings{,
author = {Kalameyets, Maksim and Levshun, Dmitry and Soloviev, Sergei and Chechulin, Andrey and Kotenko, Igor},
title = {Social Networks Bot Detection Using Benford’s Law},
year = {2020},
isbn = {9781450387514},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://dl.acm.org/doi/abs/10.1145/3433174.3433589},
doi = {10.1145/3433174.3433589},
booktitle = {Proceedings of 13th International Conference on Security of Information and Networks},
articleno = {19},
numpages = {8},
location = {Merkez, Turkey},
series = {SIN 2020} }
Reference Type: Conference Paper
Subject Area(s): Computer Science