tags:: [[#zotero]]
date:: 7/2021
publisher:: IEEE
place:: "Athens, Greece"
conference-name:: 2021 International Conference on Computer Communications and Networks (ICCCN)
proceedings-title:: 2021 International Conference on Computer Communications and Networks (ICCCN)
isbn:: 978-1-66541-278-0
doi:: 10.1109/ICCCN52240.2021.9522339
title:: @It’s a Matter of Style: Detecting Social Bots through Writing Style Consistency
pages:: 1-9
item-type:: [[conferencePaper]]
access-date:: 2024-02-21T12:04:49Z
original-title:: It’s a Matter of Style: Detecting Social Bots through Writing Style Consistency
language:: en
url:: https://ieeexplore.ieee.org/document/9522339/
short-title:: It’s a Matter of Style
authors:: [[Matteo Cardaioli]], [[Mauro Conti]], [[Andrea Di Sorbo]], [[Enrico Fabrizio]], [[Sonia Laudanna]], [[Corrado A. Visaggio]]
library-catalog:: DOI.org (Crossref)
links:: [Local library](zotero://select/library/items/NLQQ48LE), [Web library](https://www.zotero.org/users/1039502/items/NLQQ48LE)

-
- [[Abstract]]
	- Social bots are computer algorithms able to produce content and interact with other users on social media autonomously, trying to emulate and possibly influence humans’ behavior. Indeed, bots are largely employed for malicious purposes, like spreading disinformation and conditioning electoral campaigns. Nowadays, bots’ capability of emulating human behaviors has become increasingly sophisticated, making their detection harder. In this paper, we aim at recognizing bot-driven accounts by evaluating the consistency of users’ writing style over time. In particular, we leverage the intuition that while bots compose posts according to fairly deterministic processes, humans are influenced by subjective factors (e.g., emotions) that can alter their writing style. To verify this assumption, by using stylistic consistency indicators, we characterize the writing style of more than 12,000 among bot-driven and human-operated Twitter accounts and find that statistically significant differences can be observed between the different types of users. Thus, we evaluate the effectiveness of different machine learning (ML) algorithms based on stylistic consistency features in discerning between human-operated and bot-driven Twitter accounts and show that the experimented ML algorithms can achieve high performance (i.e., F-measure values up to 98%) in social bot detection tasks.
- ### Attachments
	- [Cardaioli et al. - 2021 - It’s a Matter of Style Detecting Social Bots thro.pdf](zotero://select/library/items/LQLKJIZ3) {{zotero-imported-file LQLKJIZ3, "Cardaioli et al. - 2021 - It’s a Matter of Style Detecting Social Bots thro.pdf"}}