Year: 2018
Type: article
Abstract: In this paper, we present novel bot detection algorithms to identify Twitter bot accounts and to determine their prevalence in current online discourse. On social media, bots are ubiquitous. Bot accounts are problematic because they can manipulate information, spread misinformation, and promote unverified information, which can adversely affect public opinion on various topics, such as product sales and political campaigns. Detecting bot activity is complex because many bots are actively trying to avoid detection. We present a novel, complex machine learning algorithm utilizing a range of features including: length of user names, reposting rate, temporal patterns, sentiment expression, followers-to-friends ratio, and message variability for bot detection. Our novel technique for Twitter bot detection is effective at detecting bots with a 2.25% misclassification rate.
Language: English
Topic: Spam and Phishing Detection
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2893495107