Contrast Pattern-Based Classification for Bot Detection on Twitter
By Octavio Loyola-González, Raúl Monroy, Jorge Rodríguez, Armando López-Cuevas, Javier Israel Mata-Sánchez. 2019
65 citations recorded by the source corpus.
Detecting non-human activity in social networks has become an area of great interest for both industry and academia. In this context, obtaining a high detection accuracy is not the only desired quality; experts in the application domain would also like having an understandable model, with which one may explain a decision. An explanatory decision model may help experts to consider, for example, taking legal action against an account that has displayed offensive behavior, or forewarning an account holder about suspicious activity. In this paper, we shall use a pattern-based classification mechanism to social bot detection, specifically for Twitter. Furthermore, we shall introduce a new feature model for social bot detection, which extends (part of) an existing model with features out of Twitter account usage and tweet content sentiment analysis. From our experimental results, we shall see that our mechanism outperforms other, state-of-the-art classifiers, not based on patterns; and that our feature model yields better classification results than others reported on in the literature.
Subfields: Computer Networks and Communications, Information Systems, Signal Processing
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Citations in the corpus
- Black-Box vs. White-Box: Understanding Their Advantages and Weaknesses From a Practical Point of View (2019, methodology): “Contrast pattern-based classifiers have been applied in several practical problems, such as bot detection on twitter [9], road safety [138], crime pattern [139], cerebrovascular examination [140], describing political figures [141], sales trends [142], detection of frequent alarm patterns [143], bot”