Abstract
The article focuses on solving an important problem of detecting suspicious reviewers in online discussions on social networks. We have concentrated on a special type of suspicious authors, on trolls. We have used methods of machine learning for generation of detection models to discriminate a troll reviewer from a common reviewer, but also methods of sentiment analysis to recognize the sentiment typical for troll’s comments. The sentiment analysis can be provided also using machine learning or lexicon-based approach. We have used lexicon-based sentiment analysis for its better ability to detect a dictionary typical for troll authors. We have achieved Accuracy = 0.95 and F1 = 0.80 using sentiment analysis. The best results using machine learning methods were achieved by support vector machine, Accuracy = 0.986 and F1 = 0.988, using a dataset with the set of all selected attributes. We can conclude that detection model based on machine learning is more successful than lexicon-based sentiment analysis, but the difference in accuracy is not so large as in F1 measure.
Highlights
Sensors 2022, 22, 155. https://The detection of suspicious online reviewers is important in context of revealing antisocial behavior on social networks
We have focused at first on classic methods as naïve Bayes, decision trees and support vector machines
Support vector machines are often used because of they form a mathematical model of hyperplane dividing two classes
Summary
The detection of suspicious online reviewers is important in context of revealing antisocial behavior on social networks. The antisocial behavior carried out by trolls and other suspicious reviewers can harm web users and even undermine democracy in many countries. The purpose of the work is to offer an effective model for detection of trolls in online space and to join our effort with the effort of many web platforms, which are trying hard today to keep trolls out of business. The significance of the paper is in comparison of various methods of machine learning and sentiment analysis for the selection of the best method for the detection model building. The specific scope of this article is to propose different approaches to the generation of a model for troll recognition in various social networks and to compare these approaches
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