Peer Review #1 of "Application of interval type-2 fuzzy logic and type-1 fuzzy logic-based approaches to social networks for spam detection with combined feature capabilities (v0.1)"
2023
Background: Social networks are large platforms that allow their users to interact with each other on the Internet.Today, the widespread use of social networks has made them vulnerable to malicious use through different methods such as fake accounts and spam.As a result, many social network users are exposed to the harmful effects of spam accounts created by malicious people.Although Twitter, one of the most popular social networking platforms, uses spam filters to protect its users from the harmful effects of spam, these filters are insufficient to detect spam accounts that exhibit new methods and behaviours.That's why on social networking platforms like Twitter, it has become a necessity to use robust and more dynamic methods to detect spam accounts.Methods: Fuzzy logic (FL) based approaches, as they are the models such that generate results by interpreting the data obtained based on heuristics viewpoint according to past experiences, they can provide robust and dynamic solutions in spam detection, as in many application areas.For this purpose, a data set was created by collecting data on the twitter platform for spam detection.In the study, fuzzy logic-based classification approaches are suggested for spam detection.In the first stage of the proposed method, a data set with extracted attributes was obtained by applying normalization and crowdsourcing approaches to the raw data obtained from Twitter.In the next stage, as a process of the data preprocessing step, 6 attributes in the binary form in the data set were subjected to a rating-based transformation and combined with the other real-valued attribute to create a database to be used in spam detection.Classification process inputs were obtained by applying the fisher-score method, one of the commonly used filter-based methods, to the data set obtained in the second stage.In the last stage, the data were classified based on FL based approaches according to the obtained inputs.As FL approaches, 4 different Mamdani and