Biblio
Filters: Keyword is automated social agent detection [Clear All Filters]
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2018. A Network Topology Approach to Bot Classification. Proceedings of the Annual Conference of the South African Institute of Computer Scientists and Information Technologists. :79-88.
Automated social agents, or bots are increasingly becoming a problem on social media platforms. There is a growing body of literature and multiple tools to aid in the detection of such agents on online social networking platforms. We propose that the social network topology of a user would be sufficient to determine whether the user is a automated agent or a human. To test this, we use a publicly available dataset containing users on Twitter labelled as either automated social agent or human. Using an unsupervised machine learning approach, we obtain a detection accuracy rate of 70%.
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2018. Deploying South African Social Honeypots on Twitter. Proceedings of the Annual Conference of the South African Institute of Computer Scientists and Information Technologists. :179-187.
Inspired by the simple, yet effective, method of tweeting gibberish to attract automated social agents (bots), we attempt to create localised honeypots in the South African political context. We produce a series of defined techniques and combine them to generate interactions from users on Twitter. The paper offers two key contributions. Conceptually, an argument is made that honeypots should not be confused for bot detection methods, but are rather methods to capture low-quality users. Secondly, we successfully generate a list of 288 local low quality users active in the political context.