Visible to the public A Network Topology Approach to Bot Classification

TitleA Network Topology Approach to Bot Classification
Publication TypeConference Paper
Year of Publication2018
AuthorsCornelissen, Laurenz A., Barnett, Richard J, Schoonwinkel, Petrus, Eichstadt, Brent D., Magodla, Hluma B.
Conference NameProceedings of the Annual Conference of the South African Institute of Computer Scientists and Information Technologists
ISBN Number978-1-4503-6647-2
Keywordsautomated social agent detection, Human Behavior, human factors, pubcrawl, Scalability, Social Agents, social network theory, Twitter, unsupervised machine learning
AbstractAutomated 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%.
Citation Keycornelissen_network_2018