| Title | A Network Topology Approach to Bot Classification |
| Publication Type | Conference Paper |
| Year of Publication | 2018 |
| Authors | Cornelissen, Laurenz A., Barnett, Richard J, Schoonwinkel, Petrus, Eichstadt, Brent D., Magodla, Hluma B. |
| Conference Name | Proceedings of the Annual Conference of the South African Institute of Computer Scientists and Information Technologists |
| Publisher | ACM |
| ISBN Number | 978-1-4503-6647-2 |
| Keywords | automated social agent detection, Human Behavior, human factors, pubcrawl, Scalability, Social Agents, social network theory, Twitter, unsupervised machine learning |
| Abstract | 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%. |
| DOI | 10.1145/3278681.3278692 |
| Citation Key | cornelissen_network_2018 |
A Network Topology Approach to Bot Classification