| Title | Classification of Twitter Accounts into Automated Agents and Human Users |
| Publication Type | Conference Paper |
| Year of Publication | 2017 |
| Authors | Gilani, Zafar, Kochmar, Ekaterina, Crowcroft, Jon |
| Conference Name | Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 |
| Publisher | ACM |
| Conference Location | New York, NY, USA |
| ISBN Number | 978-1-4503-4993-2 |
| Keywords | account classification, automated agents, bot detection, Human Behavior, human factors, pubcrawl, Scalability, Social Agents, social network analysis |
| Abstract | Online social networks (OSNs) have seen a remarkable rise in the presence of surreptitious automated accounts. Massive human user-base and business-supportive operating model of social networks (such as Twitter) facilitates the creation of automated agents. In this paper we outline a systematic methodology and train a classifier to categorise Twitter accounts into 'automated' and 'human' users. To improve classification accuracy we employ a set of novel steps. First, we divide the dataset into four popularity bands to compensate for differences in types of accounts. Second, we create a large ground truth dataset using human annotations and extract relevant features from raw tweets. To judge accuracy of the procedure we calculate agreement among human annotators as well as with a bot detection research tool. We then apply a Random Forests classifier that achieves an accuracy close to human agreement. Finally, as a concluding step we perform tests to measure the efficacy of our results. |
| DOI | 10.1145/3110025.3110091 |
| Citation Key | gilani_classification_2017 |
Classification of Twitter Accounts into Automated Agents and Human Users