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.
|Publication status||Published - 31 Jul 2017|
|Event||2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining: ASONAM - Sydney, NSW, Australia, Sydney, Australia|
Duration: 31 Jul 2017 → 3 Aug 2017
|Conference||2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining|
|Period||31/07/17 → 3/08/17|