@inproceedings{ba893a7574a7407db15a385973bd142b,
title = "Early Detection of Multilingual Troll Accounts on Twitter",
abstract = "Internet troll farms have recently been employed as a powerful and prevailing weapon of information warfare. Even though different tactics may be utilized by different groups of state-sponsored trolls, our goal is to leverage identified troll data for revealing new emerging trolls generating multilingual content. In this work, we adopt a model agnostic meta-learning framework making use of previously released troll farm datasets for the early detection of newly-emerged troll accounts from identified or unidentified troll farms. The detection earliness of various models is evaluated using variable amounts of the earliest tweets from the tested accounts. To evaluate the proposed meta-model, we compare it to several classification models based on different types of account features. Our experiments demonstrate the effectiveness of the meta-model requiring as few as ten tweets to detect a troll account with an average accuracy of 94%.",
keywords = "Twitter, meta-learning, multilingual classification, troll account detection",
author = "Lin Miao and Mark Last and Marian Litvak",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 14th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2022 ; Conference date: 10-11-2022 Through 13-11-2022",
year = "2022",
month = jan,
day = "1",
doi = "10.1109/ASONAM55673.2022.10068705",
language = "English",
series = "Proceedings of the 2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2022",
publisher = "Institute of Electrical and Electronics Engineers",
pages = "378--382",
editor = "Jisun An and Chelmis Charalampos and Walid Magdy",
booktitle = "Proceedings of the 2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2022",
address = "United States",
}