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Type: Conference paper
Title: Real-time detection of content polluters in partially observable Twitter networks
Author: Nasim, M.
Nguyen, A.
Lothian, N.
Cope, R.
Mitchell, L.
Citation: Proceedings of the Web Conference 2018, as published in WWW '18 Companion: The 2018 Web Conference, 2018 / Champin, P.-A., Gandon, F.L., Lalmas, M., Ipeirotis, P.G. (ed./s), pp.1331-1339
Publisher: ACM
Issue Date: 2018
ISBN: 9781450356404
Conference Name: The Web Conference 2018 (23 Apr 2018 - 27 Apr 2018 : Lyon, France)
Editor: Champin, P.-A.
Gandon, F.L.
Lalmas, M.
Ipeirotis, P.G.
Statement of
Mehwish Nasim, Andrew Nguyen, Nick Lothian, Robert Cope, Lewis Mitchell
Abstract: Content polluters, or bots that hijack a conversation for political or advertising purposes are a known problem for event prediction, election forecasting and when distinguishing real news from fake news in social media data. Identifying this type of bot is particularly challenging, with state-of-the-art methods utilising large volumes of network data as features for machine learning models. Such datasets are generally not readily available in typical applications which stream social media data for real-time event prediction. In this work we develop a methodology to detect content polluters in social media datasets that are streamed in real-time. Applying our method to the problem of civil unrest event prediction in Australia, we identify content polluters from individual tweets, without collecting social network or historical data from individual accounts. We identify some peculiar characteristics of these bots in our dataset and propose metrics for identification of such accounts. We then pose some research questions around this type of bot detection, including: how good Twitter is at detecting content polluters and how well state-of-the-art methods perform in detecting bots in our dataset.
Keywords: Civil unrest; Social bots; Content polluters; Missing links; Twitter
Description: 9th International Workshop on Modeling Social Media (MSM 2018) Applying Machine Learning and AI for Modeling Social Media.
Rights: Copyright © 2018 by IW3C2 (International World Wide Web Conference Committee). The papers are published under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. Authors reserve their rights to disseminate the work on their personal and corporate Web sites with the appropriate attribution. In case of republication, reuse, etc., the following attribution should be used: “Published in WWW2018 Proceedings © 2018 International World Wide Web Conference Committee, published under Creative Commons CC By 4.0 License.”
DOI: 10.1145/3184558.3191574
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Mathematical Sciences publications

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