Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/51925
Title: Real-time Detection of Content Polluters in Partially Observable Twitter Networks
Contributor(s): Nasim, Mehwish (author); Nguyen, Andrew (author); Lothian, Nick (author); Cope, Robert  (author)orcid ; Mitchell, Lewis (author)
Publication Date: 2018-04
Open Access: Yes
DOI: 10.1145/3184558.3191574Open Access Link
Handle Link: https://hdl.handle.net/1959.11/51925
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.

Publication Type: Conference Publication
Conference Details: WWW 2018: Web Conference 2018, Lyon, France, 23rd - 27th April, 2018
Source of Publication: WWW '18: Companion Proceedings of the The Web Conference 2018, p. 1331-1339
Publisher: International World Wide Web Conferences Steering Committee
Place of Publication: Geneva, Switzerland
Fields of Research (FoR) 2020: 490508 Statistical data science
460501 Data engineering and data science
Socio-Economic Objective (SEO) 2020: 220502 Internet, digital and social media
Peer Reviewed: Yes
HERDC Category Description: E1 Refereed Scholarly Conference Publication
Description: This paper was presented at a subsection of the Conference: Track: 9th International Workshop on Modeling Social Media (MSM 2018) Applying Machine Learning and AI for Modeling Social Media.
Appears in Collections:Conference Publication
School of Science and Technology

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