Echeverria, J;
Zhou, S;
(2017)
Discovery, retrieval, and analysis of the 'Star wars' botnet in twitter.
In: Diesner, Jana and Ferrari, Elena and Xu, Guandong, (eds.)
Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 (ASONAM '17).
(pp. pp. 1-8).
ACM: Sydney, Australia.
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Abstract
It is known that many Twitter users are bots, which are accounts controlled and sometimes created by computers. Twitter bots can send spam tweets, manipulate public opinion and be used for online fraud. Here we report the discovery, retrieval, and analysis of the ‘Star Wars’ botnet in Twitter, which consists of more than 350,000 bots tweeting random quotations exclusively from Star Wars novels. The botnet contains a single type of bot, showing exactly the same properties throughout the botnet. It is unusually large, many times larger than other available datasets. It provides a valuable source of ground truth for research on Twitter bots. We analysed and revealed rich details on how the botnet was designed and created. As of this writing, the Star Wars bots are still alive in Twitter. They have survived since their creation in 2013, despite the increasing efforts in recent years to detect and remove Twitter bots. We also reflect on the ‘unconventional’ way in which we discovered the Star Wars bots, and discuss the current problems and future challenges of Twitter bot detection.
| Type: | Proceedings paper |
|---|---|
| Title: | Discovery, retrieval, and analysis of the 'Star wars' botnet in twitter |
| Event: | IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 (ASONAM '17) |
| ISBN-13: | 9781450349932 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1145/3110025.3110074 |
| Publisher version: | https://doi.org/10.1145/3110025.3110074 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10056994 |
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