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- W2808133075 abstract "The use of bots in social media has become highly topical with the allegations that Russia actively uses such automated accounts to lend artificial amplification to desired messages, and as subterfuge in Western public debate. A key question analysts must answer, therefore, is which accounts are likely to be bots and how to separate these from ‘real people’. In practice, current approaches to answering this question are time-consuming and heuristics-based, and even in many academic approaches, heuristics still play a key role. The reliance on heuristics, however, means that adversaries can easily adapt as soon as they find out what the heuristics are. In this paper, we propose a new, more robust, unsupervised approach. While still using as input the same basic elements that inform heuristics-based approaches – account names, timing and frequency of posts, words used, etc. – the approach abstracts away from specific heuristics and potentially allows ‘unknown unknowns’ to be searched for – patterns that would be unlikely to be human-generated. By virtue of this, our approach has the potential to reduce the time and costs for those seeking to make sense of the information space, while simultaneously making it harder for adversaries to ‘game the system’. We demonstrate examples of the sort of output that our unsupervised approach to bot detection can yield." @default.
- W2808133075 created "2018-06-21" @default.
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- W2808133075 date "2018-01-01" @default.
- W2808133075 modified "2023-09-24" @default.
- W2808133075 title "Searching for Unknown Unknowns: Unsupervised Bot Detection to Defeat an Adaptive Adversary" @default.
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- W2808133075 doi "https://doi.org/10.1007/978-3-319-93372-6_39" @default.
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