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The anatomy of online deception: what makes automated text convincing?

Abstract:
Technology is rapidly evolving, and with it comes increasingly sophisticated bots (i.e. software robots) which automatically produce content to inform, influence, and deceive genuine users. This is particularly a problem for social media networks where content tends to be extremely short, informally written, and full of inconsistencies. Motivated by the rise of bots on these networks, we investigate the ease with which a bot can deceive a human. In particular, we focus on deceiving a human into believing that an automatically generated sample of text was written by a human, as well as analysing which factors affect how convincing the text is. To accomplish this, we train a set of models to write text about several distinct topics, to simulate a bot's behaviour, which are then evaluated by a panel of judges. We find that: (1) typical Internet users are twice as likely to be deceived by automated content than security researchers; (2) text that disagrees with the crowd's opinion is more believably human; (3) light-hearted topics such as Entertainment are significantly easier to deceive with than factual topics such as Science; and (4) automated text on Adult content is the most deceptive regardless of a user's background.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1145/2851613.2851813

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More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Role:
Author


Publisher:
Association for Computing Machinery
Host title:
SAC '16 Proceedings of the 31st Annual ACM Symposium on Applied Computing
Pages:
1115-1120
Publication date:
2016-01-01
Acceptance date:
2015-11-30
DOI:
ISBN:
9781450337397


Keywords:
Pubs id:
pubs:614783
UUID:
uuid:a22ff288-8f27-457a-860b-c06337ee3d73
Local pid:
pubs:614783
Source identifiers:
614783
Deposit date:
2016-04-10

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