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The Anatomy of Online Deception: What Makes Automated Text Convincing?

Everett, Richard, Nurse, Jason R. C., Erola, Arnau (2016) The Anatomy of Online Deception: What Makes Automated Text Convincing? In: 31st Annual ACM Symposium on Applied Computing (SAC). (doi:10.1145/2851613.2851813)

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https://doi.org/10.1145/2851613.2851813

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.

Item Type: Conference or workshop item (Paper)
DOI/Identification number: 10.1145/2851613.2851813
Subjects: Q Science
T Technology
Divisions: Faculties > Sciences > School of Computing
Faculties > Sciences > School of Computing > Security Group
Depositing User: Jason Nurse
Date Deposited: 03 Jul 2018 13:24 UTC
Last Modified: 01 Aug 2019 10:43 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/67493 (The current URI for this page, for reference purposes)
Nurse, Jason R. C.: https://orcid.org/0000-0003-4118-1680
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