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Hi Doppelgänger : Towards Detecting Manipulation in News Comments

 
: Pennekamp, J.; Hohlfeld, O.; Henze, M.; Panchenko, A.

:

Amer-Yahia, S. ; Association for Computing Machinery -ACM-:
The Web Conference 2019 : Companion Proceedings of The 2019 World Wide Web Conference; May 13-17, 2019, San Francisco, CA, USA
New York: ACM, 2019
ISBN: 978-1-4503-6675-5
pp.197-205
World Wide Web Conference (WWW) <28, 2019, San Francisco/Calif.>
English
Conference Paper
Fraunhofer FKIE ()

Abstract
Public opinion manipulation is a serious threat to society, potentially influencing elections and the political situation even in established democracies. The prevalence of online media and the opportunity for users to express opinions in comments magnifies the problem. Governments, organizations, and companies can exploit this situation for biasing opinions. Typically, they deploy a large number of pseudonyms to create an impression of a crowd that supports specific opinions. Side channel information (such as IP addresses or identities of browsers) often allows a reliable detection of pseudonyms managed by a single person. However, while spoofing and anonymizing data that links these accounts is simple, a linking without is very challenging. In this paper, we evaluate whether stylometric features allow a detection of such doppelgängers within comment sections on news articles. To this end, we adapt a state-of-the-art doppelgänger detector to work on small texts (such as comments) and apply it on three popular news sites in two languages. Our results reveal that detecting potential doppelgängers based on linguistics is a promising approach even when no reliable side channel information is available. Preliminary results following an application in the wild shows indications for doppelgängers in real world data sets.

: http://publica.fraunhofer.de/documents/N-634306.html