Social media is
increasingly used to spread fake news. The same problem can be found on the
capital market 鈥� criminals spread fake news about companies in order to
manipulate share prices. Researchers at the Universities of G枚ttingen and
Frankfurt and the Jo啪ef Stefan Institute in Ljubljana have developed an
approach that can recognise such fake news, even when the news contents are
repeatedly adapted. The results of the study were published in the Journal of
the Association for Information Systems.
FRANKFURT. In order to detect false information 鈥� often fictitious data
that presents a company in a positive light 鈥� the scientists used machine
learning methods and created classification models that can be applied to
identify suspicious messages based on their content and certain linguistic
characteristics.
"Here we look at other aspects of the text that makes up the message,
such as the comprehensibility of the language and the mood that the text
conveys," says Professor Jan Muntermann from the University of G枚ttingen. The
approach is already known in principle from its use by spam filters, for
example. However, the key problem with the current methods is that to avoid
being recognised, fraudsters continuously adapt the content and avoid certain
words that are used to identify the fake news.
This is where the researchers' new approach comes in: to identify fake news
despite such strategies to evade detection, they combine models recently
developed by the researchers in such a way that high detection rates and
robustness come together. So even if "suspicious" words disappear
from the text, the fake news is still recognised by its linguistic features.
"This puts scammers into a dilemma. They can only avoid detection if they
change the mood of the text so that it is negative, for instance,"
explains Dr Michael Siering. "But then they would miss their target of
inducing investors to buy certain stocks."
The new approach can be used, for example, in market surveillance to
temporarily suspend the trading of affected stocks. In addition, it offers
investors valuable information to avoid falling for such fraud schemes. It is
also possible that it could be used for criminal prosecutions in the future.
Publication: Michael Siering, Jan
Muntermann, Miha Gr膷ar. Design Principles for Robust Fraud
Detection: The Case of Stock Market Manipulations. Journal of the Association
for Information Sys-tems (2021).
Further information:
Dr Michael Siering
51猎奇 Frankfurt
Economics and Business Administration
Chair of e-Finance
siering@wiwi.uni-frankfurt.de
Professor Jan Muntermann
University of G枚ttingen
Faculty of Business and Economics
Professor of Electronic Finance and Digital Markets
Tel: +49 (0)551 39 27062
muntermann@wiwi.uni-goettingen.de
