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Unsupervised Verification of Fake News by Public Opinion
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SYSNO ASEP 0543203 Document Type V - Research Report R&D Document Type The record was not marked in the RIV Title Unsupervised Verification of Fake News by Public Opinion Author(s) Grim, Jiří (UTIA-B) RID, ORCID Number of authors 1 Issue data Praha: UTIA, 2021 Series Research Report Series number 2390 Number of pages 13 s. Publication form Print - P Language eng - English Country CZ - Czech Republic Keywords weighted voting ; unsupervised optimization Subject RIV IN - Informatics, Computer Science OECD category Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) Institutional support UTIA-B - RVO:67985556 Annotation In this paper we discuss a simple way to evaluate the messages in social networks automatically, without any special content analysis or external intervention. We presume, that a large number of social network participants is capable of a relatively reliable evaluation of materials presented in the network. Considering a simple binary evaluation scheme (like/dislike), we propose a transparent algorithm with the aim to increase the voting power of reliable network members by means of weights. The algorithm supports the votes which correlate with the more reliable weighted majority and, in turn, the modified weights improve the quality of the weighted majority voting. In this sense the weighting is controlled only by a general coincidence of voting members while the specific content of messages is unimportant. The iterative optimization procedure is unsupervised and does not require any external intervention with only one exception, as discussed in Sec. 5.2 .
In simulation experiments the algorithm nearly exactly identifies the reliable members by means of weights. Using the reinforced weights we can compute for a new message the weighted sum of votes as a quantitative measure of its positive or negative nature. In this way any fake news can be recognized as negative and indicated as controversial. The accuracy of the resulting weighted decision making was essentially higher than a simple majority voting and has been considerably robust with respect to possible external manipulations.
The main motivation of the proposed algorithm is its application in a large social network. The content of evaluated messages is unimportant, only the related decision making of participants is registered and compared with the weighted vote with the aim to identify the most reliable voters. A large number of participants and communicated messages should enable to design a reliable and robust weighted voting scheme. Ideally the resulting weighted vote should provide a generally acceptable emotional feedback for network participants and could be used to indicate positive or controversial news in a suitably chosen quantitative way. The optimization algorithm has to be simple, transparent and intuitive to make the weighted vote well acceptable as a general evaluation tool.
Workplace Institute of Information Theory and Automation Contact Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Year of Publishing 2022
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