Abstract The prediction of election results has always been an intriguing concern for the general public and experts in various fields. Nowadays, with the expansion of social networks and the expression of people’s opinions and beliefs on these platforms, combined with algorithms and methods for analyzing messages and relationships within these networks, it is possible to assess the inclinations of individuals and, consequently, the potential election results. Based on the research conducted in this paper, election prediction methods can be broadly categorized into four main groups: sentiment analysis, stance detection, social network analysis, and traditional methods. Considering that the focus of this paper is on algorithms for analyzing social networks, traditional methods are briefly mentioned, while detailed approaches are explored for the other three categories. According to the results obtained from this research, simultaneous use of sentiment analysis or stance detection alongside structural analysis of social networks leads to high accuracy in predicting election results. Therefore, in conclusion, along with summarizing the presented content, suggestions for selecting a thesis topic have been provided.
Nikraftar et al. (Wed,) studied this question.
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