مقاله علمی و پژوهشی مطالعه و ارزیابی روش‌های پیش‌بینی انتخابات مبتنی بر کلان داده‌های شبکه‌های اجتماعی

Objective: A lot of raw data has been created in different fields within social networks, and researchers in different fields analyzed and evaluated these data according to the question in question. On the other hand, human beings have always wanted to know what will happen tomorrow or what other people think and decide.
Method: The key goal of this article is to study and evaluate election prediction methods based on big data of social networks. Therefore, this article studies and evaluates election forecasting methods based on abundant data in the social network in Iran and the world as a result of international experience and the author. Based on this, 25 cases of research on election prediction based on big data of social networks were studied and two key methods were extracted. The first method is to use the amount of data and count the content, and the other method is to analyze the sentiments of the published content (identification of liking, disliking, or approach measurement or evaluating feedback classified as positive, negative, or neutral responses).
Results: The findings from the evaluation of international research with the author’s experience indicate that although both methods have been responsive in some cases, the probability of response of the sentiment analysis method and the understanding of the content of the posts in FALB approach measurement seems to be more effective and accurate. arrive Also, by examining these articles, it seems that although the methods are slightly different, these two methods are common in these 25 conveyors that have been examined in different countries.
Conclusions: It can be concluded that elections and similar political and social events can be predicted, and even in a positive view, the decisions and feelings of a society can be predicted with the help of social network big data, but this means very high accuracy in It is not all the methods, but it can be the introduction of researches and experiences that will be useful for the development of using the big data of social networks.