With the emergence of virtual social networks, predicting social events such as elections using social network data has attracted the attention of researchers. In this paper, three indicators for election prediction have been proposed. First, the tweets are grouped based on a specific time window. Next, the indicator values for each candidate in each time window are calculated based on the sentiment scores and re-tweet numbers. In fact, the indicators are calculated based on the ratio of features related to positive to negative sentiments. Finally, using the aging estimation method, the indicator values for each party on the election date are predicted. The party with larger predicted indicator values will be considered as the winner. Investigations into Twitter data related to 2016 and 2020 US presidential elections on a four-month time span indicate that the indicator values and elections can be predicted with a high accuracy.

Event detection using social media analysis has attracted researchers’ attention. Prediction of events especially in the management of social crises can be of particular significance. In this study, events are predicted through analyzing Twitter messages and examining the changes in the rate of Tweets in a specified subject. In the proposed method, the Tweets are initially preprocessed in consecutive fixed-length time windows. Tweets are then categorized using the non-negative matrix factorization analysis and the distance dependent Chinese restaurant process incremental clustering. The categorization results show that a high rate of Tweets entering a cluster represents the occurrence of a new event in near future. Finally, a description of the event is presented in the form of some frequent words in each cluster. In this paper, investigations on a Tweet dataset during a 6-month period indicate that the rate of sending Tweets about predictable events considerably changes before their occurrence. The use of this feature can make it possible to predict events with high degrees of precision.