A Temporal evolving graph convolutional network for Public opinion prediction in emergencies

Abstract:

Public opinion prediction is one of the key solutions to improve the ability to guide public opinion in emergencies. However, most of the existing public opinion prediction work focuses on the trend indicator or sentiment polarity of events ,while little attention paid to the prediction of hot words and topics in specific events. In this paper, a temporal evolving graph convolutional network for public opinion prediction in emergencies is proposed, in which the hot words associated with specific events are taken as the object of public opinion prediction. Our approach combines evolving graph convolutional network with gated recurrent unit: the former is used to learn the dynamic spatial correlation between words and the latter is used to capture the temporal changes of words, the hot words of an emergency in the next time period is then predicted through full connection layer output. To validate the proposed method, we selected discussion texts related to two emergencies on Weibo as the dataset, and conducted comparative experiments with two existing hot word prediction methods. The results show that our method achieved higher precision, recall, and F1-score in both emergencies, with precision of 51.21% and 50.98%, recall of 50.17% and 48.15%, and F1-scores of 50.68% and 49.52%, respectively. These results demonstrate that our proposed method is effective inpredicting public opinion during emergencies