An influences-adapted two-phase approach to stance detection in the diachronic perspective

Abstract

Stance detection in social networks is still a challenging research topic, and in recent years, various methods and techniques have been proposed to deal with it. The main challenge in stance detection is that the content does not directly refer to the target. Most of the conducted research have used side information such as user interactions, target phrase, and external knowledge to increase the efficiency of their method. The stance of the users changes and evolves over time, so stance detection is proposed from the diachronic perspective. Two crucial challenges of detection in the diachronic perspective are to derive the evolution and adaptation of the detection system. In this article, we deal with stance detection from the diachronic perspective by extracting the evolution of the users' stances with the help of side information and adapting with evolution. Our approach is a two-phase method. In the first phase, by using subjectivity detection, the times when each user has taken a passive stance towards the target are determined. In the second phase, by using an influence model, the evolution of the active stance of the user is extracted, and its adaptation is made. The proposed method uses tweets from user’s home timeline and the user's favorite list as side information to discover the evolution. It can adapt itself to the dynamics of users' stances evolution by using the evolution of users' stances and automatically determine better time intervals for dividing tweets and improve user stance tracking. For the first time, we performed stance detection in the diachronic perspective on the SemEval-2016 Task 6A benchmark dataset and achieved Favg=78.18. Two other datasets are also used for experiments, and the results show that adaption increases the F-score between 3.99 and 9.72%.