Event Popularity Prediction Using Influential Hashtags From Social Media

• Published in: IEEE transactions on knowledge and data engineering Vol. 34; no. 10; pp. 4797 - 4811
• Main Authors: Chen, Xi, Zhou, Xiangmin, Chan, Jeffrey, Chen, Lei, Sellis, Timos, Zhang, Yanchun
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.10.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Correlation; Decision making; Digital media; Event popularity prediction; Feature extraction; Games; influential hashtag; Information dissemination; Media; Messages; Prediction models; Predictive models; Propagation; social media; Social networking (online); Social networks; Tagging; Tags
• ISSN: 1041-4347; 1558-2191
• DOI: 10.1109/TKDE.2020.3048428

Event popularity prediction over social media is crucial for estimating information propagation scope, decision making, and emergency prevention. However, existing approaches only focus on predicting the occurrences of single attribute such as a message, a hashtag or an image, which are not comprehensive enough for representing complex social event propagation. In this paper, we predict the event popularity, where an event is described as a set of messages containing multiple hashtags. We propose a novel hashtag-influence-based event popularity prediction by mining the impact of an influential hashtag set on the event propagation. Specifically, we first propose a hashtag-influence-based cascade model to select the influential hashtags over an event hashtag graph built by the pairwise hashtag similarity and the topic distribution of event-related hashtags. A novel measurement is proposed to identify the hashtag influence of an event over its content and social impacts. A hashtag correlation-based algorithm is proposed to optimize the seed selection in a greedy manner. Then, we propose an event-fitting boosting model to predict the event popularity by embedding the feature importance over events into the XGBOOST model. Moreover, we propose an event-structure-based method, which incrementally updates the prediction model over social streams. We have conducted extensive experiments to prove the effectiveness and efficiency of the proposed approach.