A hashtag-based sub-event detection framework for social media. (September 2021)

• Record Type: Journal Article
• Authors: Lu, Guoming; Mu, Yaqiao; Gu, Jianbin; Kouassi, Franck A.P.; Lu, Chenxi; Wang, Ruozhou; Chen, Aiguo
• Is Part Of: Computers & electrical engineering. Volume 94(2021)
• Publication Date: 2021-09
• Subjects: Sub-event detection -- Encoder–decoder framework -- Text-CNN -- Social media mining; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2021.107317
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: Sub-event detection for social media is getting increasingly important because social media platforms have played key roles in disseminating vital information to the public. However, conventional methods fail to achieve high-quality analysis of events evolution due to the severe sparseness and noise of tweets data. In this paper, we propose an unsupervised sub-event detection model which learns rich information from hashtags with a Text-CNN model. Furthermore, a two steps training method leveraged by KL divergence is introduced to further reduce the negative influence of incoherent semantics. The experiments show that our method achieves very good performance on datasets of different languages. On the Chinese dataset, in terms of NMI, and BCubed F1 precision, our method has a significant increase of 5.1%, and 11.9%, respectively, over the baseline methods. In most cases, our method significantly improves the performance of sub-event detection compared with state-of-the-art methods. Graphical abstract: Highlights: A hashtag-based sub-event detection framework enhances semantic information utilization. An unsupervised sub-event representation with a Text-CNN model learns sub-event representation from tweets. A two-step training method reduces the influence of inconsistent information from tweets.