Finding Diachronic Like‐Minded Users. (28th April 2017)

• Record Type: Journal Article
• Authors: Fani, Hossein; Bagheri, Ebrahim; Zarrinkalam, Fattane; Zhao, Xin; Du, Weichang
• Is Part Of: Computational intelligence. Volume 34:Number 1(2018)
• Page Start: 124
• Page End: 144
• Publication Date: 2017-04-28
• Subjects: community detection -- time series analysis -- topic detection; Artificial intelligence -- Periodicals; Computational linguistics -- Periodicals; 006.3
• DOI: 10.1111/coin.12117
• Languages: English
• ISSNs: 0824-7935

Abstract

Abstract : User communities in social networks are usually identified by considering explicit structural social connections between users. While such communities can reveal important information about their members such as family or friendship ties and geographical proximity, just to name a few, they do not necessarily succeed at pulling like‐minded users that share the same interests together. Therefore, researchers have explored the topical similarity of social content to build like‐minded communities of users. In this article, following the topic‐based approaches, we are interested in identifying communities of users that share similar topical interests with similar temporal behavior. More specifically, we tackle the problem of identifying temporal (diachronic) topic‐based communities, i.e., communities of users who have a similar temporal inclination toward emerging topics. To do so, we utilize multivariate time series analysis to model the contributions of each user toward emerging topics. Further, our modeling is completely agnostic to the underlying topic detection method. We extract topics of interest by employing seminal topic detection methods; one graph‐based and two latent Dirichlet allocation‐based methods. Through our experiments on Twitter data, we demonstrate the effectiveness of our proposed temporal topic‐based community detection method in the context of news recommendation, user prediction, and document timestamp prediction applications, compared with the nontemporal as well as the state‐of‐the‐art temporal approaches.