Publications

Finding Diachronic Like-Minded Users

Hossein Fani and Ebrahim Bagheri and Fattane Zarrinkalam and Xin Zhao and Weichang Du
Reference:
Hossein Fani; Ebrahim Bagheri; Fattane Zarrinkalam; Xin Zhao and Weichang Du Finding Diachronic Like-Minded Users. In Computational Intelligence: An International Journal, 2017.
Links to Publication: [doi][www][pdf]
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 likeminded 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 paper, 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 towards emerging topics. To do so, we utilize multivariate time series analysis to model the contributions of each user towards emerging topics. Further, our modeling is completely agnostic to the topic detection method. We extract topics of interest by employing seminal topic detection methods; one graph-based and two LDA-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 to the non-temporal as well as the state-of-the-art temporal approaches.
Bibtex Entry:
@article{DBLP:journals/ci/FaniBZZD17, author = {Hossein Fani and Ebrahim Bagheri and Fattane Zarrinkalam and Xin Zhao and Weichang Du}, title = {Finding Diachronic Like-Minded Users}, journal = {Computational Intelligence: An International Journal}, issn = {1467-8640}, year = {2017}, url = {http://onlinelibrary.wiley.com/doi/10.1111/coin.12117/full}, doi = {10.1111/coin.12117}, keywords = {community detection, time series analysis, topic detection}, publisher = {Wiley Periodicals, Inc.} webpdf = {http://ls3.rnet.ryerson.ca/papers/FINDING_DIACHRONIC_LIKE-MINDED_USERS-coin2017.pdf}, 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 likeminded 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 paper, 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 towards emerging topics. To do so, we utilize multivariate time series analysis to model the contributions of each user towards emerging topics. Further, our modeling is completely agnostic to the topic detection method. We extract topics of interest by employing seminal topic detection methods; one graph-based and two LDA-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 to the non-temporal as well as the state-of-the-art temporal approaches.} }




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