{"type":"ScholarlyArticle","title":"LMPP: A Large Margin Point Process Combining Reinforcement and Competition for Modeling Hashtag Popularity","authors":[{"name":"Bidisha Samanta","slug":"bidisha-samanta","given":"Bidisha","family":"Samanta","affiliation":"Indian Institute of Technology Kharagpur , India"},{"name":"Abir De","slug":"abir-de","given":"Abir","family":"De","affiliation":"Indian Institute of Technology Kharagpur , India"},{"name":"Abhijnan Chakraborty","slug":"abhijnan-chakraborty","given":"Abhijnan","family":"Chakraborty","affiliation":"Indian Institute of Technology Kharagpur , India"},{"name":"Niloy Ganguly","slug":"niloy-ganguly","given":"Niloy","family":"Ganguly","affiliation":"Indian Institute of Technology Kharagpur , India"}],"abstract":"Predicting the popularity dynamics of Twitter hashtags has a broad spectrum of applications. Existing works have mainly focused on modeling the popularity of individual tweets rather than the popularity of the underlying hashtags. Hence, they do not consider several realistic factors for hashtag popularity. In this paper, we propose Large Margin Point Process (LMPP), a probabilistic framework that integrates hashtag-tweet influence and hashtag-hashtag competitions, the two factors which play  important roles in hashtag propagation. Furthermore, while considering the hashtag competitions, LMPP looks into the variations of popularity rankings of the competing hashtags across time. Extensive experiments on seven real datasets demonstrate that LMPP outperforms existing popularity prediction approaches by a significant margin. Going further, LMPP can accurately predict the relative rankings of competing hashtags, offering additional advantage over the state-of-the-art baselines.","doi":"10.24963/ijcai.2017/373","url":"https://ijcai.org/proceedings/2017/373","pdf":"https://ijcai.org/proceedings/2017/0373.pdf","markdown":"https://ijcai.org/proceedings/2017/373.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/373","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Machine Learning S-Z","track":"Main track","number":373,"pages":{"first":2679,"last":2685},"keywords":["Machine Learning: Data Mining","Multidisciplinary Topics and Applications: AI and Social Sciences","Machine Learning: Time-series/Data Streams"],"videos":[],"references":[{"position":1,"raw":"[Bao et al., 2015] Peng Bao, Hua-Wei Shen, Xiaolong Jin, and Xue-Qi Cheng. 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