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Syst."],"published-print":{"date-parts":[[2022,10,31]]},"abstract":"<jats:p>User-generated contents (UGC) in social media are the direct expression of users\u2019 interests, preferences, and opinions. User behavior prediction based on UGC has increasingly been investigated in recent years. Compared to learning a person\u2019s behavioral patterns in each social media site separately, jointly predicting user behavior in multiple social media sites and complementing each other (cross-site user behavior prediction) can be more accurate. However, cross-site user behavior prediction based on UGC is a challenging task due to the difficulty of cross-site data sampling, the complexity of UGC modeling, and uncertainty of knowledge sharing among different sites. For these problems, we propose a Cross-Site Multi-Task (CSMT) learning method to jointly predict user behavior in multiple social media sites. CSMT mainly derives from the hierarchical attention network and multi-task learning. Using this method, the UGC in each social media site can obtain fine-grained representations in terms of words, topics, posts, hashtags, and time slices as well as the relevances among them, and prediction tasks in different social media sites can be jointly implemented and complement each other. By utilizing two cross-site datasets sampled from Weibo, Douban, Facebook, and Twitter, we validate our method\u2019s superiority on several classification metrics compared with existing related methods.<\/jats:p>","DOI":"10.1145\/3495530","type":"journal-article","created":{"date-parts":[[2022,1,17]],"date-time":"2022-01-17T06:04:22Z","timestamp":1642399462000},"page":"1-28","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Jointly Predicting Future Content in Multiple Social Media Sites Based on Multi-task Learning"],"prefix":"10.1145","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9109-4625","authenticated-orcid":false,"given":"Peng","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Computer Science, Fudan University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2335-144X","authenticated-orcid":false,"given":"Baoxi","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science, Fudan University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6633-4826","authenticated-orcid":false,"given":"Tun","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Computer Science, Fudan University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8122-6252","authenticated-orcid":false,"given":"Xianghua","family":"Ding","sequence":"additional","affiliation":[{"name":"School of Computer Science, Fudan University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1426-3210","authenticated-orcid":false,"given":"Hansu","family":"Gu","sequence":"additional","affiliation":[{"name":"Seattle, Bellevue, WA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2915-974X","authenticated-orcid":false,"given":"Ning","family":"Gu","sequence":"additional","affiliation":[{"name":"School of Computer Science, Fudan University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,1,11]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11257-012-9131-2"},{"key":"e_1_3_2_3_2","volume-title":"Proceedings of the 6th International Conference on Knowledge Discovery and Data Mining, Workshop on Social Network Mining and Analysis","author":"Bartunov Sergey","year":"2012","unstructured":"Sergey Bartunov, Anton Korshunov, Seung-Taek Park, Wonho Ryu, and Hyungdong Lee. 2012. 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