{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T13:19:37Z","timestamp":1771075177487,"version":"3.50.1"},"reference-count":45,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2012,10,1]],"date-time":"2012-10-01T00:00:00Z","timestamp":1349049600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Manage. Inf. Syst."],"published-print":{"date-parts":[[2012,10]]},"abstract":"<jats:p>\n            Real-time microblogging systems such as Twitter offer users an easy and lightweight means to exchange information. Instead of writing formal and lengthy messages, microbloggers prefer to frequently broadcast several short messages to be read by other users. Only when messages are interesting, are they propagated further by the readers. In this article, we examine user behavior relevant to information propagation through microblogging. We specifically use retweeting activities among Twitter users to define and model\n            <jats:italic>originating<\/jats:italic>\n            and\n            <jats:italic>promoting<\/jats:italic>\n            behavior. We propose a basic model for measuring the two behaviors, a\n            <jats:italic>mutual dependency<\/jats:italic>\n            model, which considers the mutual relationships between the two behaviors, and a\n            <jats:italic>range-based<\/jats:italic>\n            model, which considers the\n            <jats:italic>depth<\/jats:italic>\n            and\n            <jats:italic>reach<\/jats:italic>\n            of users\u2019 original tweets. Next, we compare the three behavior models and contrast them with the existing work on modeling influential Twitter users. Last, to demonstrate their applicability, we further employ the behavior models to detect interesting events from sudden changes in aggregated information propagation behavior of Twitter users. The results will show that the proposed behavior models can be effectively applied to detect interesting events in the Twitter stream, compared to the baseline tweet-based approaches.\n          <\/jats:p>","DOI":"10.1145\/2361256.2361258","type":"journal-article","created":{"date-parts":[[2014,4,23]],"date-time":"2014-04-23T13:52:04Z","timestamp":1398261124000},"page":"1-30","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":28,"title":["Who is Retweeting the Tweeters? Modeling, Originating, and Promoting Behaviors in the Twitter Network"],"prefix":"10.1145","volume":"3","author":[{"given":"Palakorn","family":"Achananuparp","sequence":"first","affiliation":[{"name":"Singapore Management University"}]},{"given":"Ee-Peng","family":"Lim","sequence":"additional","affiliation":[{"name":"Singapore Management University"}]},{"given":"Jing","family":"Jiang","sequence":"additional","affiliation":[{"name":"Singapore Management University"}]},{"given":"Tuan-Anh","family":"Hoang","sequence":"additional","affiliation":[{"name":"Singapore Management University"}]}],"member":"320","published-online":{"date-parts":[[2012,10]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/1341531.1341559"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/1148170.1148177"},{"key":"e_1_2_1_3_1","volume-title":"Proceedings of the National AAAI Conference on Weblogs and Social Media (ICWSM). 434--437","author":"Asur S.","unstructured":"Asur , S. , Huberman , B. A. , Szabo , G. , and Wang , C . 2011. Trends in social media: Persistence and decay . In Proceedings of the National AAAI Conference on Weblogs and Social Media (ICWSM). 434--437 . Asur, S., Huberman, B. A., Szabo, G., and Wang, C. 2011. Trends in social media: Persistence and decay. In Proceedings of the National AAAI Conference on Weblogs and Social Media (ICWSM). 434--437."},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/1718487.1718524"},{"key":"e_1_2_1_5_1","unstructured":"Berger J. A. and Milkman K. L. 2009. Social Transmission Emotion and the Virality of Online Content. Social Science Research Library (SSRN) eLibrary. Berger J. A. and Milkman K. L. 2009. Social Transmission Emotion and the Virality of Online Content . Social Science Research Library (SSRN) eLibrary."},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/1277741.1277814"},{"key":"e_1_2_1_7_1","volume-title":"Proceedings of the National AAAI Conference on Weblogs and Social Media (ICWSM).","author":"Cha M.","unstructured":"Cha , M. , Haddadi , H. , Benevenuto , F. , and Gummadi , K. P . 2010. Measuring user influence in Twitter: The million follower fallacy . In Proceedings of the National AAAI Conference on Weblogs and Social Media (ICWSM). Cha, M., Haddadi, H., Benevenuto, F., and Gummadi, K. P. 2010. Measuring user influence in Twitter: The million follower fallacy. In Proceedings of the National AAAI Conference on Weblogs and Social Media (ICWSM)."},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/1810617.1810674"},{"key":"e_1_2_1_9_1","volume-title":"Statistical Methods For Rates and Proportions","author":"Fleiss J. L.","unstructured":"Fleiss , J. L. 1981. Statistical Methods For Rates and Proportions 2 nd Ed. John Wiley , New York . Fleiss, J. L. 1981. Statistical Methods For Rates and Proportions 2nd Ed. John Wiley, New York.","edition":"2"},{"key":"e_1_2_1_10_1","volume-title":"Proceedings of the ACM Workshop on Social Network Mining and Analysis and Knowledge Discovery and Data Mining (SNAKDD).","author":"Ghosh R.","unstructured":"Ghosh , R. and Lerman , K . 2010. Predicting influential users in online social networks . In Proceedings of the ACM Workshop on Social Network Mining and Analysis and Knowledge Discovery and Data Mining (SNAKDD). Ghosh, R. and Lerman, K. 2010. Predicting influential users in online social networks. 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