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Knowl. Discov. Data"],"published-print":{"date-parts":[[2017,11,30]]},"abstract":"<jats:p>Social media has become a popular and important tool for human communication. However, due to this popularity, spam and the distribution of malicious content by computer-controlled users, known as bots, has become a widespread problem. At the same time, when users use social media, they generate valuable data that can be used to understand the patterns of human communication. In this article, we focus on the following important question: Can we identify and use patterns of human communication to decide whether a human or a bot controls a user? The first contribution of this article is showing that the distribution of inter-arrival times (IATs) between postings is characterized by following four patterns: (i) heavy-tails, (ii) periodic-spikes, (iii) correlation between consecutive values, and (iv) bimodallity. As our second contribution, we propose a mathematical model named Act-M (Activity Model). We show that Act-M can accurately fit the distribution of IATs from social media users. Finally, we use Act-M to develop a method that detects if users are bots based only on the timing of their postings. We validate Act-M using data from over 55 million postings from four social media services: Reddit, Twitter, Stack-Overflow, and Hacker-News. Our experiments show that Act-M provides a more accurate fit to the data than existing models for human dynamics. Additionally, when detecting bots, Act-M provided a precision higher than 93% and 77% with a sensitivity of 70% for the Twitter and Reddit datasets, respectively.<\/jats:p>","DOI":"10.1145\/3064884","type":"journal-article","created":{"date-parts":[[2017,7,17]],"date-time":"2017-07-17T12:20:12Z","timestamp":1500294012000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":17,"title":["Modeling Temporal Activity to Detect Anomalous Behavior in Social Media"],"prefix":"10.1145","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1716-9577","authenticated-orcid":false,"given":"Alceu Ferraz","family":"Costa","sequence":"first","affiliation":[{"name":"University of S\u00e3o Paulo, Avenida Trabalhador S\u00e3ao-carlense, Centro"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuto","family":"Yamaguchi","sequence":"additional","affiliation":[{"name":"Tsukuba University, National Institute of Advanced Industrial Science and Technology, Tokyo, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Agma Juci Machado","family":"Traina","sequence":"additional","affiliation":[{"name":"University of S\u00e3o Paulo, Avenida Trabalhador S\u00e3ao-carlense, Centro"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Caetano Traina","family":"Jr.","sequence":"additional","affiliation":[{"name":"University of S\u00e3o Paulo, Avenida Trabalhador S\u00e3ao-carlense, Centro"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christos","family":"Faloutsos","sequence":"additional","affiliation":[{"name":"Carnegie Mellon University, Forbes Avenue Pittsburgh, PA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2017,7,14]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1038\/nature03459"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.2139\/ssrn.2456632"},{"key":"e_1_2_1_3_1","volume-title":"Bowman and Adelchi Azzalini","author":"Adrian","year":"2004","unstructured":"Adrian W. Bowman and Adelchi Azzalini . 2004 . Applied Smoothing Techniques for Data Analysis: The Kernel Approach with S-Plus Illustrations. Oxford University Press . 1--196 pages. Adrian W. Bowman and Adelchi Azzalini. 2004. Applied Smoothing Techniques for Data Analysis: The Kernel Approach with S-Plus Illustrations. Oxford University Press. 1--196 pages."},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/1541880.1541882"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/857166.857170"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/1920261.1920265"},{"key":"e_1_2_1_7_1","volume-title":"International Conference on Knowledge Discovery and Data Mining. ACM, 269--278","author":"Costa Alceu Ferraz","year":"2015","unstructured":"Alceu Ferraz Costa , Yuto Yamaguchi , Agma Juci Machado Traina , Caetano Traina Jr ., and Christos Faloutsos . 2015 . RSC: Mining and modeling temporal activity in social media . 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