{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T10:33:23Z","timestamp":1785321203760,"version":"3.55.0"},"reference-count":24,"publisher":"SAGE Publications","issue":"2","license":[{"start":{"date-parts":[[2016,10,19]],"date-time":"2016-10-19T00:00:00Z","timestamp":1476835200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Big Data &amp; Society"],"published-print":{"date-parts":[[2016,12]]},"abstract":"<jats:p>In online social networks, the audience size commanded by an organization or an individual is a critical measure of that entity\u2019s popularity and this measure has important economic and\/or political implications. Such efforts to measure popularity of users or exploit knowledge about their audience are complicated by the presence of fake profiles on these networks. In this study, analysis of 62 million publicly available Twitter user profiles was conducted and a strategy to identify automatically generated fake profiles was established. Using a combination of a pattern-matching algorithm on screen-names and an analysis of update times, a reasonable number (\u223c0.1% of total users) of highly reliable fake user accounts were identified. Analysis of profile creation times and URLs of these fake accounts revealed their distinct behavior relative to a ground truth data set. The characteristics of friends and followers of users in the two data sets further revealed the very different nature of the two groups. The ratio of number of followers-to-friends for ground truth users was \u223c1, consistent with past observations, while the fake profiles had a median ratio \u223c30, indicating that the fake users we identified were primarily focused on gathering friends. An analysis of the temporal evolution of accounts over 2 years showed that the friends-to-followers ratio increased over time for fake profiles while they decreased for ground truth users. Our results, thus, suggest that a profile-based approach can be used for identifying a core set of fake online social network users in a time-efficient manner.<\/jats:p>","DOI":"10.1177\/2053951716674236","type":"journal-article","created":{"date-parts":[[2016,10,19]],"date-time":"2016-10-19T21:54:24Z","timestamp":1476914064000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":57,"title":["Profile characteristics of fake Twitter accounts"],"prefix":"10.1177","volume":"3","author":[{"given":"Supraja","family":"Gurajala","sequence":"first","affiliation":[{"name":"Department of Computer Science, Clarkson University, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joshua S","family":"White","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Clarkson University, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Brian","family":"Hudson","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Clarkson University, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Brian R","family":"Voter","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Clarkson University, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jeanna N","family":"Matthews","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Clarkson University, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2016,10,19]]},"reference":[{"key":"bibr1-2053951716674236","unstructured":"Benevenuto F, Magno G, Rodrigues T, et\u00a0al. (2010) Detecting spammers on twitter. In:\n                      Collaboration, electronic messaging, anti-abuse and spam conference\n                      , p.12."},{"key":"bibr2-2053951716674236","doi-asserted-by":"publisher","DOI":"10.1890\/1540-9295-12.5.259"},{"key":"bibr3-2053951716674236","unstructured":"Danezis G and Mittal P (2009) SybilInfer: Detecting sybil nodes using social networks. In:\n                      NDSS\n                      ."},{"key":"bibr101-2053951716674236","doi-asserted-by":"crossref","unstructured":"Clark, EM, Williams JR, Jones CA., et al. (2016) Sifting robotic from organic text: a natural language approach for detecting automation on Twitter.\n                      Journal of Computational Science\n                      16: 1\u20137.","DOI":"10.1016\/j.jocs.2015.11.002"},{"key":"bibr4-2053951716674236","unstructured":"Dawson S (2013) Is Twitter telling the truth about their \u201cactive user\u201d stats? Available at: http:\/\/eggsbacon.co.nz\/twitter."},{"key":"bibr5-2053951716674236","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-45748-8_24"},{"key":"bibr6-2053951716674236","doi-asserted-by":"crossref","unstructured":"Gabielkov M and Legout A (2012) The complete picture of the Twitter social graph. In:\n                      CoNEXT Student \u201812 Proceedings of ACM conference on CoNEXT student workshop\n                      , New York, NY, USA, pp.19\u201320.","DOI":"10.1145\/2413247.2413260"},{"key":"bibr7-2053951716674236","doi-asserted-by":"crossref","unstructured":"Grier C, Thomas K, Paxson V, et\u00a0al. (2010) @ spam: The underground on 140 characters or less. In:\n                      Proceedings of the 17th ACM conference on computer and communications security\n                      , pp.27\u201337.","DOI":"10.1145\/1866307.1866311"},{"key":"bibr108-2053951716674236","doi-asserted-by":"crossref","unstructured":"Gurajala S, White JS, Hudson B, et al. Fake Twitter accounts: profile characteristics obtained using an activity-based pattern detection approach. In:\n                      Proceedings of the 2015 International Conference on Social Media & Society\n                      , July 2015, p. 9. ACM.","DOI":"10.1145\/2789187.2789206"},{"key":"bibr8-2053951716674236","doi-asserted-by":"crossref","unstructured":"Jiang M, Cui P, Beutel, A, et al. Detecting suspicious following behavior in multimillion-node social networks. In:\n                      Proceedings of the 23rd International Conference on World Wide Web\n                      , April 2014, pp. 305\u2013306. ACM.","DOI":"10.1145\/2567948.2577306"},{"key":"bibr104-2053951716674236","doi-asserted-by":"crossref","unstructured":"Jiang M, Cui P, Beutel A, et al. (2016) Catching synchronized behaviors in large networks: A graph mining approach.\n                      ACM Transactions on Knowledge Discovery from Data (TKDD)\n                      . 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In:\n                      Proceedings of the 33rd international ACM SIGIR conference on research and development in information retrieval\n                      , pp.435\u2013442.","DOI":"10.1145\/1835449.1835522"},{"key":"bibr10-2053951716674236","volume-title":"We first: How brands and consumers use social media to build a better world","author":"Mainwaring S","year":"2011"},{"key":"bibr11-2053951716674236","volume-title":"Politics and the Twitter Revolution: How Tweets Influence the Relationship Between Political Leaders and the Public","author":"Parmelee JH","year":"2011"},{"key":"bibr12-2053951716674236","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMp1307752"},{"key":"bibr13-2053951716674236","doi-asserted-by":"crossref","unstructured":"Stringhini G, Kruegel C and Vigna G (2010) Detecting spammers on social networks. In:\n                      Proceedings of the 26th annual computer security applications conference\n                      , pp.1\u20139.","DOI":"10.1145\/1920261.1920263"},{"key":"bibr14-2053951716674236","doi-asserted-by":"crossref","unstructured":"Stringhini G, Wang G, Egele M, et\u00a0al. (2013) Follow the green: Growth and dynamics in twitter follower markets. In:\n                      Proceedings of the 2013 conference on Internet measurement\n                      , pp.163\u2013176.","DOI":"10.1145\/2504730.2504731"},{"key":"bibr15-2053951716674236","unstructured":"Syeed N, Zafar R, Asaad R, et\u00a0al. (2014) Arab women rising: 35 Entrepreneurs making a difference in the Arab World.\n                      Knowledge@ Wharton\n                      ."},{"key":"bibr16-2053951716674236","unstructured":"Thomas K, McCoy D, Grier C, et\u00a0al. (2013) Trafficking fraudulent accounts: The role of the underground market in Twitter spam and abuse. 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