{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,16]],"date-time":"2025-12-16T12:25:33Z","timestamp":1765887933198,"version":"3.41.0"},"reference-count":57,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2017,9,20]],"date-time":"2017-09-20T00:00:00Z","timestamp":1505865600000},"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. Priv. Secur."],"published-print":{"date-parts":[[2017,11,30]]},"abstract":"<jats:p>\n            Online social networks offer convenient ways to reach out to large audiences. In particular, Facebook pages are increasingly used by businesses, brands, and organizations to connect with multitudes of users worldwide. As the number of likes of a page has become a de-facto measure of its popularity and profitability, an underground market of services artificially inflating page likes\n            <jats:italic>(\u201clike farms<\/jats:italic>\n            \u201d) has emerged alongside Facebook\u2019s official targeted advertising platform. Nonetheless, besides a few media reports, there is little work that systematically analyzes Facebook pages\u2019 promotion methods. Aiming to fill this gap, we present a honeypot-based comparative measurement study of page likes garnered via Facebook advertising and from popular like farms. First, we analyze likes based on demographic, temporal, and social characteristics and find that some farms seem to be operated by bots and do not really try to hide the nature of their operations, while others follow a stealthier approach, mimicking regular users\u2019 behavior. Next, we look at fraud detection algorithms currently deployed by Facebook and show that they do not work well to detect stealthy farms that spread likes over longer timespans and like popular pages to mimic regular users. To overcome their limitations, we investigate the feasibility of timeline-based detection of like farm accounts, focusing on characterizing content generated by Facebook accounts on their timelines as an indicator of genuine versus fake social activity. We analyze a wide range of features extracted from timeline posts, which we group into two main categories: lexical and non-lexical. We find that like farm accounts tend to re-share content more often, use fewer words and poorer vocabulary, and more often generate duplicate comments and likes compared to normal users. Using relevant lexical and non-lexical features, we build a classifier to detect like farms accounts that achieves a precision higher than 99% and a 93% recall.\n          <\/jats:p>","DOI":"10.1145\/3121134","type":"journal-article","created":{"date-parts":[[2017,9,20]],"date-time":"2017-09-20T12:35:19Z","timestamp":1505910919000},"page":"1-28","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":23,"title":["Measuring, Characterizing, and Detecting Facebook Like Farms"],"prefix":"10.1145","volume":"20","author":[{"given":"Muhammad","family":"Ikram","sequence":"first","affiliation":[{"name":"UNSW and Data61-CSIRO"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lucky","family":"Onwuzurike","sequence":"additional","affiliation":[{"name":"University College London, London, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shehroze","family":"Farooqi","sequence":"additional","affiliation":[{"name":"University of Iowa, US"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Emiliano De","family":"Cristofaro","sequence":"additional","affiliation":[{"name":"University College London, London, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Arik","family":"Friedman","sequence":"additional","affiliation":[{"name":"Atlassian"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guillaume","family":"Jourjon","sequence":"additional","affiliation":[{"name":"Data61-CSIRO"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammed Ali","family":"Kaafar","sequence":"additional","affiliation":[{"name":"Data61-CSIRO"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"M. Zubair","family":"Shafiq","sequence":"additional","affiliation":[{"name":"University of Iowa, US"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2017,9,20]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/1327452.1327494"},{"key":"e_1_2_1_2_1","unstructured":"Charles Arthur. 2013. How Low-Paid Workers at \u2018Click Farms\u2019 Create Appearance of Online Popularity. Retrieved from http:\/\/gu.com\/p\/3hmn3\/stw.  Charles Arthur. 2013. How Low-Paid Workers at \u2018Click Farms\u2019 Create Appearance of Online Popularity. 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CRC Press."},{"volume-title":"Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases Workshop (ECML\/PKDD LML\u201913)","year":"2013","author":"Buitinck Lars","key":"e_1_2_1_8_1"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.5555\/2228298.2228319"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/2660267.2660269"},{"key":"e_1_2_1_11_1","unstructured":"Brian Carter. 2013. The Like Economy: How Businesses Make Money with Facebook. QUE Publishing.   Brian Carter. 2013. The Like Economy: How Businesses Make Money with Facebook. QUE Publishing."},{"key":"e_1_2_1_12_1","unstructured":"Rory Cellan-Jones. 2012. Who \u2018likes\u2019 my Virtual Bagels? (July 2012). Retrieved from http:\/\/www.bbc.co.uk\/news\/technology-18819338.  Rory Cellan-Jones. 2012. Who \u2018likes\u2019 my Virtual Bagels? (July 2012). Retrieved from http:\/\/www.bbc.co.uk\/news\/technology-18819338."},{"volume-title":"Proceedings of the 19th Annual Network and Distributed System Security Symposium (NDSS\u201912)","year":"2012","author":"Chaabane A.","key":"e_1_2_1_13_1"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-39077-7_12"},{"volume-title":"Proceedings of the 16th Annual Network and Distributed System Security Symposium (NDSS\u201909)","year":"2009","author":"Danezis George","key":"e_1_2_1_15_1"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/2377677.2377715"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/2663716.2663729"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/1644879.1644881"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1037\/h0057532"},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1006\/jcss.1997.1504"},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/1879141.1879147"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/2699418"},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623632"},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-06608-0_11"},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1101\/gr.648603"},{"volume-title":"Proceedings of the 1st International Conference on Knowledge Discovery and Data Mining (KDD\u201995)","year":"1995","author":"Kohavi Ron","key":"e_1_2_1_26_1"},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2016.2623586"},{"key":"e_1_2_1_28_1","unstructured":"Justin Lafferty. 2013. 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