{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,25]],"date-time":"2026-01-25T20:42:43Z","timestamp":1769373763378,"version":"3.49.0"},"reference-count":69,"publisher":"Association for Computing Machinery (ACM)","issue":"5","license":[{"start":{"date-parts":[[2021,10,31]],"date-time":"2021-10-31T00:00:00Z","timestamp":1635638400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001843","name":"SERB","doi-asserted-by":"crossref","award":["ECR\/2017\/001691"],"award-info":[{"award-number":["ECR\/2017\/001691"]}],"id":[{"id":"10.13039\/501100001843","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Ramanujan Fellowship"},{"name":"ihub-Anubhuti-iiitd Foundation"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Intell. Syst. Technol."],"published-print":{"date-parts":[[2021,10,31]]},"abstract":"<jats:p>\n            YouTube sells advertisements on the posted videos, which in turn enables the content creators to monetize their videos. As an unintended consequence, this has proliferated various illegal activities such as artificial boosting of views, likes, comments, and subscriptions. We refer to such\n            <jats:italic>videos<\/jats:italic>\n            (gaining likes and comments artificially) and\n            <jats:italic>channels<\/jats:italic>\n            (gaining subscriptions artificially) as \u201ccollusive entities.\u201d Detecting such collusive entities is an important yet challenging task. Existing solutions mostly deal with the problem of spotting fake views, spam comments, fake content, and so on, and oftentimes ignore how such fake activities emerge via collusion. Here, we collect a large dataset consisting of two types of collusive entities on YouTube\u2014\n            <jats:italic>videos<\/jats:italic>\n            submitted to gain collusive likes and comment requests and\n            <jats:italic>channels<\/jats:italic>\n            submitted to gain collusive subscriptions.\n          <\/jats:p>\n          <jats:p>\n            We begin by providing an in-depth analysis of collusive entities on YouTube fostered by various\n            <jats:italic>blackmarket services<\/jats:italic>\n            . Following this, we propose models to detect three types of collusive YouTube entities: videos seeking collusive likes, channels seeking collusive subscriptions, and videos seeking collusive comments. The third type of entity is associated with temporal information. To detect videos and channels for collusive likes and subscriptions, respectively, we utilize one-class classifiers trained on our curated collusive entities and a set of novel features. The SVM-based model shows significant performance with a true positive rate of 0.911 and 0.910 for detecting collusive videos and collusive channels, respectively. To detect videos seeking collusive comments, we propose\n            <jats:monospace>CollATe<\/jats:monospace>\n            , a novel end-to-end neural architecture that leverages time-series information of posted comments along with static metadata of videos.\n            <jats:monospace>CollATe<\/jats:monospace>\n            is composed of three components: metadata feature extractor (which derives metadata-based features from videos), anomaly feature extractor (which utilizes the time-series data to detect sudden changes in the commenting activity), and comment feature extractor (which utilizes the text of the comments posted during collusion and computes a similarity score between the comments). Extensive experiments show the effectiveness of\n            <jats:monospace>CollATe<\/jats:monospace>\n            \u00a0(with a true positive rate of 0.905) over the baselines.\n          <\/jats:p>","DOI":"10.1145\/3477300","type":"journal-article","created":{"date-parts":[[2021,11,24]],"date-time":"2021-11-24T16:04:45Z","timestamp":1637769885000},"page":"1-28","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":16,"title":["Detecting and Analyzing Collusive Entities on YouTube"],"prefix":"10.1145","volume":"12","author":[{"given":"Hridoy Sankar","family":"Dutta","sequence":"first","affiliation":[{"name":"IIIT-Delhi, Delhi, India"}]},{"given":"Mayank","family":"Jobanputra","sequence":"additional","affiliation":[{"name":"IIIT-Delhi, Delhi, India"}]},{"given":"Himani","family":"Negi","sequence":"additional","affiliation":[{"name":"BVCOE, Paschim Vihar, New Delhi, India"}]},{"given":"Tanmoy","family":"Chakraborty","sequence":"additional","affiliation":[{"name":"IIIT-Delhi, Delhi, India"}]}],"member":"320","published-online":{"date-parts":[[2021,11,24]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Data Craft: The manipulation of social media metadata. Data Societ. Res. Inst.","author":"Acker Amelia","year":"2018","unstructured":"Amelia Acker . 2018 . Data Craft: The manipulation of social media metadata. Data Societ. Res. Inst. (2018). https:\/\/ccn.unistra.fr\/websites\/ccn\/documentation\/Recherche-Data\/DS_Data_Craft_Manipulation_of_Social_Media_Metadata.pdf. Amelia Acker. 2018. Data Craft: The manipulation of social media metadata. Data Societ. Res. Inst. (2018). https:\/\/ccn.unistra.fr\/websites\/ccn\/documentation\/Recherche-Data\/DS_Data_Craft_Manipulation_of_Social_Media_Metadata.pdf."},{"key":"e_1_2_1_2_1","volume-title":"Followers or phantoms? An anatomy of purchased Twitter followers. arXiv preprint arXiv:1408.1534","author":"Aggarwal Anupama","year":"2014","unstructured":"Anupama Aggarwal and Ponnurangam Kumaraguru . 2014. Followers or phantoms? An anatomy of purchased Twitter followers. arXiv preprint arXiv:1408.1534 ( 2014 ). Anupama Aggarwal and Ponnurangam Kumaraguru. 2014. Followers or phantoms? An anatomy of purchased Twitter followers. arXiv preprint arXiv:1408.1534 (2014)."},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2018.05.181"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-21741-9_23"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICMLA.2015.37"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICMLA.2015.192"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/3380537"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/3341161.3342934"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/2983323.2983695"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2008.01.077"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1970.10481180"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/342009.335388"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/2567948.2578996"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/3106426.3106489"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/S17-2001"},{"key":"e_1_2_1_16_1","volume-title":"et\u00a0al","author":"Cer Daniel","year":"2018","unstructured":"Daniel Cer , Yinfei Yang , Sheng-yi Kong, Nan Hua , Nicole Limtiaco , Rhomni\u00a0 St. John , Noah Constant , Mario Guajardo-Cespedes , Steve Yuan , Chris Tar , et\u00a0al . 2018 . Universal sentence encoder. arXiv preprint arXiv:1803.11175 (2018). Daniel Cer, Yinfei Yang, Sheng-yi Kong, Nan Hua, Nicole Limtiaco, Rhomni\u00a0St. John, Noah Constant, Mario Guajardo-Cespedes, Steve Yuan, Chris Tar, et\u00a0al. 2018. Universal sentence encoder. arXiv preprint arXiv:1803.11175 (2018)."},{"key":"e_1_2_1_17_1","first-page":"44","article-title":"Analysis and detection of fake views in online video services. ACM Trans. Multimedia Comput","volume":"11","author":"Chen Liang","year":"2015","unstructured":"Liang Chen , Yipeng Zhou , and Dah\u00a0Ming Chiu . 2015 . Analysis and detection of fake views in online video services. ACM Trans. Multimedia Comput ., Commun. Applic. 11 , 2s (2015), 44 . Liang Chen, Yipeng Zhou, and Dah\u00a0Ming Chiu. 2015. Analysis and detection of fake views in online video services. ACM Trans. Multimedia Comput., Commun. Applic. 11, 2s (2015), 44.","journal-title":"Commun. Applic."},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3289600.3291010"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDIM.2013.6694038"},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.dss.2015.09.003"},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/2342356.2342394"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/2663716.2663729"},{"key":"e_1_2_1_23_1","volume-title":"Shiv Kumar, and Tanmoy Chakraborty.","author":"Dhawan Sarthika","year":"2019","unstructured":"Sarthika Dhawan , Siva Charan\u00a0Reddy Gangireddy , Shiv Kumar, and Tanmoy Chakraborty. 2019 . Spotting collusive behaviour of online fraud groups in customer reviews. arXiv preprint arXiv:1905.13649 (2019). Sarthika Dhawan, Siva Charan\u00a0Reddy Gangireddy, Shiv Kumar, and Tanmoy Chakraborty. 2019. Spotting collusive behaviour of online fraud groups in customer reviews. arXiv preprint arXiv:1905.13649 (2019)."},{"key":"e_1_2_1_24_1","volume-title":"DECIFE: Detecting collusive users involved in blackmarket following services on Twitter. arXiv preprint arXiv:2107.11697","author":"Dutta Hridoy\u00a0Sankar","year":"2021","unstructured":"Hridoy\u00a0Sankar Dutta , Kartik Aggarwal , and Tanmoy Chakraborty . 2021 . DECIFE: Detecting collusive users involved in blackmarket following services on Twitter. arXiv preprint arXiv:2107.11697 (2021). Hridoy\u00a0Sankar Dutta, Kartik Aggarwal, and Tanmoy Chakraborty. 2021. DECIFE: Detecting collusive users involved in blackmarket following services on Twitter. arXiv preprint arXiv:2107.11697 (2021)."},{"key":"e_1_2_1_25_1","volume-title":"Blackmarket-driven collusion among retweeters\u2013Analysis, detection, and characterization","author":"Dutta Hridoy\u00a0Sankar","year":"2019","unstructured":"Hridoy\u00a0Sankar Dutta and Tanmoy Chakraborty . 2019. Blackmarket-driven collusion among retweeters\u2013Analysis, detection, and characterization . IEEE Trans. Inf. Forens. Secur . 15 ( 2019 ). Hridoy\u00a0Sankar Dutta and Tanmoy Chakraborty. 2019. Blackmarket-driven collusion among retweeters\u2013Analysis, detection, and characterization. IEEE Trans. Inf. Forens. Secur. 15 (2019)."},{"key":"e_1_2_1_26_1","volume-title":"Blackmarket-driven collusion on online media: A survey. arXiv preprint arXiv:2008.13102","author":"Dutta Hridoy\u00a0Sankar","year":"2020","unstructured":"Hridoy\u00a0Sankar Dutta and Tanmoy Chakraborty . 2020. Blackmarket-driven collusion on online media: A survey. arXiv preprint arXiv:2008.13102 ( 2020 ). Hridoy\u00a0Sankar Dutta and Tanmoy Chakraborty. 2020. Blackmarket-driven collusion on online media: A survey. arXiv preprint arXiv:2008.13102 (2020)."},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/ASONAM.2018.8508801"},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2020.2970601"},{"key":"e_1_2_1_29_1","volume-title":"20th Annual Network & Distributed System Security Symposium. 1\u201317","author":"Egele Manuel","year":"2013","unstructured":"Manuel Egele , Gianluca Stringhini , Christopher Kruegel , and Giovanni Vigna . 2013 . COMPA: Detecting compromised accounts on social networks . In 20th Annual Network & Distributed System Security Symposium. 1\u201317 . Manuel Egele, Gianluca Stringhini, Christopher Kruegel, and Giovanni Vigna. 2013. COMPA: Detecting compromised accounts on social networks. In 20th Annual Network & Distributed System Security Symposium. 1\u201317."},{"key":"e_1_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/TDSC.2015.2479616"},{"key":"e_1_2_1_31_1","volume-title":"A longitudinal analysis of YouTube\u2019s promotion of conspiracy videos. arXiv preprint arXiv:2003.03318","author":"Faddoul Marc","year":"2020","unstructured":"Marc Faddoul , Guillaume Chaslot , and Hany Farid . 2020. A longitudinal analysis of YouTube\u2019s promotion of conspiracy videos. arXiv preprint arXiv:2003.03318 ( 2020 ). Marc Faddoul, Guillaume Chaslot, and Hany Farid. 2020. A longitudinal analysis of YouTube\u2019s promotion of conspiracy videos. arXiv preprint arXiv:2003.03318 (2020)."},{"key":"e_1_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/2187836.2187846"},{"key":"e_1_2_1_33_1","unstructured":"Huifeng Guo Ruiming Tang Yunming Ye Zhenguo Li and Xiuqiang He. 2017. DeepFM: A factorization-machine based neural network for CTR prediction. arXiv preprint arXiv:1703.04247 (2017).  Huifeng Guo Ruiming Tang Yunming Ye Zhenguo Li and Xiuqiang He. 2017. DeepFM: A factorization-machine based neural network for CTR prediction. arXiv preprint arXiv:1703.04247 (2017)."},{"key":"e_1_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/2487788.2488033"},{"key":"e_1_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1002\/wics.1421"},{"key":"e_1_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/ASONAM.2018.8508766"},{"key":"e_1_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/3121134"},{"key":"e_1_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623632"},{"key":"e_1_2_1_39_1","volume-title":"The flourishing business of fake YouTube views. The New York Times 11","author":"Keller H.","year":"2018","unstructured":"Michael\u00a0 H. Keller . 2018. The flourishing business of fake YouTube views. The New York Times 11 ( 2018 ). http:\/\/authenticationinart.org\/wp-content\/uploads\/2018\/08\/flourishing-fake-youtube.pdf. Michael\u00a0H. Keller. 2018. The flourishing business of fake YouTube views. The New York Times 11 (2018). http:\/\/authenticationinart.org\/wp-content\/uploads\/2018\/08\/flourishing-fake-youtube.pdf."},{"key":"e_1_2_1_40_1","volume-title":"6th Conference on Email and Anti-spam. 1\u201310","author":"Ko\u0142cz Aleksander","year":"2009","unstructured":"Aleksander Ko\u0142cz and Choon\u00a0Hui Teo . 2009 . Feature weighting for improved classifier robustness . In 6th Conference on Email and Anti-spam. 1\u201310 . Aleksander Ko\u0142cz and Choon\u00a0Hui Teo. 2009. Feature weighting for improved classifier robustness. In 6th Conference on Email and Anti-spam. 1\u201310."},{"key":"e_1_2_1_41_1","volume-title":"False information on web and social media: A survey. arXiv preprint arXiv:1804.08559","author":"Kumar Srijan","year":"2018","unstructured":"Srijan Kumar and Neil Shah . 2018. False information on web and social media: A survey. arXiv preprint arXiv:1804.08559 ( 2018 ). Srijan Kumar and Neil Shah. 2018. False information on web and social media: A survey. arXiv preprint arXiv:1804.08559 (2018)."},{"key":"e_1_2_1_42_1","volume-title":"International Conference on Machine Learning. 957\u2013966","author":"Kusner Matt","year":"2015","unstructured":"Matt Kusner , Yu Sun , Nicholas Kolkin , and Kilian Weinberger . 2015 . From word embeddings to document distances . In International Conference on Machine Learning. 957\u2013966 . Matt Kusner, Yu Sun, Nicholas Kolkin, and Kilian Weinberger. 2015. From word embeddings to document distances. In International Conference on Machine Learning. 957\u2013966."},{"key":"e_1_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1145\/2872427.2882972"},{"key":"e_1_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2008.17"},{"key":"e_1_2_1_45_1","volume-title":"Proceedings. Presses universitaires de Louvain","volume":"89","author":"Malhotra Pankaj","year":"2015","unstructured":"Pankaj Malhotra , Lovekesh Vig , Gautam Shroff , and Puneet Agarwal . 2015 . Long short term memory networks for anomaly detection in time series . In Proceedings. Presses universitaires de Louvain , vol. 89 . 89\u201394. https:\/\/books.google.com\/books?hl=en&lr=&id=USGLCgAAQBAJ&oi=fnd&pg=PA89&ots=FtfcmqEUSO&sig=WNUFEBgYkzvW5tMkK9HCNP3FChM. Pankaj Malhotra, Lovekesh Vig, Gautam Shroff, and Puneet Agarwal. 2015. Long short term memory networks for anomaly detection in time series. In Proceedings. Presses universitaires de Louvain, vol. 89. 89\u201394. https:\/\/books.google.com\/books?hl=en&lr=&id=USGLCgAAQBAJ&oi=fnd&pg=PA89&ots=FtfcmqEUSO&sig=WNUFEBgYkzvW5tMkK9HCNP3FChM."},{"key":"e_1_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1145\/2872427.2882980"},{"key":"e_1_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1109\/IC3I.2016.7918016"},{"key":"e_1_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1145\/1242572.1242606"},{"key":"e_1_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1145\/3133956.3134055"},{"key":"e_1_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1162\/089976601750264965"},{"key":"e_1_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1145\/3201064.3201105"},{"key":"e_1_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2017.140"},{"key":"e_1_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2017.07.007"},{"key":"e_1_2_1_54_1","volume-title":"24th USENIX Security Symposium. 563\u2013578","author":"Stringhini Gianluca","year":"2015","unstructured":"Gianluca Stringhini , Pierre Mourlanne , Gregoire Jacob , Manuel Egele , Christopher Kruegel , and Giovanni Vigna . 2015 . EVILCOHORT: Detecting communities of malicious accounts on online services . In 24th USENIX Security Symposium. 563\u2013578 . Gianluca Stringhini, Pierre Mourlanne, Gregoire Jacob, Manuel Egele, Christopher Kruegel, and Giovanni Vigna. 2015. EVILCOHORT: Detecting communities of malicious accounts on online services. In 24th USENIX Security Symposium. 563\u2013578."},{"key":"e_1_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1145\/2504730.2504731"},{"key":"e_1_2_1_56_1","volume-title":"Mining user comment activity for detecting forum spammers in YouTube. arXiv preprint arXiv:1103.5044","author":"Sureka Ashish","year":"2011","unstructured":"Ashish Sureka . 2011. Mining user comment activity for detecting forum spammers in YouTube. arXiv preprint arXiv:1103.5044 ( 2011 ). Ashish Sureka. 2011. Mining user comment activity for detecting forum spammers in YouTube. arXiv preprint arXiv:1103.5044 (2011)."},{"key":"e_1_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1145\/2068816.2068840"},{"key":"e_1_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.image.2018.12.002"},{"key":"e_1_2_1_59_1","first-page":"4","article-title":"Feature selection for comment spam filtering on YouTube. Data Sci","volume":"1","author":"Uysal Alper\u00a0K\u00fcr\u015fat","year":"2018","unstructured":"Alper\u00a0K\u00fcr\u015fat Uysal . 2018 . Feature selection for comment spam filtering on YouTube. Data Sci . Applic. 1 , 1 (2018), 4 \u2013 8 . Alper\u00a0K\u00fcr\u015fat Uysal. 2018. Feature selection for comment spam filtering on YouTube. Data Sci. Applic. 1, 1 (2018), 4\u20138.","journal-title":"Applic."},{"key":"e_1_2_1_60_1","first-page":"3371","article-title":"Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion","author":"Vincent Pascal","year":"2010","unstructured":"Pascal Vincent , Hugo Larochelle , Isabelle Lajoie , Yoshua Bengio , and Pierre-Antoine Manzagol . 2010 . Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion . J. Mach. Learn. Res. 11 , Dec. (2010), 3371 \u2013 3408 . Pascal Vincent, Hugo Larochelle, Isabelle Lajoie, Yoshua Bengio, and Pierre-Antoine Manzagol. 2010. Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. J. Mach. Learn. Res. 11, Dec. (2010), 3371\u20133408.","journal-title":"J. Mach. Learn. Res. 11"},{"key":"e_1_2_1_61_1","volume-title":"International Conference on Security and Cryptography (SECRYPT). IEEE, 1\u201310","author":"Wang Alex\u00a0Hai","year":"2010","unstructured":"Alex\u00a0Hai Wang . 2010 . Don\u2019t follow me: Spam detection in Twitter . In International Conference on Security and Cryptography (SECRYPT). IEEE, 1\u201310 . Alex\u00a0Hai Wang. 2010. Don\u2019t follow me: Spam detection in Twitter. In International Conference on Security and Cryptography (SECRYPT). IEEE, 1\u201310."},{"key":"e_1_2_1_62_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2011.124"},{"key":"e_1_2_1_63_1","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2017.23020"},{"key":"e_1_2_1_64_1","volume-title":"International Conference on Computing and Informatics. 228\u2013234","author":"Yusof Yuhanis","year":"2017","unstructured":"Yuhanis Yusof and Omar\u00a0Hadeb Sadoon . 2017 . Detecting video spammers in YouTube social media . In International Conference on Computing and Informatics. 228\u2013234 . Yuhanis Yusof and Omar\u00a0Hadeb Sadoon. 2017. Detecting video spammers in YouTube social media. In International Conference on Computing and Informatics. 228\u2013234."},{"key":"e_1_2_1_65_1","doi-asserted-by":"publisher","DOI":"10.1109\/ASONAM.2018.8508288"},{"key":"e_1_2_1_66_1","doi-asserted-by":"publisher","DOI":"10.1109\/ASONAM.2018.8508523"},{"key":"e_1_2_1_67_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.image.2018.02.002"},{"key":"e_1_2_1_68_1","doi-asserted-by":"publisher","DOI":"10.1109\/PRDC.2017.43"},{"key":"e_1_2_1_69_1","volume-title":"International Conference on Computer Networks and Communication Technology (CNCT\u201916)","author":"Zhi Yang","year":"2016","unstructured":"Zhen-hui Zhu, Yang Zhi , and Ya-fei Dai. 2016 . A new approach to detect user collusion behavior in online QA system . In International Conference on Computer Networks and Communication Technology (CNCT\u201916) . Atlantis Press, 836\u2013842. Zhen-hui Zhu, Yang Zhi, and Ya-fei Dai. 2016. A new approach to detect user collusion behavior in online QA system. In International Conference on Computer Networks and Communication Technology (CNCT\u201916). Atlantis Press, 836\u2013842."}],"container-title":["ACM Transactions on Intelligent Systems and Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3477300","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3477300","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:18:33Z","timestamp":1750191513000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3477300"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,31]]},"references-count":69,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2021,10,31]]}},"alternative-id":["10.1145\/3477300"],"URL":"https:\/\/doi.org\/10.1145\/3477300","relation":{},"ISSN":["2157-6904","2157-6912"],"issn-type":[{"value":"2157-6904","type":"print"},{"value":"2157-6912","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,10,31]]},"assertion":[{"value":"2020-04-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-07-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-11-24","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}