{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T03:17:48Z","timestamp":1784085468047,"version":"3.55.0"},"publisher-location":"New York, NY, USA","reference-count":44,"publisher":"ACM","license":[{"start":{"date-parts":[[2019,6,26]],"date-time":"2019-06-26T00:00:00Z","timestamp":1561507200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"European Union?s Horizon 2020 research and innovation programme","award":["691025"],"award-info":[{"award-number":["691025"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2019,6,26]]},"DOI":"10.1145\/3292522.3326028","type":"proceedings-article","created":{"date-parts":[[2019,7,1]],"date-time":"2019-07-01T19:23:35Z","timestamp":1562009015000},"page":"105-114","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":132,"title":["A Unified Deep Learning Architecture for Abuse Detection"],"prefix":"10.1145","author":[{"given":"Antigoni Maria","family":"Founta","sequence":"first","affiliation":[{"name":"Aristotle University, Thessaloniki, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Despoina","family":"Chatzakou","sequence":"additional","affiliation":[{"name":"Aristotle University, Thessaloniki, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicolas","family":"Kourtellis","sequence":"additional","affiliation":[{"name":"Telefonica Research, Barcelona, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jeremy","family":"Blackburn","sequence":"additional","affiliation":[{"name":"University of Alabama, Tuscaloosa, AL, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Athena","family":"Vakali","sequence":"additional","affiliation":[{"name":"Aristotle University, Thessaloniki, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ilias","family":"Leontiadis","sequence":"additional","affiliation":[{"name":"Telefonica Research, Barcelona, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2019,6,26]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Proceedings of TRAC-2018","author":"Aroyehun Segun Taofeek","year":"2018"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3041021.3054223"},{"key":"e_1_3_2_1_3_1","volume-title":"Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473","author":"Bahdanau Dzmitry","year":"2014"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/2566486.2567987"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1002\/poi3.85"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3091478.3091487"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/SocialCom-PASSAT.2012.55"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/W17-3001"},{"key":"e_1_3_2_1_9_1","unstructured":"Sam Cook. 2018. Cyberbullying facts and statistics for 2016--2018. https:\/\/www.comparitech.com\/internet-providers\/cyberbullying-statistics\/.  Sam Cook. 2018. Cyberbullying facts and statistics for 2016--2018. https:\/\/www.comparitech.com\/internet-providers\/cyberbullying-statistics\/."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"crossref","unstructured":"Thomas Davidson Dana Warmsley Michael Macy and Ingmar Weber. 2017. Automated Hate Speech Detection and the Problem of Offensive Language. In ICWSM.  Thomas Davidson Dana Warmsley Michael Macy and Ingmar Weber. 2017. Automated Hate Speech Detection and the Problem of Offensive Language. In ICWSM.","DOI":"10.1609\/icwsm.v11i1.14955"},{"key":"e_1_3_2_1_11_1","volume-title":"The Social Mobile Web","volume":"11","author":"Dinakar Karthik","year":"2011"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/2740908.2742760"},{"key":"e_1_3_2_1_13_1","unstructured":"Paula Fortuna Ilaria Bonavita and S\u00e9rgio Nunes. {n. d.}. Merging datasets for hate speech classification in Italian. ({n. d.}).  Paula Fortuna Ilaria Bonavita and S\u00e9rgio Nunes. {n. d.}. Merging datasets for hate speech classification in Italian. ({n. d.})."},{"key":"e_1_3_2_1_14_1","volume-title":"12th AAAI ICWSM.","author":"Founta Antigoni-Maria"},{"key":"e_1_3_2_1_15_1","volume-title":"1st Workshop on Abusive Language Online.","author":"Utpal Kumar Sikdar Bj\u00f6rn","year":"2017"},{"key":"e_1_3_2_1_16_1","first-page":"244","article-title":"Detection Of Cyberbullying On Social Media Using Data Mining Techniques","volume":"15","author":"Imam Riadi Hariani","year":"2017","journal-title":"International Journal of Computer Science and Information Security"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_1_18_1","unstructured":"Alex Hern. 2016. Did trolls cost Twitter $3.5bn and its sale? https:\/\/www.theguardian.com\/technology\/2016\/oct\/18\/did-trolls-cost-twitter-35bn.  Alex Hern. 2016. Did trolls cost Twitter $3.5bn and its sale? https:\/\/www.theguardian.com\/technology\/2016\/oct\/18\/did-trolls-cost-twitter-35bn."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/2959100.2959167"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/W17-2902"},{"key":"e_1_3_2_1_21_1","volume-title":"Bag of tricks for efficient text classification. arXiv preprint arXiv:1607.01759","author":"Joulin Armand","year":"2016"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"crossref","unstructured":"Irene Kwok and Yuzhou Wang. 2013. Locate the Hate: Detecting Tweets against Blacks.  Irene Kwok and Yuzhou Wang. 2013. Locate the Hate: Detecting Tweets against Blacks.","DOI":"10.1609\/aaai.v27i1.8539"},{"key":"e_1_3_2_1_23_1","unstructured":"Daniel Lowd. 2017. Can Facebook use AI to fight online abuse? http:\/\/theconversation.com\/can-facebook-use-ai-to-fight-online-abuse-95203.  Daniel Lowd. 2017. Can Facebook use AI to fight online abuse? http:\/\/theconversation.com\/can-facebook-use-ai-to-fight-online-abuse-95203."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICEDEG.2017.7962526"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1080\/0952813X.2017.1409284"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"crossref","unstructured":"Yashar Mehdad and Joel R Tetreault. 2016. Do Characters Abuse More Than Words?. In SIGDIAL.  Yashar Mehdad and Joel R Tetreault. 2016. Do Characters Abuse More Than Words?. In SIGDIAL.","DOI":"10.18653\/v1\/W16-3638"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/2872427.2883062"},{"key":"e_1_3_2_1_28_1","volume-title":"One-step and Two-step Classification for Abusive Language Detection on Twitter. arXiv preprint arXiv:1706.01206","author":"Park Ji Ho","year":"2017"},{"key":"e_1_3_2_1_29_1","volume-title":"Manning","author":"Pennington Jeffrey","year":"2014"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-018-1242-y"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/2684822.2685316"},{"key":"e_1_3_2_1_32_1","unstructured":"Matthew Rozsa. 2016. Twitter trolls are now abusing the company's bottom line. https:\/\/www.salon.com\/2016\/10\/19\/twitter-trolls-are-now-abusing-the-companys-bottom-line\/.  Matthew Rozsa. 2016. Twitter trolls are now abusing the company's bottom line. https:\/\/www.salon.com\/2016\/10\/19\/twitter-trolls-are-now-abusing-the-companys-bottom-line\/."},{"key":"e_1_3_2_1_33_1","unstructured":"Twitter Safety. 2017. Enforcing new rules to reduce hateful conduct and abusive behavior. https:\/\/blog.twitter.com\/official\/en_us\/topics\/company\/2017\/safetypoliciesdec2017.html.  Twitter Safety. 2017. Enforcing new rules to reduce hateful conduct and abusive behavior. https:\/\/blog.twitter.com\/official\/en_us\/topics\/company\/2017\/safetypoliciesdec2017.html."},{"key":"e_1_3_2_1_34_1","unstructured":"Huascar Sanchez and Shreyas Kumar. 2011. Twitter bullying detection. NSDI.  Huascar Sanchez and Shreyas Kumar. 2011. Twitter bullying detection. NSDI."},{"key":"e_1_3_2_1_35_1","volume-title":"HIIwiStJS at GermEval-2018: Integrating Linguistic Features in a Neural Network for the Identification of Offensive Language in Microposts","author":"Johannes","year":"2018"},{"key":"e_1_3_2_1_36_1","volume-title":"5th SocialNLP.","author":"Schmidt Anna"},{"key":"e_1_3_2_1_37_1","unstructured":"Elizabeth Schulze. 2019. EU says Facebook Google and Twitter are getting faster at removing hate speech online. goo.gl\/XPQzGC.  Elizabeth Schulze. 2019. EU says Facebook Google and Twitter are getting faster at removing hate speech online. goo.gl\/XPQzGC."},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/W18-5106"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1109\/ITW.2015.7133169"},{"key":"e_1_3_2_1_40_1","volume-title":"2nd Workshop on Language in Social Media.","author":"Warner William","year":"2012"},{"key":"e_1_3_2_1_41_1","volume-title":"Understanding Abuse: A Typology of Abusive Language Detection Subtasks. arXiv preprint arXiv:1705.09899","author":"Waseem Zeerak","year":"2017"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"crossref","unstructured":"Zeerak Waseem and Dirk Hovy. 2016. Hateful Symbols or Hateful People? Predictive Features for Hate Speech Detection on Twitter.. In SRW@ HLT-NAACL.  Zeerak Waseem and Dirk Hovy. 2016. Hateful Symbols or Hateful People? Predictive Features for Hate Speech Detection on Twitter.. In SRW@ HLT-NAACL.","DOI":"10.18653\/v1\/N16-2013"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1145\/2396761.2398556"},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-93417-4_48"}],"event":{"name":"WebSci '19: 11th ACM Conference on Web Science","location":"Boston Massachusetts USA","acronym":"WebSci '19","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web"]},"container-title":["Proceedings of the 10th ACM Conference on Web Science"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3292522.3326028","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3292522.3326028","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T00:25:57Z","timestamp":1750206357000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3292522.3326028"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,6,26]]},"references-count":44,"alternative-id":["10.1145\/3292522.3326028","10.1145\/3292522"],"URL":"https:\/\/doi.org\/10.1145\/3292522.3326028","relation":{},"subject":[],"published":{"date-parts":[[2019,6,26]]},"assertion":[{"value":"2019-06-26","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}