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This tutorial equips researchers and practitioners in computational social science with a practical overview for detecting and examining anti-social behavior in online discourse using machine learning and social network analysis techniques, including tools and workflows supporting toxic interaction analysis.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/from-trolling-to-cyberbullying-using-machine-learning-and-network-analysis-to-study-anti-social-behavior-on-social-media/124663/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/from-trolling-to-cyberbullying-using-machine-learning-and-network-analysis-to-study-anti-social-behavior-on-social-media/124663.png","ImageObject",300,407,{"name":92,"@type":93},"Anda","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-17","2026-08-05",true,{"@type":102,"interactionType":103,"userInteractionCount":52},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What anti-social behaviors does the tutorial focus on?","Question",{"text":112,"@type":113},"It focuses on trolling, cyberbullying, and hate speech as forms of anti-social behavior in online discourse.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How does the tutorial recommend detecting anti-social interactions?",{"text":117,"@type":113},"It uses machine learning for toxicity detection together with social network analysis to identify and examine toxic interactions and related patterns.",{"name":119,"@type":110,"acceptedAnswer":120},"Which tool is used for the hands-on part?",{"text":121,"@type":113},"Communalytic is the primary tool, running in a web browser and supporting toxicity and social network analysis over public datasets.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},124663,1785893642,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":52,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":14,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":129,"read_time":24},549768064622,"https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8","From Trolling to Cyberbullying: Using Machine Learning and Network Analysis to Study Anti-Social Behavior on Social Media  \nAnatoliy Gruzd  \nToronto Metropolitan University, Social Media Lab Toronto, Ontario, Canada [gruzd@torontomu.ca](gruzd@torontomu.ca)  \nPhilip Mai  \nToronto Metropolitan University, Social Media Lab Toronto, Ontario, Canada [philip.mai@torontomu.ca](philip.mai@torontomu.ca)  \nFelipe Bonow Soares  \nUniversity ofthe Arts London, London College of Communication London, UK [f.soares@lcc.arts.ac.uk](f.soares@lcc.arts.ac.uk)  \nABSTRACT  \nThe rise of social media and other web and mobile applications has transformed how people interact, but it has also created new challenges, such as anti-social behavior like trolling, cyberbullying, and hate speech. This behavior can have severe negative consequences for individuals and communities. This tutorial is intended for researchers and practitioners interested in computational social science and provides an overview of how to use machine learning and social network analysis techniques to detect and examine anti-social behavior in online discourse.  \nCCS CONCEPTS  \n• Computing methodologies → Machine learning; • Information systems → Social networks; Internet communications tools.  \nKEYWORDS  \nanti-social, online discourse, toxicity analysis, social network analysis, trolling, cyberbullying, computational social science  \nACM Reference Format:  \nAnatoliy Gruzd, Philip Mai, and Felipe Bonow Soares. 2023. From Trolling to Cyberbullying: Using Machine Learning and Network Analysis to Study Anti-Social Behavior on Social Media. In 34th ACM Conference on Hypertext and Social Media (HT’23), September 4–8, 2023, Rome, Italy. ACM, New York, NY, USA, 2 pages. [https://doi.org/10.1145/3603163.3610531](https://doi.org/10.1145/3603163.3610531)  \n1 INTRODUCTION  \nThe rise of social media and other web and mobile applications has transformed how people interact, but it has also created new challenges, such as anti-social behavior like trolling, cyberbullying, and hate speech. This behavior can have severe negative consequences for individuals and communities. This tutorial providesan overview of how to use machine learning and social network analysis techniques to detect and examine anti-social behavior in online discourse. It is designed for researchers and practitioners who are interested in learning about computational approaches to identifying and examining toxic interactions in digital spaces. The interdisciplinary nature of this research topic makes it relevant to  \nPermission to make digital or hard copies of part or all ofthis work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s) .  \nHT’23, September 4–8, 2023, Rome, Italy  \n© 2023 Copyright held by the owner/author(s) .  \nACM ISBN 979-8-4007-0232-7/23/09 .  \n[https://doi.org/10.1145/3603163.3610531](https://doi.org/10.1145/3603163.3610531)  \nparticipants from a wide range of fields, including Computational Social Science, Information Systems, Communication, Data Science, Education, Psychology, and Sociology.  \nThe primary tool to be used during the tutorial is Communalytic [5, 6], a research tool for studying online communities and online discourse. The tool runs within a web browser and is available at [communalytic.org. It](communalytic.org. It) can collect and analyze publicly available data from various social media platforms including Reddit, Telegram, YouTube, Twitter, and Facebook/Instagram (via CrowdTangle) . Communalytic leverages advanced techniques from text analysis (e.g., topic modeling, sentiment and toxicity analysis) and social network analysis to automatically identify anti-social interactions, pinpoint influencers, map shared interests","cbCainoAAvljM3Le","https://ap.wps.com/l/cbCainoAAvljM3Le","pdf",1012589,"English","# Introduction\n## Tutorial objective and target audience\n## Core technologies and tool overview\n# Method\n## Toxicity analysis\n## “Signed” network analysis\n## Hands-on exercise with Communalytic","[{\"question\":\"What anti-social behaviors does the tutorial focus on?\",\"answer\":\"It focuses on trolling, cyberbullying, and hate speech as forms of anti-social behavior in online discourse.\"},{\"question\":\"How does the tutorial recommend detecting anti-social interactions?\",\"answer\":\"It uses machine learning for toxicity detection together with social network analysis to identify and examine toxic interactions and related patterns.\"},{\"question\":\"Which tool is used for the hands-on part?\",\"answer\":\"Communalytic is the primary tool, running in a web browser and supporting toxicity and social network analysis over public datasets.\"}]","From Trolling to Cyberbullying - Using Machine Learning and Network Analysis to Study Anti-Social Behavior on Social Media | PDF"]