[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123397-en":3,"doc-seo-123397-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123397,962075114101,"Seraphina","https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165",8,"Research & Report","CYBERBULLYING DETECTION WITH MACHINE LEARNING & PSYCHOLOGY: A SYSTEMATIC REVIEW - A SYSTEMATIC REVIEW","Cyberbullying detection with machine learning and psychology is reviewed through a systematic lens, synthesizing research that links behavioral understanding with computational methods. The work frames cyberbullying as a multifactor problem where algorithmic detection must account for linguistic cues, user context, and psychologically grounded dimensions of harm and intent. The review organizes existing approaches, evaluates performance and methodological choices, and highlights gaps in datasets, labeling practices, and model robustness. It supports future development of detection systems that better generalize across platforms and user populations.","Association for Information Systems  \nAIS Electronic Library (AISeL)  \n\n| CONF-IRM 2025 Proceedings | International Conference on Information Resources Management (CONF-IRM) |\n| --- | --- |\n| 7-2025\u003Cbr>CYBERBULLYING DETECTION WITH MACHINE LEARNING & PSYCHOLOGY: A SYSTEMATIC REVIEW\u003Cbr>Navaid Lubaina\u003Cbr>Institute of Business Administration, Karachi, [lnavaid@khi.iba.edu.pk](lnavaid@khi.iba.edu.pk)\u003Cbr>Faisal Iradat\u003Cbr>Institute of Business Administration, Karachi, [firadat@iba.edu.pk](firadat@iba.edu.pk)\u003Cbr>Nazım Taşkın\u003Cbr>Bogazici University, [nazim.taskin@bogazici.edu.tr](nazim.taskin@bogazici.edu.tr)\u003Cbr>Follow this and additional works at: [https://aisel.aisnet.org/confirm2025](https://aisel.aisnet.org/confirm2025) |  |\n\nRecommended Citation  \nLubaina, Navaid; Iradat, Faisal; and Taşkın, Nazım, \"CYBERBULLYING DETECTION WITH MACHINE LEARNING & PSYCHOLOGY: A SYSTEMATIC REVIEW\" (2025) . CONF-IRM 2025 Proceedings. 20.  \n[https://aisel.aisnet.org/confirm2025/20](https://aisel.aisnet.org/confirm2025/20)  \nThis material is brought to you by the International Conference on Information Resources Management (CONFIRM) at AIS Electronic Library (AISeL) . It has been accepted for inclusion in CONF-IRM 2025 Proceedings by an authorized administrator of AIS Electronic Library (AISeL) . For more information, please contact [elibrary@aisnet.org](elibrary@aisnet.org).  \n8. GENERATIVE ARTIFICIAL INTELLIGENCE AND DIGITAL TRANSFORMATION IN CONTACT CENTER  \nBUSINESSES  \nShafiq Alam School of Management & Marketing, Massey University, New Zealand [salam1@massey.ac.nz](salam1@massey.ac.nz)  \nLorraine Skelton  \nOtago Polytechnic, Auckland International Campus, Auckland, New Zealand [lorraine.skelton@op.ac.nz](lorraine.skelton@op.ac.nz)  \nWindy Dharmawan Otago Polytechnic, Auckland International Campus, Auckland, New Zealand [dharw1@student.op.ac.nz](dharw1@student.op.ac.nz)  \nMuhammad Sohaib Ayub Data Science Institute, University of Galway, Galway, Ireland [0133839s@universityofgalway.ie](0133839s@universityofgalway.ie)  \nAbstract  \nIn today’s business landscape, digital transformation is critical to competitiveness, with generative AI (GAI) offering significant potential to improve customer interactions and operational efficiency-yet its adoption in contact centres faces challenges such as technical complexity, data privacy concerns, and resistance to change. In this study, we explore these barriers by surveying contact centre personnel. Our findings reveal that security risks, potential misuse, and process complexity drive reluctance toward GAI adoption, along with a knowledge gap to maximize its benefits. The study also uncovers obstacles at all levels of employees and stresses the need for strong governance, multi-stakeholder collaboration, a focus on data ethics, bias mitigation, and risk management. We recommend addressing these challenges to ensure successful integration of GAI, providing valuable insights for organizations that navigate digital transformation while balancing innovation with responsible implementation.  \nKeywords: Generative AI (GAI), Contact Centres, Digital Transformation, Adoption Barriers  \n1 Introduction  \nContemporary businesses have prioritized digital transformation to stay competitive in the rapidly evolving technological landscape (Astapciks, 2023) . Digital transformation is crucial for businesses across industries, enabling them to adapt and thrive by integrating digital tools into their operations (Bumann & Peter, 2019) . This transformation involves reimagining business models, enhancing customer service, and improving overall productivity (Rogers, 2016). Technology has also heightened customer expectations, driving the need for businesses to innovate their models to meet these demands (Dwivedi et al., 2021) . As businesses strive to stay ahead, emerging technologies like Generative Artificial Intelligence (GAI) are playing an increasingly pivotal role in accelerating this digital shift. 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This supports selecting features and evaluation strategies beyond surface-level text patterns.\"},{\"question\":\"What are common gaps the review highlights?\",\"answer\":\"Gaps include limitations in datasets and annotation quality, reduced robustness across platforms, and incomplete treatment of psychological context. These issues motivate improved data practices and model evaluation.\"}]","CYBERBULLYING DETECTION WITH MACHINE LEARNING & PSYCHOLOGY: A SYSTEMATIC REVIEW - A SYSTEMATIC REVIEW | PDF",1785816276,35,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"cyberbullying-detection-with-machine-learning-psychology-a-systematic-review-a-systematic-review","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/cyberbullying-detection-with-machine-learning-psychology-a-systematic-review-a-systematic-review/123397/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What does the systematic review focus on?","Question",{"text":75,"@type":76},"The review focuses on cyberbullying detection using machine learning methods informed by psychological perspectives. 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