[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121754-en":3,"doc-seo-121754-105":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":20,"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},121754,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","Machine Learning Methods for Detection of Bystanders - A Survey","Social media usage continues to expand, bringing increased exposure to online harassment, abusive language, and related cyberbullying. Victims may experience psychological distress, antisocial behaviour, and severe outcomes such as suicide. Because bystanders witness incidents and can reduce harm, their role is central to mitigation. This survey reviews cyberbullying content in the internet domain, including category classification, author-role categorisation (harasser, victim, defender, assistant), data sources, and machine learning approaches for detection.","Machine Learning Methods for Detection of Bystanders: A Survey  \nKhushboo Thakkar, Anamika Gupta, Vibhor Mathur, Aman Tiwari  \nSSCBS, University of Delhi, Delhi, India.  \nAbstract:- The number of users on social media networks is increasing daily due to the rising popularity of these platforms. These users share their photos, videos, daily experiences, views, and status updates on various social networking sites. While social networking sites offer great possibilities for young people to interact with others, they also expose them to unpleasant phenomena such as online harassment and abusive language, resulting in cyberbullying. Cyberbullying is a pervasive social problem that has detrimental consequences for the health and safety of its victims, including psychological distress, anti-social behaviour, and even suicide. The Bystander role plays a crucial part in minimising the impact of cyberbullying. This paper presents a review of cyberbullying content on the internet, the classification of cyberbullying categories, the categorisation of author roles (harasser, victim, bystander-defender, bystander-assistant), data sources, and machine learning techniques for detecting cyberbullying.  \nIntroduction  \nSocial media platforms have evolved into remarkable tools for connecting individuals worldwide. However, as these online platforms gain popularity in the digital realm, some utilise them positively, while others engage in reprehensible actions. Cyberbullying is one concerning issue that has emerged due to the proliferation of social media platforms [1] . Cyberbullying entails using digital technology, such as smartphones, computers, and tablets, to engage in bullying behaviours. It can transpire through applications, online social media, forums, and gaming platforms where users interact, exchange content, or participate in discussions. Cyberbullying encompasses acts such as sending, uploading, or disseminating hurtful, false, derogatory content about others, including disclosing personal or private information  \nleading to embarrassment or humiliation. Certain forms of cyberbullying are even illegal or criminal [2] .  \nThe impact of cyberbullying on youth is considerable. In the context of cyberbullying, bystanders are individuals who witness bullying incidents online, which may involve even strangers. Witnessing cyberbullying is distressing and affects bystanders as well. Bystanders hold the potential to make a positive impact in such situations by assuming various responsibilities. The presence of supportive peers can alleviate the distress and unhappiness experienced by bullied individuals. In fact, during instances of bullying, bystanders are present 80% of the time, and when they intervene, the bullying ceases in 57% of cases within 10 seconds [3] .  \nToxic behaviour often unfolds in the presence of bystanders. In such scenarios, bystanders can assume different roles to alter the dynamics of social situations. Bystanders play a crucial role in handling situations involving toxic behaviour. They can react in three ways: mirroring the perpetrator’s toxic behaviour (inadvisable), hindering the toxic conversation and standing up for the victim (recommended), or simply observing the unfolding events. The dynamics of bystander engagement in prosocial behaviour within cyberspace in response to hate speech, cyberbullying, or trolling are intricate. This complexity arises because the presence of other internet users might lessen one’s sense of responsibility to intervene, assuming that someone else will take action. However, in smaller groups, bystanders feel a stronger obligation to intervene in instances of cyberbullying [4] . Bystanders play an essential role in preventing and intervening in bullying. Their roles encompass various aspects, such as Outsiders are Individuals who observe the situation without getting involved. Defenders are  \nIndividuals who intervene and support the victim. Reinforcers are Individuals who support a","cbCaikrIzsh97GsZ","https://ap.wps.com/l/cbCaikrIzsh97GsZ","pdf",114795,1,9,"English","en",105,"# Introduction\n## Cyberbullying and its impacts\n## The role and dynamics of bystanders\n# Types of Cyberbullying\n## Flaming\n## Harassment\n## Cyberstalking\n## Masquerade\n## Trolling\n## Denigration","[{\"question\":\"What is the focus of the survey on bystander detection?\",\"answer\":\"The survey focuses on machine learning methods for detecting bystanders in cyberbullying contexts, including how bystander roles relate to intervention or participation.\"},{\"question\":\"Which author and bystander roles are discussed in the document?\",\"answer\":\"The document categorises roles such as harasser, victim, bystander-defender, and bystander-assistant, distinguishing supportive intervention from reinforcement of bullying.\"},{\"question\":\"What are the main types of cyberbullying covered?\",\"answer\":\"It covers common types including flaming, harassment, cyberstalking, masquerade, trolling, and denigration, each with distinct behaviours and online patterns.\"}]","Machine Learning Methods for Detection of Bystanders - 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