[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121854-en":3,"doc-seo-121854-105":29,"detail-sidebar-cat-0-en-105":89},{"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":20,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},121854,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Suitability of Various Machine Learning Approaches for Recognition of Antisocial Behaviour on Social Networks - research report","Social networks enable users to publicly express agreement, disagreement, and opinions, but this openness is frequently abused, leading to offensive commentary. Growth in online text data has driven web mining research that analyzes short posts to identify hate and other harmful communication. The study focuses on recognizing antisocial behaviour, including hate speech, offensive posts, and cyberbullying. It evaluates classic, deep, and ensemble machine learning strategies using LSTM/GRU, SVM/NB/DT, and RF/AdaBoost, testing performance across three dataset sizes to assess how training data volume affects accuracy.","5th International Conference on Advanced Research Methods and Analytics (CARMA2023) Universidad de Sevilla, Sevilla, 2023  \nSuitability of various machine learning approaches for recognition of antisocial behaviour on social networks  \nKristína Machová1, Tomáš Tomčík1  \n1Department of Cybernetics and Artificial Intelligence, Technical University of Košice, Slovakia.  \nAbstract  \nNowadays, social networks allow web users to express publicly agreement or disagreement with other people and express freely their opinions. This freedom is often abused and that is why we can see social networks that are full of offensive comments. The increase in textual data on the Internet has stimulated the emergence of new scientific fields as web mining that examine short texts in the online space and look for hate or offensive speech, and that try to analyze textual data in online space. Our paper is focused on a special type of analysis concentrated on detection of some forms of antisocial behaviour, particularly on hate speech, offensive posts, and cyberbullying recognition in the online space. The main goal of the work was to find out which of the machine learning strategies-classic, deep or ensemble-are the most effective in detecting of these forms of antisocial behaviour on social networks. We have compared models generated by the following methods: deep learning of neural networks (LSTM, and GRU), classical methods (SVM, NB, and DT), and ensemble learning (RF, AdaBoost). We have tested those methods on three datasets created from posts of various volume to find how the volume of data available for training affects the results of machine learning models. The best result on the smallest Hate Speech Dataset were achieved by ensemble learning using AdaBoost (Accuracy=0,904). On the other hand, the best result on the largest Offensive Speech Dataset was achieved by deep learning using GRU (Accuracy=0.964).  \nKeywords: Machine learning; deep learning; ensemble learning; social web mining; detection of antisocial behaviour.  \nThis work is licensed under a Creative Commons License CC BY-NC-SA 4.0  \nEditorial Universitat Politcnica de Valncia 101","cbCaivFS8warL5VR","https://ap.wps.com/l/cbCaivFS8warL5VR","pdf",306019,1,"English","en",105,"# Abstract\n## Problem and objective\n## Methods and models\n## Experimental datasets and data volume\n## Results by dataset size","[{\"question\":\"What antisocial behaviours does the study aim to recognize?\",\"answer\":\"The work targets hate speech, offensive posts, and cyberbullying in online social network text.\"},{\"question\":\"Which machine learning strategy types are compared?\",\"answer\":\"It compares classic methods (SVM, NB, DT), deep learning with neural networks (LSTM, GRU), and ensemble learning (RF, AdaBoost).\"},{\"question\":\"How does training data volume affect the results?\",\"answer\":\"Models are tested on three datasets with different post volumes to observe how available training data influences detection accuracy.\"}]","Suitability of Various Machine Learning Approaches for Recognition of Antisocial Behaviour on Social Networks - 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