Early detection of cyberbullying on social media networks

[1]  Fabio Crestani,et al.  eRisk 2020: Self-harm and Depression Challenges , 2020, ECIR.

[2]  Benjamin C. M. Fung,et al.  Detecting breaking news rumors of emerging topics in social media , 2020, Inf. Process. Manag..

[3]  Htin Zaw Soe,et al.  Assessing risk factors and impact of cyberbullying victimization among university students in Myanmar: A cross-sectional study , 2020, PloS one.

[4]  William J. Tolone,et al.  Identifying malicious social media contents using multi-view Context-Aware active learning , 2019, Future Gener. Comput. Syst..

[5]  Francisco J. Novoa,et al.  Early Intrusion Detection for OS Scan Attacks , 2019, 2019 IEEE 18th International Symposium on Network Computing and Applications (NCA).

[6]  Fidel Cacheda,et al.  Early Detection of Depression: Social Network Analysis and Random Forest Techniques , 2019, Journal of medical Internet research.

[7]  Abdullah Gani,et al.  Predicting Cyberbullying on Social Media in the Big Data Era Using Machine Learning Algorithms: Review of Literature and Open Challenges , 2019, IEEE Access.

[8]  Ruocheng Guo,et al.  Hierarchical Attention Networks for Cyberbullying Detection on the Instagram Social Network , 2019, SDM.

[9]  Ricardo Ribeiro,et al.  Automatic cyberbullying detection: A systematic review , 2019, Comput. Hum. Behav..

[10]  He Jiang,et al.  Combating Fake News , 2019, ACM Trans. Intell. Syst. Technol..

[11]  Vivek K. Singh,et al.  See No Evil, Hear No Evil , 2018, Proc. ACM Hum. Comput. Interact..

[12]  Vivek K. Singh,et al.  Time Reveals All Wounds: Modeling Temporal Characteristics of Cyberbullying , 2018, ICWSM.

[13]  Steven Bethard,et al.  Measuring the Latency of Depression Detection in Social Media , 2018, WSDM.

[14]  Walter Daelemans,et al.  Automatic detection of cyberbullying in social media text , 2018, PloS one.

[15]  Jong Hyuk Park,et al.  Social network security: Issues, challenges, threats, and solutions , 2017, Inf. Sci..

[16]  Daniel Dajun Zeng,et al.  Detecting Social Bots by Jointly Modeling Deep Behavior and Content Information , 2017, CIKM.

[17]  Huan Liu,et al.  Sentiment Informed Cyberbullying Detection in Social Media , 2017, ECML/PKDD.

[18]  Suhang Wang,et al.  Fake News Detection on Social Media: A Data Mining Perspective , 2017, SKDD.

[19]  Cody Buntain,et al.  Automatically Identifying Fake News in Popular Twitter Threads , 2017, 2017 IEEE International Conference on Smart Cloud (SmartCloud).

[20]  Michael S. Bernstein,et al.  Anyone Can Become a Troll: Causes of Trolling Behavior in Online Discussions , 2017, CSCW.

[21]  Shivakant Mishra,et al.  Analysis and detection of labeled cyberbullying instances in Vine, a video-based social network , 2016, Social Network Analysis and Mining.

[22]  Fabio Crestani,et al.  A Test Collection for Research on Depression and Language Use , 2016, CLEF.

[23]  Kate Starbird,et al.  How Information Snowballs: Exploring the Role of Exposure in Online Rumor Propagation , 2016, CSCW.

[24]  Kate Starbird,et al.  Keeping Up with the Tweet-dashians: The Impact of 'Official' Accounts on Online Rumoring , 2016, CSCW.

[25]  Igor Santos,et al.  Supervised machine learning for the detection of troll profiles in twitter social network: application to a real case of cyberbullying , 2015, Log. J. IGPL.

[26]  Walter Daelemans,et al.  Detection and Fine-Grained Classification of Cyberbullying Events , 2015, RANLP.

[27]  Shivakant Mishra,et al.  Careful what you share in six seconds: Detecting cyberbullying instances in Vine , 2015, 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM).

[28]  Qiaozhu Mei,et al.  Enquiring Minds: Early Detection of Rumors in Social Media from Enquiry Posts , 2015, WWW.

[29]  Qianjia Huang,et al.  Cyber Bullying Detection Using Social and Textual Analysis , 2014, SAM '14.

[30]  Hugues Sampasa-Kanyinga,et al.  Associations between Cyberbullying and School Bullying Victimization and Suicidal Ideation, Plans and Attempts among Canadian Schoolchildren , 2014, PloS one.

[31]  Robin M. Kowalski,et al.  Psychological, physical, and academic correlates of cyberbullying and traditional bullying. , 2013, The Journal of adolescent health : official publication of the Society for Adolescent Medicine.

[32]  Kelly Reynolds,et al.  Detecting cyberbullying: query terms and techniques , 2013, WebSci.

[33]  Sushil Jajodia,et al.  Detecting Automation of Twitter Accounts: Are You a Human, Bot, or Cyborg? , 2012, IEEE Transactions on Dependable and Secure Computing.

[34]  Ying Chen,et al.  Detecting Offensive Language in Social Media to Protect Adolescent Online Safety , 2012, 2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing.

[35]  Jun-Ming Xu,et al.  Learning from Bullying Traces in Social Media , 2012, NAACL.

[36]  R. Ordelman,et al.  Improved cyberbullying detection using gender information , 2012 .

[37]  Kelly Reynolds,et al.  Using Machine Learning to Detect Cyberbullying , 2011, 2011 10th International Conference on Machine Learning and Applications and Workshops.

[38]  Dragomir R. Radev,et al.  Rumor has it: Identifying Misinformation in Microblogs , 2011, EMNLP.

[39]  Henry Lieberman,et al.  Modeling the Detection of Textual Cyberbullying , 2011, The Social Mobile Web.

[40]  Terrill F. Saxon,et al.  Internalizing problems among cyberbullying victims and moderator effects of friendship quality , 2011 .

[41]  G. S. O'Keeffe,et al.  The Impact of Social Media on Children, Adolescents, and Families , 2011, Pediatrics.

[42]  Barbara Poblete,et al.  Information credibility on twitter , 2011, WWW.

[43]  Justin W. Patchin,et al.  Bullying, Cyberbullying, and Suicide , 2010, Archives of suicide research : official journal of the International Academy for Suicide Research.

[44]  Robert S. Tokunaga,et al.  Following you home from school: A critical review and synthesis of research on cyberbullying victimization , 2010, Comput. Hum. Behav..

[45]  Peter K. Smith,et al.  Cyberbullying: another main type of bullying? , 2008, Scandinavian journal of psychology.

[46]  Rodrigo Salas,et al.  Robust Alternating AdaBoost , 2007, CIARP.

[47]  D. Olweus,et al.  Bullying in School , 2017 .

[48]  Fabio Crestani,et al.  Overview of eRisk at CLEF 2019: Early Risk Prediction on the Internet (extended overview) , 2019, CLEF.

[49]  Marcelo Luis Errecalde,et al.  UNSL at eRisk 2019: a Unified Approach for Anorexia, Self-harm and Depression Detection in Social Media , 2019, CLEF.

[50]  Victor Carneiro,et al.  Analysis and Experiments on Early Detection of Depression , 2018, CLEF.

[51]  Monther Aldwairi,et al.  Detecting Fake News in Social Media Networks , 2018, EUSPN/ICTH.

[52]  Marcelo Luis Errecalde,et al.  LIDIC - UNSL's Participation at eRisk 2017: Pilot Task on Early Detection of Depression , 2017, CLEF.

[53]  Sven Koitka,et al.  Linguistic Metadata Augmented Classifiers at the CLEF 2017 Task for Early Detection of Depression , 2017, CLEF.

[54]  Marten Risius,et al.  Automatic Detection of Fake News on Social Media Platforms , 2017, PACIS.

[55]  Narendra Shekokar,et al.  A Framework for Cyberbullying Detection in Social Network , 2015 .

[56]  Xue Li,et al.  An Effective Approach for Cyberbullying Detection , 2013 .

[57]  Liao Hong,et al.  Review of AdaBoost and Its Improvement , 2012 .

[58]  Anna Cinzia Squicciarini,et al.  Identification and characterization of cyberbullying dynamics in an online social network , 2022 .