Automatic cyberbullying detection: A systematic review

Abstract Automatic cyberbullying detection is a task of growing interest, particularly in the Natural Language Processing and Machine Learning communities. Not only is it challenging, but it is also a relevant need given how social networks have become a vital part of individuals' lives and how dire the consequences of cyberbullying can be, especially among adolescents. In this work, we conduct an in-depth analysis of 22 studies on automatic cyberbullying detection, complemented by an experiment to validate current practices through the analysis of two datasets. Results indicated that cyberbullying is often misrepresented in the literature, leading to inaccurate systems that would have little real-world application. Criteria concerning cyberbullying definitions and other methodological concerns seem to be often dismissed. Additionally, there is no uniformity regarding the methodology to evaluate said systems and the natural imbalance of datasets remains an issue. This paper aims to direct future research on the subject towards a viewpoint that is more coherent with the definition and representation of the phenomenon, so that future systems can have a practical and impactful application. Recommendations on future works are also made.

[1]  Vikas S. Chavan,et al.  Machine learning approach for detection of cyber-aggressive comments by peers on social media network , 2015, 2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI).

[2]  Peter K. Smith,et al.  Cyberbullying: its nature and impact in secondary school pupils. , 2008, Journal of child psychology and psychiatry, and allied disciplines.

[3]  Ramón Fernández Astudillo,et al.  Page Rank Versus Katz: Is the Centrality Algorithm Choice Relevant to Measure User Influence in Twitter? , 2018 .

[4]  Amy Beth Warriner,et al.  Norms of valence, arousal, and dominance for 13,915 English lemmas , 2013, Behavior Research Methods.

[5]  Henry Lieberman,et al.  Common Sense Reasoning for Detection, Prevention, and Mitigation of Cyberbullying , 2012, TIIS.

[6]  Nitesh V. Chawla,et al.  Editorial: special issue on learning from imbalanced data sets , 2004, SKDD.

[7]  D. Olweus,et al.  Bullying at school : what we know and what we can do / Dan Olweus , 1993 .

[8]  Karolien Poels,et al.  "Thinking before posting?" Reducing cyber harassment on social networking sites through a reflective message , 2017, Comput. Hum. Behav..

[9]  Martin Lindström,et al.  Subjective health complaints in adolescent victims of cyber harassment: moderation through support from parents/friends - a Swedish population-based study , 2015, BMC Public Health.

[10]  Yoshua Bengio,et al.  A Neural Probabilistic Language Model , 2003, J. Mach. Learn. Res..

[11]  Hongxin Hu,et al.  Cyberbullying Detection with a Pronunciation Based Convolutional Neural Network , 2016, 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA).

[12]  Abhishek Agrawal,et al.  Automatic Monitoring and Prevention of Cyberbullying , 2016 .

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

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

[15]  Karolien Poels,et al.  The development of a serious game on cyberbullying: a concept test , 2015 .

[16]  Karolien Poels,et al.  Harmonizing freedom and protection: Adolescents' voices on automatic monitoring of social networking sites , 2016 .

[17]  Thengo Kavinya,et al.  A Deeper Look... , 2016 .

[18]  Jeffrey Dean,et al.  Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.

[19]  Yang Zhang,et al.  Cyberbullying Detection based on text-stream classification , 2013, AusDM 2013.

[20]  Rui Zhao,et al.  Automatic detection of cyberbullying on social networks based on bullying features , 2016, ICDCN.

[21]  Derek C. Glover,et al.  Bullying in 25 secondary schools: incidence, impact and intervention , 2000 .

[22]  C. Nixon.  Current perspectives: the impact of cyberbullying on adolescent health , 2014, Adolescent health, medicine and therapeutics.

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

[24]  Dolf Trieschnigg,et al.  Improving Cyberbullying Detection with User Context , 2013, ECIR.

[25]  Peter K. Smith,et al.  Bullying in schools: Lessons from two decades of research , 2000 .

[26]  Brian W. Sturm,et al.  Cyberbullying: From Playground to Computer , 2007 .

[27]  Gianluca Stringhini,et al.  Mean Birds: Detecting Aggression and Bullying on Twitter , 2017, WebSci.

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

[29]  Bruce Johnson,et al.  Behind the Scenes and Screens Insights into the Human Dimension of Covert and Cyberbullying , 2009 .

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

[31]  S. Bauman,et al.  Principles of cyberbullying research: definitions, measures, and methodology , 2013 .

[32]  Kenji Araki,et al.  Sustainable cyberbullying detection with category-maximized relevance of harmful phrases and double-filtered automatic optimization , 2016, Int. J. Child Comput. Interact..

[33]  Ricardo Ribeiro,et al.  Using Fuzzy Fingerprints for Cyberbullying Detection in Social Networks , 2018, 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).

[34]  C. Salmivalli.  Bullying and the peer group: A review , 2010 .

[35]  D. Olweus.  Cyberbullying: An overrated phenomenon? , 2012 .

[36]  Justin W. Patchin,et al.  Bullies Move Beyond the Schoolyard , 2006 .

[37]  Yulan He,et al.  Approaches to Automated Detection of Cyberbullying: A Survey , 2020, IEEE Transactions on Affective Computing.

[38]  Michelle F. Wright.  Cyberbullying in Cultural Context , 2017 .

[39]  Qing Li.  Cyberbullying in Schools , 2006 .

[40]  Dirk Helbing,et al.  Saving Human Lives: What Complexity Science and Information Systems can Contribute , 2014, Journal of statistical physics.

[41]  Margaret L. Kern,et al.  Personality, Gender, and Age in the Language of Social Media: The Open-Vocabulary Approach , 2013, PloS one.

[42]  Rui Zhao,et al.  Cyberbullying Detection Based on Semantic-Enhanced Marginalized Denoising Auto-Encoder , 2017, IEEE Transactions on Affective Computing.

[43]  Ricardo Ribeiro,et al.  A “Deeper” Look at Detecting Cyberbullying in Social Networks , 2018, 2018 International Joint Conference on Neural Networks (IJCNN).

[44]  April Kontostathis,et al.  Detecting the Presence of Cyberbullying Using Computer Software , 2011 .

[45]  Rajeev R. Raje,et al.  Collaborative detection of cyberbullying behavior in Twitter data , 2015, 2015 IEEE International Conference on Electro/Information Technology (EIT).

[46]  Shivakant Mishra,et al.  Prediction of cyberbullying incidents in a media-based social network , 2016, 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM).

[47]  P. Ferreira,et al.  University students’ perceptions of campus climate, cyberbullying and cultural issues: implications for theory and practice , 2018 .

[48]  Sofia Mateus Francisco,et al.  Cyberbullying: Shaping the use of verbal aggression through normative moral beliefs and self-efficacy , 2018, New Media Soc..

[49]  Billy Henson.  Bullying beyond the schoolyard: Preventing and responding to cyberbullying , 2012 .

[50]  Eric Gilbert,et al.  VADER: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text , 2014, ICWSM.

[51]  Pradeep K. Atrey,et al.  Cyberbullying detection using probabilistic socio-textual information fusion , 2016, 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM).

[52]  Nitesh V. Chawla,et al.  Data Mining for Imbalanced Datasets: An Overview , 2005, The Data Mining and Knowledge Discovery Handbook.

[53]  Kasturi Dewi Varathan,et al.  Cybercrime detection in online communications: The experimental case of cyberbullying detection in the Twitter network , 2016, Comput. Hum. Behav..

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