Attending the Emotions to Detect Online Abusive Language

In recent years, abusive behavior has become a serious issue in online social networks. In this paper, we present a new corpus from a semi-anonymous social media platform, which contains the instances of offensive and neutral classes. We introduce a single deep neural architecture that considers both local and sequential information from the text in order to detect abusive language. Along with this model, we introduce a new attention mechanism called emotion-aware attention. This mechanism utilizes the emotions behind the text to find the most important words within that text. We experiment with this model on our dataset and later present the analysis. Additionally, we evaluate our proposed method on different corpora and show new state-of-the-art results with respect to offensive language detection.

[1]  K. Mitchell,et al.  Online Harassment in Context: Trends From Three Youth Internet Safety Surveys (2000, 2005, 2010) , 2013 .

[2]  Han Liu,et al.  Suspended Accounts: A Source of Tweets with Disgust and Anger Emotions for Augmenting Hate Speech Data Sample , 2018, 2018 International Conference on Machine Learning and Cybernetics (ICMLC).

[3]  Helen Yannakoudakis,et al.  Neural Character-based Composition Models for Abuse Detection , 2018, ALW.

[4]  Fabio A. González,et al.  A Genre-Aware Attention Model to Improve the Likability Prediction of Books , 2018, EMNLP.

[5]  Helen Yannakoudakis,et al.  Author Profiling for Hate Speech Detection , 2019, ArXiv.

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

[7]  Dirk Hovy,et al.  Hateful Symbols or Hateful People? Predictive Features for Hate Speech Detection on Twitter , 2016, NAACL.

[8]  Virgílio A. F. Almeida,et al.  Characterizing and Detecting Hateful Users on Twitter , 2018, ICWSM.

[9]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[10]  Fabio A. González,et al.  Gated Multimodal Units for Information Fusion , 2017, ICLR.

[11]  Ming-Wei Chang,et al.  BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.

[12]  Björn Gambäck,et al.  Using Convolutional Neural Networks to Classify Hate-Speech , 2017, ALW@ACL.

[13]  Njagi Dennis Gitari,et al.  A Lexicon-based Approach for Hate Speech Detection , 2015, MUE 2015.

[14]  David Robinson,et al.  Detecting Hate Speech on Twitter Using a Convolution-GRU Based Deep Neural Network , 2018, ESWC.

[15]  Saif Mohammad,et al.  CROWDSOURCING A WORD–EMOTION ASSOCIATION LEXICON , 2013, Comput. Intell..

[16]  Helen Yannakoudakis,et al.  Abusive Language Detection with Graph Convolutional Networks , 2019, NAACL.

[17]  Yejin Choi,et al.  The Risk of Racial Bias in Hate Speech Detection , 2019, ACL.

[18]  Helen Yannakoudakis,et al.  Tackling Online Abuse: A Survey of Automated Abuse Detection Methods , 2019, ArXiv.

[19]  Scott A. Hale,et al.  Challenges and frontiers in abusive content detection , 2019, Proceedings of the Third Workshop on Abusive Language Online.

[20]  Yaser Al-Onaizan,et al.  Neural Word Decomposition Models for Abusive Language Detection , 2019, ArXiv.

[21]  Ingmar Weber,et al.  Automated Hate Speech Detection and the Problem of Offensive Language , 2017, ICWSM.

[22]  Serena Villata,et al.  Comparing Different Supervised Approaches to Hate Speech Detection , 2018, EVALITA@CLiC-it.

[23]  Iyad Rahwan,et al.  Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm , 2017, EMNLP.

[24]  J. Fleiss.  Measuring nominal scale agreement among many raters. , 1971 .

[25]  Thamar Solorio,et al.  RiTUAL-UH at TRAC 2018 Shared Task: Aggression Identification , 2018, TRAC@COLING 2018.

[26]  Helen Yannakoudakis,et al.  Joint Modelling of Emotion and Abusive Language Detection , 2020, ACL.

[27]  Michael Wiegand,et al.  Inducing a Lexicon of Abusive Words – a Feature-Based Approach , 2018, NAACL.

[28]  Luke S. Zettlemoyer,et al.  Deep Contextualized Word Representations , 2018, NAACL.

[29]  A. F. Adams,et al.  The Survey , 2021, Dyslexia in Higher Education.

[30]  Michael Wiegand,et al.  A Survey on Hate Speech Detection using Natural Language Processing , 2017, SocialNLP@EACL.

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

[32]  Yoon Kim,et al.  Convolutional Neural Networks for Sentence Classification , 2014, EMNLP.

[33]  Lei Gao,et al.  Detecting Online Hate Speech Using Context Aware Models , 2017, RANLP.

[34]  Thamar Solorio,et al.  Detecting Nastiness in Social Media , 2017, ALW@ACL.

[35]  Ziqi Zhang,et al.  Hate Speech Detection: A Solved Problem? The Challenging Case of Long Tail on Twitter , 2018, Semantic Web.

[36]  Pedro Rangel Henriques,et al.  Hate Speech Classification in Social Media Using Emotional Analysis , 2018, 2018 7th Brazilian Conference on Intelligent Systems (BRACIS).

[37]  Vasudeva Varma,et al.  Deep Learning for Hate Speech Detection in Tweets , 2017, WWW.

[38]  Joel R. Tetreault,et al.  Abusive Language Detection in Online User Content , 2016, WWW.

[39]  Anna Koufakou,et al.  Lexicon-Enhancement of Embedding-based Approaches Towards the Detection of Abusive Language , 2020, TRAC.

[40]  Alexander F. Gelbukh,et al.  Aggression Detection in Social Media: Using Deep Neural Networks, Data Augmentation, and Pseudo Labeling , 2018, TRAC@COLING 2018.

[41]  Arjan Durresi,et al.  A survey: Control plane scalability issues and approaches in Software-Defined Networking (SDN) , 2017, Comput. Networks.

[42]  Lucas Dixon,et al.  Ex Machina: Personal Attacks Seen at Scale , 2016, WWW.

[43]  Yoshua Bengio,et al.  Neural Machine Translation by Jointly Learning to Align and Translate , 2014, ICLR.

[44]  Michele L. Ybarra,et al.  Youth engaging in online harassment: associations with caregiver-child relationships, Internet use, and personal characteristics. , 2004, Journal of adolescence.