TRAC-1 Shared Task on Aggression Identification: IIT(ISM)@COLING’18

This paper describes the work that our team bhanodaig did at Indian Institute of Technology (ISM) towards TRAC-1 Shared Task on Aggression Identification in Social Media for COLING 2018. In this paper we label aggression identification into three categories: Overtly Aggressive, Covertly Aggressive and Non-aggressive. We train a model to differentiate between these categories and then analyze the results in order to better understand how we can distinguish between them. We participated in two different tasks named as English (Facebook) task and English (Social Media) task. For English (Facebook) task System 05 was our best run (i.e. 0.3572) above the Random Baseline (i.e. 0.3535). For English (Social Media) task our system 02 got the value (i.e. 0.1960) below the Random Bseline (i.e. 0.3477). For all of our runs we used Long Short-Term Memory model. Overall, our performance is not satisfactory. However, as new entrant to the field, our scores are encouraging enough to work for better results in future.

[1]  Walid Magdy,et al.  Abusive Language Detection on Arabic Social Media , 2017, ALW@ACL.

[2]  Aurélien Lucchi,et al.  SwissCheese at SemEval-2016 Task 4: Sentiment Classification Using an Ensemble of Convolutional Neural Networks with Distant Supervision , 2016, *SEMEVAL.

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

[4]  Ming Zhou,et al.  Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification , 2014, ACL.

[5]  Saif Mohammad,et al.  #Emotional Tweets , 2012, *SEMEVAL.

[6]  Yuzhou Wang,et al.  Locate the Hate: Detecting Tweets against Blacks , 2013, AAAI.

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

[8]  François Chollet,et al.  Xception: Deep Learning with Depthwise Separable Convolutions , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

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

[10]  Ritesh Kumar,et al.  Benchmarking Aggression Identification in Social Media , 2018, TRAC@COLING 2018.

[11]  Shervin Malmasi,et al.  Challenges in discriminating profanity from hate speech , 2017, J. Exp. Theor. Artif. Intell..

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

[13]  John Salvatier,et al.  Theano: A Python framework for fast computation of mathematical expressions , 2016, ArXiv.

[14]  Matthew Leighton Williams,et al.  Cyber Hate Speech on Twitter: An Application of Machine Classification and Statistical Modeling for Policy and Decision Making , 2015 .

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

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

[17]  Hui-Po Su,et al.  Rephrasing Profanity in Chinese Text , 2017, ALW@ACL.

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

[19]  Walter Daelemans,et al.  A Dictionary-based Approach to Racism Detection in Dutch Social Media , 2016, ArXiv.

[20]  Nancy Ide,et al.  Distant Supervision for Emotion Classification with Discrete Binary Values , 2013, CICLing.

[21]  Ritesh Kumar,et al.  Aggression-annotated Corpus of Hindi-English Code-mixed Data , 2018, LREC.

[22]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[23]  Ingmar Weber,et al.  Understanding Abuse: A Typology of Abusive Language Detection Subtasks , 2017, ALW@ACL.

[24]  Quoc V. Le,et al.  Sequence to Sequence Learning with Neural Networks , 2014, NIPS.

[25]  Shervin Malmasi,et al.  Detecting Hate Speech in Social Media , 2017, RANLP.

[26]  Jing Zhou,et al.  Hate Speech Detection with Comment Embeddings , 2015, WWW.