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Do characters abuse more than words?
- 1. Do Characters Abuse More Than Words? By: Yashar Mehdad Joel Tetreault (Yahoo! Research) 1
- 2. Outline 1. Introduction 2. Related Work 3. Methodology 4. Evaluation 5. Conclusion 6. Review 2
- 4. Introduction Rise of online communities Connect different communities Ease of communication “Hurl insults, bully & threaten through the use of profanity & hate speech” Abusive Language 4
- 5. “Kill all Jews, they are a headache” Straightforward methods to handle blacklists & regular expressions “Kill yrself a$$hole” Tokenization & Normalization issue Complex methods needed 5
- 7. 2. Related Work Profanity detection Sood et al 2012 Hate speech detection Warner & Hirschberg 2012 Cyberbullying Dadvar et al 2013 Generally abusive language detection Chen et al 2012, Djuric et al 2015 7
- 8. Majority of the work focused on supervised classification with canonical NLP features Token n-gram features Hand crafted regular expressions & blacklist features 8
- 9. Features which model the user’s past behavior Dadvar et al 2013 Semi supervised LDA approach Xiang et al 2012 Paragraph2vec approach Djuric et al 2015 Usage of many features Nobata et al 2016 9
- 11. 3. Methodology Supervised classification methods with lexical & morphological features to measure various aspects of user comments Hybrid method based on discriminative & generative classifiers Binary classification task (abusive or not) 11
- 12. Feature classes Tokens Characters Distributional semantics Methods Distributional Representation of Comments (C2V) Recurrent Neural Network Language Model (RNNLM) Support Vector Machine with Naïve Bayes (NBSVM) 12
- 13. 3.1. Distributional Representation of Comments (C2V) Modeling lexical semantics using vector space model (Mikolov et al 2013) A vector representation with comment embeddings A skip-bigram model to train the embeddings of the words in comments 13
- 14. C2V Mikolov et al. 2014 14
- 15. C2V Window size 5 & 10 Low dimensional models (100,300) d300w10 d300w5 d100w10 d100w5 10 Iterations Multi-core LibLinear Library Logisitc regression classifier 15
- 16. 3.2. Recurrent Neural Network Language Model (RNNLM) RNN is potential in representing more advanced patterns Models are trained for both classes (abusive & clean) Token n-gram where n=1,2,3,4,5 Character n-grams where n=1,2,3,4,5 Space is considered as a character to investigate character Vs word claim 16
- 17. RNNLM Testing:- Estimate the ratio of probability of the comment belonging to each class via bayes rule If the probability of a comment given the abusive language model is higher than its probability given non-abusive language model, then the comment is classified as abusive 17
- 18. RNNLM Ratios are used to calculate AUC matrix RNNLM toolkit implemented in Mikolov 2011 One “word” model Two character models as “char1” & “char2” Bptt:- No. of steps to propagate error back 18 word char1 char2 No. of hidden layers 50 50 200 bptt 4 4 10
- 19. 3.3. Support Vector machine with Naïve Bayes Features (NBSVM) SVM + NB Compute log ratio vector between the average character n-gram counts from abusive & non-abusive comments Input to the SVM Log ratio vector * binary pattern for each character n-gram in comment vector Multi-core LibLinear Library 19
- 20. 4. EVALUATION 20
- 21. 4. Evaluation Dataset:- Data sets used in Djuric et al & Nobata et al Labels:- From a combination of in-house raters users reactively flagging bad comments & abusive language pattern detectors 5 fold cross validation & report AUC Recall, precision & F1 score A token n-gram classifier with logistic regression classifier 21
- 22. Results 22
- 23. 5. CONCLUSION 23
- 24. 5. Conclusion Character based approaches fared best in cases with irregular normalization or obfuscation of words Has shown the superiority of simple character-based approaches over the previous state-of-art, as well as token- based ones & two deep learning approaches 24
- 25. THANK YOU 25
Editor's Notes
- The rise of online communities over the last ten years, in various forms such as message boards, twitter, discussion forums, etc., have allowed people from disparate backgrounds to connect in a way that would not have been possible before. However, the ease of communication online has made it possible for both anonymous and non-anonymous posters to
- Straightforward methods to handle abusive languages like …………………….. They are concious bastardizations of words in an effort to evade blacklists So characters often play an important role in the comment language
- In this model, every paragraph is mapped to a uniques vector represented by a column in a matrix and every word is also mapped to a unique vector represented by a column in a matrix The paragraph vector & word vectors are averaged or concatenated to predict the next word in a context
- After being trained the paragraph vectors can be used as features for the paragrap can be used to feed these features directly to conventional ML techniques such as logistic regression svm Or k-means Advantages Can work for tasks that do not have enough labeled data Take into consideration the word order at least in a small context in the same way that an n-gram model with a large n would do