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Towards Automatic Moderation of Online Hate Speech - Emily Spahn, March 2016
- 1. Online Hate Speech Towards automated moderation Emily Y. Spahn Galvanize Data Science Immersive, Seattle, Mar 2016
- 2. What is Hate Speech? Speech advocating incitement to harm based on the target's membership in a group Definition
- 3. The Problem with Hate Speech Online Alienates Users & Costs Time
- 4. AUTOMATE! ⇒ Build a model to predict if a comment is hate speech, and if so, against what group. What can we do about online hate speech?
- 5. DATA SOURCE: May 2015 Reddit comments
- 6. Data Used: subreddits & data labeling Reddit Comments from May 2015 54.5+ million comments with metadata, from 50,138 subreddits Hateful Subreddits 11 hateful subreddits 565,494 hateful comments: ● 56% body size ● 33.6% gender ● 9.4% race ● 1% religion Not Hateful Subreddits 13 not hateful subreddits 1,012,052 not hateful comments: ● 75% sometimes controversial but well-moderated subreddits ● 11.2 % gender ● 7.7 % religion ● 5.4 % body size ● 0.4 % race
- 7. Tools Used Computing & Analysis Natural Language Processing & Classification Modeling NLTK
- 8. Modeling TF-IDF on 1.1 million comments XGBoost multi-class classifier Word2Vec for word embeddings
- 9. TF-IDF: Term Frequency-Inverse Document Frequency words in comments Image from http://brandonrose.org/clustering matrix of numbers i : the word j: the document Bag of words + factor to weight rarely occurring words more than common ones
- 10. Gradient Boosted Trees Classifier From XGBoost Documentation Decision trees:
- 11. Gradient Boosted Trees Classifier From XGBoost Documentation Tree Ensembles
- 12. Gradient Boosted Trees Classifier Working on labeled data: Create one tree & run model Find residuals (differences between model result & labeled data) Create 2nd tree to fit to the residuals New results = results from 1st tree + those from 2nd tree Find new residuals Repeat, adding a tree to the model each time to fit the residuals, until you reach a cut off criteria.
- 13. ROC Curve: Examine classification model success Most important features fat like peopl just white dont fuck im becaus game jew women weight say
- 14. Potential Use Cases for the Predictive Model More time for the mods! User posts hateful comment Model flags comment as hateful Comment is in limbo until a human moderator reads it Human evaluates comment and publishes or deletes Power to the People! Indicate via user icons or status information those who have a recent history of hateful comments. Let site users decide if they want to read what this person has to say.
- 15. Word2Vec: Most Similar Words “fat” skinny ugly lazy lard fatshit fatass slenderman gtbanned stupid hamplanet skinny overweight obese underweight and muscular that body is anorexic
- 16. Thank You! Emily Y Spahn spahn@uw.edu @eyspahn https://github.com/eyspahn/OnlineHateSpeech Clip art in the presentation from https://openclipart.org/
- 17. Example Comment
- 18. Data Used: subreddits Hateful Subreddits Subreddit Name Comment Count Hate Type CoonTown 51979 Race WhiteRights 1352 Race Transfags 2362 Gender SlutJustice 209 Gender TheRedPill 59145 Gender KotakuInAction 128156 Gender IslamUnveiled 110 Religion GasTheKikes 919 Religion AntiPOZi 4740 Religion fatpeoplehate 311183 Size TalesofFateHate 5239 Size Not Hateful Subreddits Subreddit Name politics DebateReligion worldnews religion history islam blackladies Judaism lgbt BodyAcceptance TransSpace fatlogic TwoChromosomes women