Corpus ID: 14964721

Locate the Hate: Detecting Tweets against Blacks

@inproceedings{Kwok2013LocateTH,
  title={Locate the Hate: Detecting Tweets against Blacks},
  author={Irene Kwok and Yuzhou Wang},
  booktitle={AAAI},
  year={2013}
}
Although the social medium Twitter grants users freedom of speech, its instantaneous nature and retweeting features also amplify hate speech. [...] Key Result We apply a supervised machine learning approach, employing inexpensively acquired labeled data from diverse Twitter accounts to learn a binary classifier for the labels "racist" and "nonracist" The classifier has a 76% average accuracy on individual tweets, suggesting that with further improvements, our work can contribute data on the sources of anti…Expand
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References

SHOWING 1-5 OF 5 REFERENCES
Detecting Hate Speech on the World Wide Web
We present an approach to detecting hate speech in online text, where hate speech is defined as abusive speech targeting specific group characteristics, such as ethnic origin, religion, gender, orExpand
Offensive Language Detection Using Multi-level Classification
TLDR
An automatic flame detection method is described which extracts features at different conceptual levels and applies multi-level classification for flame detection and there is an auxiliary weighted pattern repository which improves accuracy by matching the text to its graded entries. Expand
Thumbs up? Sentiment Classification using Machine Learning Techniques
TLDR
This work considers the problem of classifying documents not by topic, but by overall sentiment, e.g., determining whether a review is positive or negative, and concludes by examining factors that make the sentiment classification problem more challenging. Expand
Comparing naive Bayes, decision trees, and SVM with AUC and accuracy
TLDR
It is proved that AUC is, in general, a better measure (defined precisely) than accuracy for evaluating performance of learning algorithms. Expand
In Proc
  • of the 2012 Workshop on LSM, 19-26.
  • 2012