3 years ago

Cyber Hate Classification: 'Othering' Language And Paragraph Embedding.

Han Liu, Pete Burnap, Matthew Williams, Wafa Alorainy

Hateful and offensive language (also known as hate speech or cyber hate) posted and widely circulated via the World Wide Web can be considered as a key risk factor for individual and societal tension linked to regional instability. Automated Web-based hate speech detection is important for the observation and understanding trends of societal tension. In this research, we improve on existing research by proposing different data mining feature extraction methods. While previous work has involved using lexicons, bags-of-words or probabilistic parsing approach (e.g. using Typed Dependencies), they all suffer from a similar issue which is that hate speech can often be subtle and indirect, and depending on individual words or phrases can lead to a significant number of false negatives. This problem motivated us to conduct new experiments to identify subtle language use, such as references to immigration or job prosperity in a hateful context. We propose a novel 'Othering Lexicon' to identify these subtleties and we incorporate our lexicon with embedding learning for feature extraction and subsequent classification using a neural network approach. Our method first explores the context around othering terms in a corpus, and identifies context patterns that are relevant to the othering context. These patterns are used along with the othering pronoun and hate speech terms to build our 'Othering Lexicon'. Embedding algorithm has the superior characteristic that the similar words have a closer distance, which is helpful to train our classifier on the negative and positive classes. For validation, several experiments were conducted on different types of hate speech, namely religion, disability, race and sexual orientation, with F-measure scores for classifying hateful instances obtained through applying our model of 0.93, 0.95, 0.97 and 0.92 respective.

Publisher URL: http://arxiv.org/abs/1801.07495

DOI: arXiv:1801.07495v1

You might also like
Discover & Discuss Important Research

Keeping up-to-date with research can feel impossible, with papers being published faster than you'll ever be able to read them. That's where Researcher comes in: we're simplifying discovery and making important discussions happen. With over 19,000 sources, including peer-reviewed journals, preprints, blogs, universities, podcasts and Live events across 10 research areas, you'll never miss what's important to you. It's like social media, but better. Oh, and we should mention - it's free.

  • Download from Google Play
  • Download from App Store
  • Download from AppInChina

Researcher displays publicly available abstracts and doesn’t host any full article content. If the content is open access, we will direct clicks from the abstracts to the publisher website and display the PDF copy on our platform. Clicks to view the full text will be directed to the publisher website, where only users with subscriptions or access through their institution are able to view the full article.