Sex, drugs, and violence

Stefania Raimondo; Frank Rudzicz

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arXiv · 1608.03448

Sex, drugs, and violence

Stefania Raimondo·Frank Rudzicz

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Abstract

Automatically detecting inappropriate content can be a difficult NLP task, requiring understanding context and innuendo, not just identifying specific keywords. Due to the large quantity of online user-generated content, automatic detection is becoming increasingly necessary. We take a largely unsupervised approach using a large corpus of narratives from a community-based self-publishing website and a small segment of crowd-sourced annotations. We explore topic modelling using latent Dirichlet allocation (and a variation), and use these to regress appropriateness ratings, effectively automating rating for suitability. The results suggest that certain topics inferred may be useful in detecting latent inappropriateness -- yielding recall up to 96% and low regression errors.

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Stefania Raimondo, Frank Rudzicz. 2016-08-11. Sex, drugs, and violence. https://arxiv.org/abs/1608.03448

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