Covering Cracks in Content Moderation: Delexicalized Distant Supervision for Illicit Drug Jargon Detection
This addresses the challenge of content moderation for social media platforms by improving detection of evolving drug-related jargon, though it is incremental as it builds on existing distant supervision methods.
The paper tackled the problem of detecting illicit drug jargon in social media content moderation by proposing JEDIS, a framework that uses context analysis instead of banlists, and it significantly outperformed state-of-the-art word-based baselines in F1-score and detection coverage on two manually annotated datasets.
In light of rising drug-related concerns and the increasing role of social media, sales and discussions of illicit drugs have become commonplace online. Social media platforms hosting user-generated content must therefore perform content moderation, which is a difficult task due to the vast amount of jargon used in drug discussions. Previous works on drug jargon detection were limited to extracting a list of terms, but these approaches have fundamental problems in practical application. First, they are trivially evaded using word substitutions. Second, they cannot distinguish whether euphemistic terms such as "pot" or "crack" are being used as drugs or in their benign meanings. We argue that drug content moderation should be done using contexts rather than relying on a banlist. However, manually annotated datasets for training such a task are not only expensive but also prone to becoming obsolete. We present JEDIS, a framework for detecting illicit drug jargon terms by analyzing their contexts. JEDIS utilizes a novel approach that combines distant supervision and delexicalization, which allows JEDIS to be trained without human-labeled data while being robust to new terms and euphemisms. Experiments on two manually annotated datasets show JEDIS significantly outperforms state-of-the-art word-based baselines in terms of F1-score and detection coverage in drug jargon detection. We also conduct qualitative analysis that demonstrates JEDIS is robust against pitfalls faced by existing approaches.