2020
EMNLP
EMNLP 2020
A Unified Taxonomy of Harmful Content
Abstract
AbstractThe ability to recognize harmful content within online communities has come into focus for researchers, engineers and policy makers seeking to protect users from abuse. While the number of datasets aiming to capture forms of abuse has grown in recent years, the community has not standardized around how various harmful behaviors are defined, creating challenges for reliable moderation, modeling and evaluation. As a step towards attaining shared understanding of how online abuse may be modeled, we synthesize the most common types of abuse described by industry, policy, community and health experts into a unified typology of harmful content, with detailed criteria and exceptions for each type of abuse.
🧭
Keyword Pioneer
— harmful content detection
🐣
Hot Topic Early Bird
— content moderation
🐝
Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Machine Learning, Natural Language Processing, Reinforcement Learning, Security & Privacy, Speech & Audio