Abstract
As user-generated Web content increases, the amount of inappropriate and/or objectionable content also grows. Several scholarly communities are addressing how to detect and manage such content: research in computer vision focuses on detection of inappropriate images, natural language processing technology has advanced to recognize insults. However, profanity detection systems remain flawed. Current list-based profanity detection systems have two limitations. First, they are easy to circumvent and easily become stale-that is, they cannot adapt to misspellings, abbreviations, and the fast pace of profane slang evolution. Secondly, they offer a one-size fits all solution; they typically do not accommodate domain, community and context specific needs. However, social settings have their own normative behaviors-what is deemed acceptable in one community may not be in another. In this paper, through analysis of comments from a social news site, we provide evidence that current systems are performing poorly and evaluate the cases on which they fail. We then address community differences regarding creation/tolerance of profanity and suggest a shift to more contextually nuanced profanity detection systems.
Original language | English (US) |
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Title of host publication | Conference Proceedings - The 30th ACM Conference on Human Factors in Computing Systems, CHI 2012 |
Pages | 1481-1490 |
Number of pages | 10 |
DOIs | |
State | Published - May 24 2012 |
Event | 30th ACM Conference on Human Factors in Computing Systems, CHI 2012 - Austin, TX, United States Duration: May 5 2012 → May 10 2012 |
Other
Other | 30th ACM Conference on Human Factors in Computing Systems, CHI 2012 |
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Country | United States |
City | Austin, TX |
Period | 5/5/12 → 5/10/12 |
Keywords
- Comment threads
- Community management
- Negativity
- Online communities
- Profanity
- User-generated content
ASJC Scopus subject areas
- Software
- Human-Computer Interaction
- Computer Graphics and Computer-Aided Design