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) |
|---|---|
| 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 |
|---|---|
| Country | United States |
| City | Austin, TX |
| Period | 5/5/12 → 5/10/12 |
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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
Cite this
}
Profanity use in online communities. / Sood, Sara Owsley; Antin, Judd; Churchill, Elizabeth F.
Conference Proceedings - The 30th ACM Conference on Human Factors in Computing Systems, CHI 2012. 2012. p. 1481-1490.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Profanity use in online communities
AU - Sood, Sara Owsley
AU - Antin, Judd
AU - Churchill, Elizabeth F.
PY - 2012/5/24
Y1 - 2012/5/24
N2 - 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.
AB - 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.
KW - Comment threads
KW - Community management
KW - Negativity
KW - Online communities
KW - Profanity
KW - User-generated content
UR - http://www.scopus.com/inward/record.url?scp=84862074729&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84862074729&partnerID=8YFLogxK
U2 - 10.1145/2207676.2208610
DO - 10.1145/2207676.2208610
M3 - Conference contribution
AN - SCOPUS:84862074729
SN - 9781450310154
SP - 1481
EP - 1490
BT - Conference Proceedings - The 30th ACM Conference on Human Factors in Computing Systems, CHI 2012
ER -