On power-law relationships of the Internet topology
Proceedings of the conference on Applications, technologies, architectures, and protocols for computer communication
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Slash(dot) and burn: distributed moderation in a large online conversation space
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Follow the (slash) dot: effects of feedback on new members in an online community
GROUP '05 Proceedings of the 2005 international ACM SIGGROUP conference on Supporting group work
Thumbs up?: sentiment classification using machine learning techniques
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Crowdsourcing user studies with Mechanical Turk
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Get another label? improving data quality and data mining using multiple, noisy labelers
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
When Online Communities Become Self-Aware
HICSS '09 Proceedings of the 42nd Hawaii International Conference on System Sciences
Financial incentives and the "performance of crowds"
Proceedings of the ACM SIGKDD Workshop on Human Computation
FeatureEng '05 Proceedings of the ACL Workshop on Feature Engineering for Machine Learning in Natural Language Processing
Fast, cheap, and creative: evaluating translation quality using Amazon's Mechanical Turk
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
A collusion-resistant automation scheme for social moderation systems
CCNC'09 Proceedings of the 6th IEEE Conference on Consumer Communications and Networking Conference
How useful are your comments?: analyzing and predicting youtube comments and comment ratings
Proceedings of the 19th international conference on World wide web
Rethinking grammatical error annotation and evaluation with the Amazon Mechanical Turk
IUNLPBEA '10 Proceedings of the NAACL HLT 2010 Fifth Workshop on Innovative Use of NLP for Building Educational Applications
Tokenizing micro-blogging messages using a text classification approach
AND '10 Proceedings of the fourth workshop on Analytics for noisy unstructured text data
Normative influences on thoughtful online participation
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Profanity use in online communities
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
| Hi-index | 0.00 |
As online communities grow and the volume of user-generated content increases, the need for community management also rises. Community management has three main purposes: to create a positive experience for existing participants, to promote appropriate, socionormative behaviors, and to encourage potential participants to make contributions. Research indicates that the quality of content a potential participant sees on a site is highly influential; off-topic, negative comments with malicious intent are a particularly strong boundary to participation or set the tone for encouraging similar contributions. A problem for community managers, therefore, is the detection and elimination of such undesirable content. As a community grows, this undertaking becomes more daunting. Can an automated system aid community managers in this task? In this paper, we address this question through a machine learning approach to automatic detection of inappropriate negative user contributions. Our training corpus is a set of comments from a news commenting site that we tasked Amazon Mechanical Turk workers with labeling. Each comment is labeled for the presence of profanity, insults, and the object of the insults. Support vector machines trained on these data are combined with relevance and valence analysis systems in a multistep approach to the detection of inappropriate negative user contributions. The system shows great potential for semiautomated community management. © 2012 Wiley Periodicals, Inc.