The Journal of Machine Learning Research
Recognizing contextual polarity in phrase-level sentiment analysis
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
The complex dynamics of collaborative tagging
Proceedings of the 16th international conference on World Wide Web
Designing novel review ranking systems: predicting the usefulness and impact of reviews
Proceedings of the ninth international conference on Electronic commerce
Finding high-quality content in social media
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Flickr tag recommendation based on collective knowledge
Proceedings of the 17th international conference on World Wide Web
Predicting information seeker satisfaction in community question answering
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Signpost from the masses: learning effects in an exploratory social tag search browser
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Facts or friends?: distinguishing informational and conversational questions in social Q&A sites
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
How opinions are received by online communities: a case study on amazon.com helpfulness votes
Proceedings of the 18th international conference on World wide web
Automatically assessing review helpfulness
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Exploiting social context for review quality prediction
Proceedings of the 19th international conference on World wide web
Impact and prospect of social bookmarks for bibliographic information retrieval
Proceedings of the 10th annual joint conference on Digital libraries
Information credibility on twitter
Proceedings of the 20th international conference on World wide web
What do you call it?: a comparison of library-created and user-created tags
Proceedings of the 11th annual international ACM/IEEE joint conference on Digital libraries
Identifying content for planned events across social media sites
Proceedings of the fifth ACM international conference on Web search and data mining
Finding and assessing social media information sources in the context of journalism
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Semantic tagging on historical maps
Proceedings of the 5th Annual ACM Web Science Conference
Are Some Tweets More Interesting Than Others? #HardQuestion
Proceedings of the Symposium on Human-Computer Interaction and Information Retrieval
| Hi-index | 0.00 |
Cultural institutions are increasingly opening up their repositories and contribute digital objects to social media platforms such as Flickr. In return they often receive user comments containing information that could be incorporated in their catalog records. Since judging the usefulness of a large number of user comments is a labor-intensive task, our aim is to provide automated support for filtering potentially useful social media comments on digital objects. In this paper, we discuss the notion of usefulness in the context of social media comments and compare it from end-users as well as expertusers perspectives. Then we present a machine-learning approach to automatically classify comments according to their usefulness. Our approach makes use of syntactic and semantic comment features and also considers user context. We present the results of an experiment we did on user comments received in six different Flickr Commons collections. They show that a few relatively straightforward features can be used to infer useful comments with up to 89% accuracy.