To search or to label?: predicting the performance of search-based automatic image classifiers
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Flickr tag recommendation based on collective knowledge
Proceedings of the 17th international conference on World Wide Web
Real-time automatic tag recommendation
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Personalized, interactive tag recommendation for flickr
Proceedings of the 2008 ACM conference on Recommender systems
Classifying tags using open content resources
Proceedings of the Second ACM International Conference on Web Search and Data Mining
Proceedings of the 18th international conference on World wide web
Cross-tagging for personalized open social networking
Proceedings of the 20th ACM conference on Hypertext and hypermedia
A two-view learning approach for image tag ranking
Proceedings of the fourth ACM international conference on Web search and data mining
Video Annotation Through Search and Graph Reinforcement Mining
IEEE Transactions on Multimedia
On the Annotation of Web Videos by Efficient Near-Duplicate Search
IEEE Transactions on Multimedia
ICWE'12 Proceedings of the 12th international conference on Web Engineering
Automated Twitter data collecting tool for data mining in social network
Proceedings of the 2012 ACM Research in Applied Computation Symposium
Automated Twitter data collecting tool and case study with rule-based analysis
Proceedings of the 14th International Conference on Information Integration and Web-based Applications & Services
On recommending hashtags in twitter networks
SocInfo'12 Proceedings of the 4th international conference on Social Informatics
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Automatic tag recommendation or annotation can help in improving the efficiency of text-based information retrieval on online social media services like Blogger, Last.FM, Flickr and YouTube. In this work, we investigate alternate solutions for tag recommendations by employing a Wisdom of Crowd approach in a mashup framework. In particular, we mine tweets on Twitter and use their hashtag(s) and content to annotate videos on Flickr, Photobucket, YouTube, Dailymotion and SoundCloud. We crawl Twitter to collect a random sample of tweets containing Flickr, Photo- bucket, YouTube, Dailymotion and SoundCloud URLs. We then recommend tags for these services using hashtag(s) and content present in tweets. We use a hybrid technique (automated and manual) to validate our results on different subsets (presence / absence of hashtags, presence / absence of media tags) of data. Experimental results demonstrate that the proposed solution approach is effective and reliable.