Mining tweets for tag recommendation on social media

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.