Survey of Trending Techniques for Detection of Emerging Topics in Computer Science within Social Media

With the advent of Internet there has been a significant and exponential growth in information available to users. The availability of resources like smart mobile phone, low cost data plans and improvement in mobile communication infrastructure has further increased the reach and availability of information. The Internet allowed creation of websites and applications that significantly kept on adding data. The data generated through these websites can be structured (relational database), unstructured (digital images, video, audio files) or semi-structured (word document). The growth of Internet and WWW services gave user a liberty to create his own data, which then can be shared with the world. The development of User-Generated Content (UGC) [2] such as blogs, wikis, forums, tweets, discussions, posts, chats, podcasts, advertisements and other form of media led to the shift of information exchange from media conglomerates to individual user. With this huge amount of data, we address the problem of trending the emerging topics. Identify trending of these emerging topics allows us to know the probable trend of computer science research topics or other relevant research topics in future

Exploring the Geography of Tags in Youtube Views

Although tags play a critical role in many social media,their link to the geographic distribution of user generatedvideos has been little investigated. In this paper, we ana-lyze the correlation between the geographic distribution ofa video’s views and the tags attached to this video in aYoutube dataset. We show that tags can be interpreted asmarkers of a video’s geographic diffusion, with some tagsstrongly linked to well identified geographic areas. Basedon our findings, we explore whether the distribution of avideo’s views can be predicted from its tags. We demon-strate how this predictive power could help improve on-linevideo services by preferentially storing videos close to wherethey are likely to be viewed. Our results show that even witha simplistic approach we are able to predict a minimum of65.9% of a video’s views for a majority of videos, and thata tag-based placement strategy can improve the hit rate ofa distributed on-line video service by up to 6.8% globally,with an improvement of up to 34% in the USA

A Vocabulary for Growth: Topic Modeling of Content Popularity Evolution

In this paper, we present a novel method to predict the long-term popularity of user-generated content (UGC). At first, the method clusters the dynamics of UGC popularity into a vocabulary of growth in popularity (sequence) by using a mixture model. Eventually, the method assigns to each sequence a topic model to describe the dynamics of the sequence in a compact way. We then use this topic model to identify similar patterns of growth in popularity of newly observed UGC. The proposed method has two key features: First, it considers the historical dynamics of the UGC popularity, and second it provides long-term popularity prediction. Results on the real dataset of UGC show that the proposed method is flexible, and able to accurately forecast the complete growth in popularity of a given UGC