Twitter is one of the primary online social networks where users share messages and contents of interest to those who follow their activities. To effectively categorize and give audience to their tweets, users try to append appropriate hashtags to their short messages. However, the hashtags usage is very small and very heterogeneous and users may spend a lot of time searching the appropriate hashtags. Thus, the need for a system to assist users in this task is very important to increase and homogenize the hashtagging usage. In this chapter, the authors present a hashtag recommendation system on microblogging platforms by leveraging semantic features. Furthermore, they conduct a detailed study on how the semantic-based model influences the final recommended hashtags using different ranking strategies. Moreover, they propose a linear and a machine learning based combination of these ranking strategies. The experiment results show that their approach improves content-based recommendations, achieving a recall of more than 47% on recommending 5 hashtags.
Authorship Authentication of Short Messages from Social Networks Machines
