Identification of Relevant Hashtags for Planned Events Using Learning to Rank

Abstract

Lots of planned events (e.g. concerts, sports matches, festivals, etc.) keep happening across the world every day. In various applications like event recommendation, event reporting, etc. it might be useful to find user discussions related to such events from social media. Identification of event related hashtags can be useful for this purpose. In this paper, we focus on identifying the top hashtags related to a given event. We define a set of features for (event, hashtag) pairs, and discuss ways to obtain these feature scores. A linear aggregation of these scores is used to finally output a ranked list of top hashtags for the event. The aggregation weights of the features are obtained using a learning to rank algorithm. We establish the superiority of our method by performing detailed experiments on a large dataset containing multiple categories of events and related tweets