A Novel Tweet Recommendation Framework for Twitter

Abstract

In order to keep them updated users follow various Twitter accounts to get the latest information. As their social network increases it becomes challenging for them to find the relevant content from the massive collection of information. A Twitter user needs to scan a lot of less relevant posts to find the interesting tweets. Important updates may get lost if user is not able to read all the messages. So there is need that the most relevant updates are shown to the user first. Traditionally, the most retweeted tweets are considered popular and are brought forward. In order to improve the attractiveness of the incoming tweets we propose a personalized tweet ranking method based on the trending topics in the user network. A hashtag ranking model is developed to map the tweets into a ranked list of hashtags. The tweets corresponding to those hashtags are then ranked based on the linear weighted model that considers features related to tweet, author of tweet and the user. Finally, conducting a pilot user study we analyze the effectiveness of the proposed framework.