Open AccessA Novel Tweet Recommendation Framework for Twitter
Author(s) -
Kamaljit Kaur,
Kanwalvir Singh Dhindsa
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.j1150.0881019
Subject(s) - computer science , ranking (information retrieval) , information retrieval , social media , order (exchange) , world wide web , attractiveness , social network (sociolinguistics) , psychology , finance , psychoanalysis , economics
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.