posted on 2023-05-23, 09:52authored byHan, SC, Hyunsuk Chung, Kim, DH, Lee, S, Byeong KangByeong Kang
Twitter is one of the most popular social media services that allow users to share and spread information. Twitter monitors their users’ postings and detects the most discussed topics of the moment. Then, they publish these topics on the list, called ‘Trending Topics’. Trending Topics on Twitter shows the list of top 10 trending topics but each topic consists of short phrase or keyword, which does not contain any explanation of those meanings. It is almost impossible to identify what a trending topic is about unless you read all related tweets. The goal of this paper is finding the most successful method that uses to retrieve the representative contents of trending topics in order to disambiguate the meaning of topics. We first collected the trending topics and tweets related to them. Then, we applied four types of information retrieval approaches (key factor extraction, named entity recognition, topic modelling, and automatic summarization) for extracting the representative contents of trending topics. We conducted human experiments with 20 postgraduate students.
History
Publication title
Lecture Notes in Artificial Intelligence 8863: Proceedings of the 13th Pacific Rim Knowledge Acquisition Workshop (PKAW2014)
Volume
8863
Editors
YS Kim, BH Kang, D Richards
Pagination
126-137
ISSN
0302-9743
Department/School
School of Information and Communication Technology
Publisher
Springer International Publishing
Place of publication
Switzerland
Event title
13th Pacific Rim Knowledge Acquisition Workshop (PKAW 2014)
Event Venue
Gold Coast, Australia
Date of Event (Start Date)
2014-12-01
Date of Event (End Date)
2014-12-02
Rights statement
Copyright 2014 Springer International Publishing Switzerland