Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
7-2012
Abstract
Twitter is a popular online social information network service which allows people to read and post messages up to 140 characters, known as “tweets”. In this paper, we focus on the tweets between pairs of individuals, i.e., the tweet replies, and propose a generative model to discover topics among groups of twitter users. Our model has then been evaluated with a tweet dataset to show its effectiveness.
Discipline
Databases and Information Systems | Numerical Analysis and Scientific Computing | Social Media
Publication
Proceedings of the 10th Workshop on Mining and Learning with Graphs (MLG-2012), Edinburgh
Publisher
Katholieke Universiteit Leuven
City or Country
Leuven
Embargo Period
4-16-2016
Citation
DAI, Bingtian; LIM, Ee Peng; and PRASETYO, Philips Kokoh.
Topic discovery from tweet replies. (2012). Proceedings of the 10th Workshop on Mining and Learning with Graphs (MLG-2012), Edinburgh.
Available at: https://ink.library.smu.edu.sg/sis_research/3160
Copyright Owner and License
LARC
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://dtai.cs.kuleuven.be/events/mlg2012/papers/6_Topic_Dai.pdf
Included in
Databases and Information Systems Commons, Numerical Analysis and Scientific Computing Commons, Social Media Commons