A Unified Model for Topics, Events and Users on Twitter
With the rapid growth of social media, Twitter has become one of the most widely adopted platforms for people to post short and instant message. On the one hand, people tweets about their daily lives, and on the other hand, when major events happen, people also follow and tweet about them. Moreover,...
Saved in:
Main Authors: | , |
---|---|
Format: | text |
Language: | English |
Published: |
Institutional Knowledge at Singapore Management University
2013
|
Subjects: | |
Online Access: | https://ink.library.smu.edu.sg/sis_research/2058 https://ink.library.smu.edu.sg/context/sis_research/article/3057/viewcontent/emnlp_2013.pdf |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Singapore Management University |
Language: | English |
id |
sg-smu-ink.sis_research-3057 |
---|---|
record_format |
dspace |
spelling |
sg-smu-ink.sis_research-30572016-04-17T00:32:22Z A Unified Model for Topics, Events and Users on Twitter DIAO, Qiming JIANG, Jing With the rapid growth of social media, Twitter has become one of the most widely adopted platforms for people to post short and instant message. On the one hand, people tweets about their daily lives, and on the other hand, when major events happen, people also follow and tweet about them. Moreover, people’s posting behaviors on events are often closely tied to their personal interests. In this paper, we try to model topics, events and users on Twitter in a unified way. We propose a model which combines an LDA-like topic model and the Recurrent Chinese Restaurant Process to capture topics and events. We further propose a duration-based regularization component to find bursty events. We also propose to use event-topic affinity vectors to model the association between events and topics. Our experiments shows that our model can accurately identify meaningful events and the event-topic affinity vectors are effective for event recommendation and grouping events by topics. 2013-10-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/2058 https://ink.library.smu.edu.sg/context/sis_research/article/3057/viewcontent/emnlp_2013.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Twitter social media tweets Twitter posts model topic capture bursty events event identification Databases and Information Systems Numerical Analysis and Scientific Computing Social Media |
institution |
Singapore Management University |
building |
SMU Libraries |
continent |
Asia |
country |
Singapore Singapore |
content_provider |
SMU Libraries |
collection |
InK@SMU |
language |
English |
topic |
Twitter social media tweets Twitter posts model topic capture bursty events event identification Databases and Information Systems Numerical Analysis and Scientific Computing Social Media |
spellingShingle |
Twitter social media tweets Twitter posts model topic capture bursty events event identification Databases and Information Systems Numerical Analysis and Scientific Computing Social Media DIAO, Qiming JIANG, Jing A Unified Model for Topics, Events and Users on Twitter |
description |
With the rapid growth of social media, Twitter has become one of the most widely adopted platforms for people to post short and instant message. On the one hand, people tweets about their daily lives, and on the other hand, when major events happen, people also follow and tweet about them. Moreover, people’s posting behaviors on events are often closely tied to their personal interests. In this paper, we try to model topics, events and users on Twitter in a unified way. We propose a model which combines an LDA-like topic model and the Recurrent Chinese Restaurant Process to capture topics and events. We further propose a duration-based regularization component to find bursty events. We also propose to use event-topic affinity vectors to model the association between events and topics. Our experiments shows that our model can accurately identify meaningful events and the event-topic affinity vectors are effective for event recommendation and grouping events by topics. |
format |
text |
author |
DIAO, Qiming JIANG, Jing |
author_facet |
DIAO, Qiming JIANG, Jing |
author_sort |
DIAO, Qiming |
title |
A Unified Model for Topics, Events and Users on Twitter |
title_short |
A Unified Model for Topics, Events and Users on Twitter |
title_full |
A Unified Model for Topics, Events and Users on Twitter |
title_fullStr |
A Unified Model for Topics, Events and Users on Twitter |
title_full_unstemmed |
A Unified Model for Topics, Events and Users on Twitter |
title_sort |
unified model for topics, events and users on twitter |
publisher |
Institutional Knowledge at Singapore Management University |
publishDate |
2013 |
url |
https://ink.library.smu.edu.sg/sis_research/2058 https://ink.library.smu.edu.sg/context/sis_research/article/3057/viewcontent/emnlp_2013.pdf |
_version_ |
1770571782456082432 |