On analyzing user topic-specific platform preferences across multiple social media sites

Topic modeling has traditionally been studied for single text collections and applied to social media data represented in the form of text documents. With the emergence of many social media platforms, users find themselves using different social media for posting content and for social interaction....

Full description

Saved in:
Bibliographic Details
Main Authors: LEE, Roy Ka Wei, HOANG, Tuan Anh, LIM, Ee Peng
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2017
Subjects:
Online Access:https://ink.library.smu.edu.sg/sis_research/3651
https://ink.library.smu.edu.sg/context/sis_research/article/4653/viewcontent/12._Apr01_2017___On_Analyzing_User_Topic_Specific_Platform_Preferences_Across_Multiple_Social_Media_Sites__WWW2017_.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-4653
record_format dspace
spelling sg-smu-ink.sis_research-46532019-06-25T13:48:44Z On analyzing user topic-specific platform preferences across multiple social media sites LEE, Roy Ka Wei HOANG, Tuan Anh LIM, Ee Peng Topic modeling has traditionally been studied for single text collections and applied to social media data represented in the form of text documents. With the emergence of many social media platforms, users find themselves using different social media for posting content and for social interaction. While many topics may be shared across social media platforms, users typically show preferences of certain social media platform(s) over others for certain topics. Such platform preferences may even be found at the individual level. To model social media topics as well as platform preferences of users, we propose a new topic model known as MultiPlatform-LDA (MultiLDA). Instead of just merging all posts from different social media platforms into a single text collection, MultiLDA keeps one text collection for each social media platform but allowing these platforms to share a common set of topics. MultiLDA further learns the user-specific platform preferences for each topic. We evaluate MultiLDA against TwitterLDA, the state-of-the-art method for social media content modeling, on two aspects: (i) the effectiveness in modeling topics across social media platforms, and (ii) the ability to predict platform choices for each post. We conduct experiments on three real-world datasets from Twitter, Instagram and Tumblr sharing a set of common users. Our experiments results show that the MultiLDA outperforms in both topic modeling and platform choice prediction tasks. We also show empirically that among the three social media platforms, "Daily matters" and "Relationship matters" are dominant topics in Twitter, "Social gathering", "Outing" and "Fashion" are dominant topics in Instagram, and "Music", "Entertainment" and "Fashion" are dominant topics in Tumblr. 2017-04-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/3651 info:doi/10.1145/3038912.3052614 https://ink.library.smu.edu.sg/context/sis_research/article/4653/viewcontent/12._Apr01_2017___On_Analyzing_User_Topic_Specific_Platform_Preferences_Across_Multiple_Social_Media_Sites__WWW2017_.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 User preference Multiple social networks Topic modeling Databases and Information Systems Social Media Theory and Algorithms
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic User preference
Multiple social networks
Topic modeling
Databases and Information Systems
Social Media
Theory and Algorithms
spellingShingle User preference
Multiple social networks
Topic modeling
Databases and Information Systems
Social Media
Theory and Algorithms
LEE, Roy Ka Wei
HOANG, Tuan Anh
LIM, Ee Peng
On analyzing user topic-specific platform preferences across multiple social media sites
description Topic modeling has traditionally been studied for single text collections and applied to social media data represented in the form of text documents. With the emergence of many social media platforms, users find themselves using different social media for posting content and for social interaction. While many topics may be shared across social media platforms, users typically show preferences of certain social media platform(s) over others for certain topics. Such platform preferences may even be found at the individual level. To model social media topics as well as platform preferences of users, we propose a new topic model known as MultiPlatform-LDA (MultiLDA). Instead of just merging all posts from different social media platforms into a single text collection, MultiLDA keeps one text collection for each social media platform but allowing these platforms to share a common set of topics. MultiLDA further learns the user-specific platform preferences for each topic. We evaluate MultiLDA against TwitterLDA, the state-of-the-art method for social media content modeling, on two aspects: (i) the effectiveness in modeling topics across social media platforms, and (ii) the ability to predict platform choices for each post. We conduct experiments on three real-world datasets from Twitter, Instagram and Tumblr sharing a set of common users. Our experiments results show that the MultiLDA outperforms in both topic modeling and platform choice prediction tasks. We also show empirically that among the three social media platforms, "Daily matters" and "Relationship matters" are dominant topics in Twitter, "Social gathering", "Outing" and "Fashion" are dominant topics in Instagram, and "Music", "Entertainment" and "Fashion" are dominant topics in Tumblr.
format text
author LEE, Roy Ka Wei
HOANG, Tuan Anh
LIM, Ee Peng
author_facet LEE, Roy Ka Wei
HOANG, Tuan Anh
LIM, Ee Peng
author_sort LEE, Roy Ka Wei
title On analyzing user topic-specific platform preferences across multiple social media sites
title_short On analyzing user topic-specific platform preferences across multiple social media sites
title_full On analyzing user topic-specific platform preferences across multiple social media sites
title_fullStr On analyzing user topic-specific platform preferences across multiple social media sites
title_full_unstemmed On analyzing user topic-specific platform preferences across multiple social media sites
title_sort on analyzing user topic-specific platform preferences across multiple social media sites
publisher Institutional Knowledge at Singapore Management University
publishDate 2017
url https://ink.library.smu.edu.sg/sis_research/3651
https://ink.library.smu.edu.sg/context/sis_research/article/4653/viewcontent/12._Apr01_2017___On_Analyzing_User_Topic_Specific_Platform_Preferences_Across_Multiple_Social_Media_Sites__WWW2017_.pdf
_version_ 1770573402805895168