Controversial topic discovery on members of congress with Twitter

Aleksey Panasyuk; Edmund Szu Li Yu; Kishan G. Mehrotra

doi:10.1016/j.procs.2014.09.073

Controversial topic discovery on members of congress with Twitter

Aleksey Panasyuk, Edmund Szu Li Yu, Kishan G. Mehrotra

Department of Electrical Engineering & Computer Science

Research output: Chapter in Book/Entry/Poem › Conference contribution

6 Scopus citations

Abstract

This paper addresses how Twitter can be used for identifying conflict between communities of users. We aggregate documents by topic and by community and perform sentiment analysis, which allows us to analyze the overall opinion of each community about each topic. We rank the topics with opposing views (negative for one community and positive for the other). For illustration of the proposed methodology we chose a problem whose results can be evaluated using news articles. We look at tweets for republican and democrat congress members for the 112^th House of Representatives from September to December 2013 and demonstrate that our approach is successful by comparing against articles in the news media.

Original language	English (US)
Title of host publication	Procedia Computer Science
Publisher	Elsevier
Pages	160-167
Number of pages	8
Volume	36
Edition	C
DOIs	https://doi.org/10.1016/j.procs.2014.09.073
State	Published - 2014
Event	Complex Adaptive Systems, 2014 - Philadelphia, United States Duration: Nov 3 2014 → Nov 5 2014

Other

Other	Complex Adaptive Systems, 2014
Country/Territory	United States
City	Philadelphia
Period	11/3/14 → 11/5/14

Keywords

Latent dirichlet allocation
Polarizing topics
Semantic extraction
Social media mining
Topic modeling
Twitter

ASJC Scopus subject areas

General Computer Science

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10.1016/j.procs.2014.09.073

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@inproceedings{464f2dcf15864da4b5aa9d4427ecbc68,

title = "Controversial topic discovery on members of congress with Twitter",

abstract = "This paper addresses how Twitter can be used for identifying conflict between communities of users. We aggregate documents by topic and by community and perform sentiment analysis, which allows us to analyze the overall opinion of each community about each topic. We rank the topics with opposing views (negative for one community and positive for the other). For illustration of the proposed methodology we chose a problem whose results can be evaluated using news articles. We look at tweets for republican and democrat congress members for the 112th House of Representatives from September to December 2013 and demonstrate that our approach is successful by comparing against articles in the news media.",

keywords = "Latent dirichlet allocation, Polarizing topics, Semantic extraction, Social media mining, Topic modeling, Twitter",

author = "Aleksey Panasyuk and Yu, {Edmund Szu Li} and Mehrotra, {Kishan G.}",

year = "2014",

doi = "10.1016/j.procs.2014.09.073",

language = "English (US)",

volume = "36",

pages = "160--167",

booktitle = "Procedia Computer Science",

publisher = "Elsevier",

address = "Netherlands",

edition = "C",

note = "Complex Adaptive Systems, 2014 ; Conference date: 03-11-2014 Through 05-11-2014",

}

TY - GEN

T1 - Controversial topic discovery on members of congress with Twitter

AU - Panasyuk, Aleksey

AU - Yu, Edmund Szu Li

AU - Mehrotra, Kishan G.

PY - 2014

Y1 - 2014

N2 - This paper addresses how Twitter can be used for identifying conflict between communities of users. We aggregate documents by topic and by community and perform sentiment analysis, which allows us to analyze the overall opinion of each community about each topic. We rank the topics with opposing views (negative for one community and positive for the other). For illustration of the proposed methodology we chose a problem whose results can be evaluated using news articles. We look at tweets for republican and democrat congress members for the 112th House of Representatives from September to December 2013 and demonstrate that our approach is successful by comparing against articles in the news media.

AB - This paper addresses how Twitter can be used for identifying conflict between communities of users. We aggregate documents by topic and by community and perform sentiment analysis, which allows us to analyze the overall opinion of each community about each topic. We rank the topics with opposing views (negative for one community and positive for the other). For illustration of the proposed methodology we chose a problem whose results can be evaluated using news articles. We look at tweets for republican and democrat congress members for the 112th House of Representatives from September to December 2013 and demonstrate that our approach is successful by comparing against articles in the news media.

KW - Latent dirichlet allocation

KW - Polarizing topics

KW - Semantic extraction

KW - Social media mining

KW - Topic modeling

KW - Twitter

UR - http://www.scopus.com/inward/record.url?scp=84937764459&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84937764459&partnerID=8YFLogxK

U2 - 10.1016/j.procs.2014.09.073

DO - 10.1016/j.procs.2014.09.073

M3 - Conference contribution

AN - SCOPUS:84937764459

VL - 36

SP - 160

EP - 167

BT - Procedia Computer Science

PB - Elsevier

T2 - Complex Adaptive Systems, 2014

Y2 - 3 November 2014 through 5 November 2014

ER -

Controversial topic discovery on members of congress with Twitter

Abstract

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Keywords

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