research-article

Characterizing debate performance via aggregated twitter sentiment

Authors:

Nicholas A.Diakopoulos,

David A.ShammaAuthors Info & Claims

CHI '10: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

Pages1195-1198

https://doi.org/10.1145/1753326.1753504

Published:10 April 2010Publication History

Abstract

Television broadcasters are beginning to combine social micro-blogging systems such as Twitter with television to create social video experiences around events. We looked at one such event, the first U.S. presidential debate in 2008, in conjunction with aggregated ratings of message sentiment from Twitter. We begin to develop an analytical methodology and visual representations that could help a journalist or public affairs person better understand the temporal dynamics of sentiment in reaction to the debate video. We demonstrate visuals and metrics that can be used to detect sentiment pulse, anomalies in that pulse, and indications of controversial topics that can be used to inform the design of visual analytic systems for social media events.

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Cited By

Yilmaz KAbul OHussain S(2024)From tweets to tensions: exploring the roots of political polarization in Turkish constitutional referendumJournal of Information Technology & Politics10.1080/19331681.2024.2385429(1-17)Online publication date: 13-Aug-2024
https://doi.org/10.1080/19331681.2024.2385429
Auconi AFederico LRiotta GCaldarelli G(2024)Fluctuations and extreme events in the public attention on Italian legislative electionsScientific Reports10.1038/s41598-024-69354-y14:1Online publication date: 1-Oct-2024
https://doi.org/10.1038/s41598-024-69354-y
Moura RCarvalho JPlastino APaes A(2024)Less is moreInformation Processing and Management: an International Journal10.1016/j.ipm.2024.10368861:4Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1016/j.ipm.2024.103688
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Index Terms

Characterizing debate performance via aggregated twitter sentiment
1. Human-centered computing

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Published In

cover image ACM Conferences

CHI '10: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

April 2010

2690 pages

ISBN:9781605589299

DOI:10.1145/1753326

General Chair:
Elizabeth Mynatt
Georgia Institute of Technology
,
Program Chairs:
Geraldine Fitzpatrick
Vienna University of Technology
,
Scott Hudson
Carnegie Mellon University
,
Keith Edwards
Georgia Tech
,
Tom Rodden
University of Nottingham

Copyright © 2010 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from[email protected]

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Published:10 April 2010

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CHI '10: CHI Conference on Human Factors in Computing Systems

April 10 - 15, 2010

Georgia, Atlanta, USA

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Cited By

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https://doi.org/10.1080/19331681.2024.2385429
Auconi AFederico LRiotta GCaldarelli G(2024)Fluctuations and extreme events in the public attention on Italian legislative electionsScientific Reports10.1038/s41598-024-69354-y14:1Online publication date: 1-Oct-2024
https://doi.org/10.1038/s41598-024-69354-y
Moura RCarvalho JPlastino APaes A(2024)Less is moreInformation Processing and Management: an International Journal10.1016/j.ipm.2024.10368861:4Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1016/j.ipm.2024.103688
Konat BGajewska ERossa W(2024)Pathos in Natural Language Argumentation: Emotional Appeals and ReactionsArgumentation10.1007/s10503-024-09631-238:3(369-403)Online publication date: 21-Jun-2024
https://doi.org/10.1007/s10503-024-09631-2
Shevtsov AOikonomidou MAntonakaki DPratikakis PIoannidis S(2023)What Tweets and YouTube comments have in common? Sentiment and graph analysis on data related to US elections 2020PLOS ONE10.1371/journal.pone.027054218:1(e0270542)Online publication date: 31-Jan-2023
https://doi.org/10.1371/journal.pone.0270542
Keerthan Kumar TDHAKATE HKoolagudi S(2023)IIMH: Intention Identification In Multimodal Human UtterancesProceedings of the 2023 Fifteenth International Conference on Contemporary Computing10.1145/3607947.3608016(337-344)Online publication date: 3-Aug-2023
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Kukkar AMohana RSharma ANayyar AShah M(2023)Improving Sentiment Analysis in Social Media by Handling Lengthened WordsIEEE Access10.1109/ACCESS.2023.323836611(9775-9788)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3238366
Kwon HKoylu CDietrich B(2023)Natural language processing meets spatial time series analysis and geovisualization: identifying and visualizing spatio-topical sentiment trends on TwitterCartography and Geographic Information Science10.1080/15230406.2023.226475150:6(593-607)Online publication date: 26-Oct-2023
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Attié EBouvet AGuibert J(2022)The Stakes of Social MediaData Mining Approaches for Big Data and Sentiment Analysis in Social Media10.4018/978-1-7998-8413-2.ch009(196-222)Online publication date: 2022
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Mohd MJaveed SNowsheenaWani MKhanday H(2022)Sentiment analysis using lexico-semantic featuresJournal of Information Science10.1177/0165551522112401650:6(1449-1470)Online publication date: 31-Oct-2022
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