skip to main content
10.1145/3572960.3572979acmotherconferencesArticle/Chapter ViewAbstractPublication PagesadcsConference Proceedingsconference-collections
research-article

Investigating Language Use by Polarised Groups on Twitter: A Case Study of the Bushfires

Published: 06 April 2023 Publication History

Abstract

Online social media platforms have become an important forum for public discourse, and have often been implicated in exacerbating polarisation in public sphere. Yet the precise mechanisms by which polarisation is driven are not fully understood. The study of linguistic style and features has been shown to be useful in exploring various aspects of online group discussions and, in turn, the processes which could contribute to polarisation. We present a case study around the hashtag #ArsonEmergency, collected from Australian Twittersphere during the unprecedented bushfires of 2019/2020. The dataset consists of two polarised groups and one unaffiliated group. We examine the linguistic style, moral language, and happiness profiles of 1786 users active during this catastrophic event. Our results suggest that polarised groups pushed ‘affective polarisation’ on Twitter while discussing the Australian Bushfires.

References

[1]
Michael D. Conover, Jacob Ratkiewicz, Matthew R. Francisco, Bruno Gonçalves, Filippo Menczer, and Alessandro Flammini. 2011. Political Polarization on Twitter. In ICWSM. The AAAI Press.
[2]
Dorottya Demszky, Nikhil Garg, Rob Voigt, James Zou, Jesse Shapiro, Matthew Gentzkow, and Dan Jurafsky. 2019. Analyzing Polarization in Social Media: Method and Application to Tweets on 21 Mass Shootings. In NAACL-HLT (1). Association for Computational Linguistics, 2970–3005.
[3]
Peter DeScioli and Robert Kurzban. 2013. A solution to the mysteries of morality.Psychological bulletin 139, 2 (2013), 477.
[4]
Peter Sheridan Dodds, Eric M Clark, Suma Desu, Morgan R Frank, Andrew J Reagan, Jake Ryland Williams, Lewis Mitchell, Kameron Decker Harris, Isabel M Kloumann, James P Bagrow, 2015. Human language reveals a universal positivity bias. Proceedings of the national academy of sciences 112, 8 (2015), 2389–2394.
[5]
Peter Sheridan Dodds, Kameron Decker Harris, Isabel M Kloumann, Catherine A Bliss, and Christopher M Danforth. 2011. Temporal patterns of happiness and information in a global social network: Hedonometrics and Twitter. PloS one 6, 12 (2011), e26752.
[6]
Jeremy A Frimer. 2020. Do liberals and conservatives use different moral languages? Two replications and six extensions of Graham, Haidt, and Nosek’s (2009) moral text analysis. Journal of Research in Personality 84 (2020), 103906.
[7]
Jeremy A Frimer, Caitlin E Tell, and Matt Motyl. 2017. Sacralizing liberals and fair-minded conservatives: Ideological symmetry in the moral motives in the culture war. Analyses of Social Issues and Public Policy 17, 1 (2017), 33–59.
[8]
Kiran Garimella, Gianmarco De Francisci Morales, Aristides Gionis, and Michael Mathioudakis. 2018. Political discourse on social media: Echo chambers, gatekeepers, and the price of bipartisanship. In Proceedings of the 2018 World Wide Web Conference. 913–922.
[9]
Kiran Garimella, Gianmarco De Francisci Morales, Aristides Gionis, and Michael Mathioudakis. 2018. Polarization on Social Media. In Tutorial of the The Web Conference 2018 (Lyon, France) (WWW ’18). https://www2018.thewebconf.org/program/tutorials-track/tutorial-202/
[10]
Matthew Gentzkow, Jesse M. Shapiro, and Matt Taddy. 2019. Measuring Group Differences in High-Dimensional Choices: Method and Application to Congressional Speech. Econometrica 87, 4 (2019), 1307–1340. https://doi.org/10.3982/ecta16566
[11]
Jesse Graham, Jonathan Haidt, Sena Koleva, Matt Motyl, Ravi Iyer, Sean P Wojcik, and Peter H Ditto. 2013. Moral foundations theory: The pragmatic validity of moral pluralism. In Advances in experimental social psychology. Vol. 47. Elsevier, 55–130.
[12]
Hugo L Hammer, Michael A Riegler, Lilja Øvrelid, and Erik Velldal. 2019. Threat: A large annotated corpus for detection of violent threats. In 2019 International Conference on Content-Based Multimedia Indexing (CBMI). IEEE, 1–5.
[13]
Frederic R Hopp, Jacob T Fisher, Devin Cornell, Richard Huskey, and René Weber. 2021. The extended Moral Foundations Dictionary (eMFD): Development and applications of a crowd-sourced approach to extracting moral intuitions from text. Behavior research methods 53, 1 (2021), 232–246.
[14]
Neta Kligler-Vilenchik, Christian Baden, and Moran Yarchi. 2020. Interpretative polarization across platforms: How political disagreement develops over time on Facebook, Twitter, and WhatsApp. Social Media+ Society 6, 3 (2020), 2056305120944393.
[15]
Haewoon Kwak, Jisun An, Elise Jing, and Yong-Yeol Ahn. 2021. FrameAxis: characterizing microframe bias and intensity with word embedding. PeerJ Computer Science 7(2021), e644.
[16]
Haewoon Kwak, Changhyun Lee, Hosung Park, and Sue Moon. 2010. What is Twitter, a social network or a news media?. In Proceedings of the 19th international conference on World wide web. 591–600.
[17]
Ping Li, Benjamin Schloss, and D. Jake Follmer. 2017. Speaking two “Languages” in America: A semantic space analysis of how presidential candidates and their supporters represent abstract political concepts differently. Behavior Research Methods 49, 5 (jul 2017), 1668–1685. https://doi.org/10.3758/s13428-017-0931-5
[18]
Olutobi Owoputi, Brendan O’Connor, Chris Dyer, Kevin Gimpel, Nathan Schneider, and Noah A Smith. 2013. Improved part-of-speech tagging for online conversational text with word clusters. In Proceedings of the 2013 conference of the North American chapter of the association for computational linguistics: human language technologies. 380–390.
[19]
J Hunter Priniski, Negar Mokhberian, Bahareh Harandizadeh, Fred Morstatter, Kristina Lerman, Hongjing Lu, and P Jeffrey Brantingham. 2021. Mapping moral valence of tweets following the killing of George Floyd. arXiv preprint arXiv:2104.09578(2021).
[20]
Karolina Sylwester and Matthew Purver. 2015. Twitter Language Use Reflects Psychological Differences between Democrats and Republicans. PLOS ONE 10, 9 (sep 2015), e0137422. https://doi.org/10.1371/journal.pone.0137422
[21]
Yla R Tausczik and James W Pennebaker. 2010. The psychological meaning of words: LIWC and computerized text analysis methods. Journal of language and social psychology 29, 1 (2010), 24–54.
[22]
Sze-Yuh Nina Wang and Yoel Inbar. 2020. Moral-Language Use by US Political Elites. Psychological Science(2020), 0956797620960397. https://doi.org/10.1177/0956797620960397
[23]
Derek Weber, Mehwish Nasim, Lucia Falzon, and Lewis Mitchell. 2020. # ArsonEmergency and Australia’s “Black Summer”: Polarisation and Misinformation on Social Media. In Multidisciplinary International Symposium on Disinformation in Open Online Media. Springer, 159–173.
[24]
Derek Weber, Mehwish Nasim, Lucia Falzon, and Lewis Mitchell. 2020. #ArsonEmergency and Australia’s “Black Summer”: A study of polarisation and its broader effect on the online discussion. Talk presented at the fifth Australian Social Network Analysis Conference, ASNAC’20, 25–27 November, Adelaide, Australia.
[25]
Aksel Wester, Lilja Øvrelid, Erik Velldal, and Hugo Lewi Hammer. 2016. Threat detection in online discussions. In Proceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis. 66–71.
[26]
S.C. Woolley and D.R. Guilbeault. 2018. United States: Manufacturing Consensus Online. In Computational Propaganda: Political Parties, Politicians, and Political Manipulation on Social Media, P.N. Howard and S.C. Woolley (Eds.). Oxford University Press, Chapter 8, 185–211. https://doi.org/10.1093/oso/9780190931407.001.0001

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences
ADCS '22: Proceedings of the 26th Australasian Document Computing Symposium
December 2022
48 pages
ISBN:9798400700217
DOI:10.1145/3572960
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]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 April 2023

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. Bushfires
  2. Moral language
  3. Polarisation
  4. Twitter

Qualifiers

  • Research-article
  • Research
  • Refereed limited

Funding Sources

  • Defence Innovation Partnership
  • ARC Centre of Excellence for mathematical and statistical frontiers, Australia

Conference

ADCS '22
ADCS '22: Australasian Document Computing Symposium
December 15 - 16, 2022
SA, Adelaide, Australia

Acceptance Rates

Overall Acceptance Rate 30 of 57 submissions, 53%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 82
    Total Downloads
  • Downloads (Last 12 months)44
  • Downloads (Last 6 weeks)4
Reflects downloads up to 14 Feb 2025

Other Metrics

Citations

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

HTML Format

View this article in HTML Format.

HTML Format

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media