research-article

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Analysis of Climate Campaigns on Social Media using Bayesian Model Averaging

Authors:

Tunazzina Islam,

Dan GoldwasserAuthors Info & Claims

AIES '23: Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society

Pages 15 - 25

https://doi.org/10.1145/3600211.3604665

Published: 29 August 2023 Publication History

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Abstract

Climate change is the defining issue of our time, and we are at a defining moment. Various interest groups, social movement organizations, and individuals engage in collective action on this issue on social media. In addition, issue advocacy campaigns on social media often arise in response to ongoing societal concerns, especially those faced by energy industries. Our goal in this paper is to analyze how those industries, their advocacy group, and climate advocacy group use social media to influence the narrative on climate change. In this work, we propose a minimally supervised model soup [57] approach combined with messaging themes to identify the stances of climate ads on Facebook. Finally, we release our stance dataset, model, and set of themes related to climate campaigns for future work on opinion mining and the automatic detection of climate change stances.

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Cited By

Upadhyaya ANejdl WFisichella MSerra ESpezzano F(2024)Harnessing Empathy and Ethics for Relevance Detection and Information Categorization in Climate and COVID-19 TweetsProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679937(4091-4095)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679937

Index Terms

Analysis of Climate Campaigns on Social Media using Bayesian Model Averaging
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
2. Information systems
  1. World Wide Web
    1. Online advertising
      1. Sponsored search advertising

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Information & Contributors

Information

Published In

cover image ACM Conferences

AIES '23: Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society

August 2023

1026 pages

ISBN:9798400702310

DOI:10.1145/3600211

Editors:
Francesca Rossi
IBM
,
Sanmay Das
George Mason University
,
Jenny Davis
Australian National University
,
Kay Firth-Butterfield
Centre for Trustworthy Technology
,
Alex John
London, Carnegie Mellon University

Copyright © 2023 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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SIGAI: ACM Special Interest Group on Artificial Intelligence

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 August 2023

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Refereed limited

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NSF CAREER award
Purdue Graduate School Summer Research Grant

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AIES '23

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SIGAI

AIES '23: AAAI/ACM Conference on AI, Ethics, and Society

August 8 - 10, 2023

QC, Montr\'{e}al, Canada

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Overall Acceptance Rate 61 of 162 submissions, 38%

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Cited By

Upadhyaya ANejdl WFisichella MSerra ESpezzano F(2024)Harnessing Empathy and Ethics for Relevance Detection and Information Categorization in Climate and COVID-19 TweetsProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679937(4091-4095)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679937

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