research-article

LibertyMFD: A Lexicon to Assess the Moral Foundation of Liberty.

Authors:

Kyriaki KalimeriAuthors Info & Claims

GoodIT '22: Proceedings of the 2022 ACM Conference on Information Technology for Social Good

Pages 154 - 160

https://doi.org/10.1145/3524458.3547264

Published: 07 September 2022 Publication History

Abstract

Quantifying the moral narratives expressed in the user-generated text, news, or public discourses is fundamental for understanding individuals’ concerns and viewpoints and preventing violent protests and social polarisation. The Moral Foundation Theory (MFT) was developed to operationalise morality in a five-dimensional scale system. Recent developments of the theory urged for the introduction of a new foundation, the Liberty Foundation. Being only recently added to the theory, there are no available linguistic resources to assess whether liberty is present in text corpora. Given its importance to current social issues such as the vaccination debate, we propose two data-driven approaches, deriving two candidate lexicons generated based on aligned documents from online news sources with different worldviews. After extensive experimentation, we contribute to the research community a novel lexicon that assesses the liberty moral foundation in the way individuals with contrasting viewpoints express themselves through written text. The LibertyMFD dictionary can be a valuable tool for policymakers to understand diverse viewpoints on controversial social issues such as vaccination, abortion, or even uprisings, as they happen and on a large scale.

References

[1]

Oscar Araque, Lorenzo Gatti, and Kyriaki Kalimeri. 2020. MoralStrength: Exploiting a moral lexicon and embedding similarity for moral foundations prediction. Knowledge-based systems 191 (2020).

[2]

Oscar Araque, Lorenzo Gatti, and Kyriaki Kalimeri. 2021. The Language of Liberty: A Preliminary Study. In Companion Proceedings of the Web Conference 2021(Ljubljana, Slovenia) (WWW ’21). Association for Computing Machinery, New York, NY, USA, 623?626. https://doi.org/10.1145/3442442.3452351

Digital Library

[3]

O. Araque, L. Gatti, J. Staiano, and M. Guerini. 2019. DepecheMood++: a Bilingual Emotion Lexicon Built Through Simple Yet Powerful Techniques. IEEE Transactions on Affective Computing(2019), 1–1. https://doi.org/10.1109/TAFFC.2019.2934444

[4]

Oscar Araque and Carlos A. Iglesias. 2020. An Approach for Radicalization Detection Based on Emotion Signals and Semantic Similarity. IEEE Access 8(2020), 17877–17891. https://doi.org/10.1109/ACCESS.2020.2967219

[5]

Stefano Baccianella, Andrea Esuli, and Fabrizio Sebastiani. 2010. SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining. In Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC’10). European Language Resources Association (ELRA), Valletta, Malta. http://www.lrec-conf.org/proceedings/lrec2010/pdf/769_Paper.pdf

[6]

Randy E Barnett. 2014. The structure of liberty: Justice and the rule of law. OUP Oxford.

[7]

Lorenzo Betti, Gianmarco De Francisci Morales, Laetitia Gauvin, Kyriaki Kalimeri, Yelena Mejova, Daniela Paolotti, and Michele Starnini. 2021. Detecting adherence to the recommended childhood vaccination schedule from user-generated content in a US parenting forum. PLoS computational biology 17, 4 (2021), e1008919.

[8]

Jason Brennan. 2012. Libertarianism: What everyone needs to know®. Oxford University Press.

[9]

Scott Clifford, Vijeth Iyengar, Roberto Cabeza, and Walter Sinnott-Armstrong. 2015. Moral foundations vignettes: A standardized stimulus database of scenarios based on moral foundations theory. Behavior research methods 47, 4 (2015), 1178–1198.

[10]

Rong-En Fan, Kai-Wei Chang, Cho-Jui Hsieh, Xiang-Rui Wang, and Chih-Jen Lin. 2008. LIBLINEAR: A library for large linear classification. the Journal of machine Learning research 9 (2008), 1871–1874.

[11]

Ryan J Gallagher, Morgan R Frank, Lewis Mitchell, Aaron J Schwartz, Andrew J Reagan, Christopher M Danforth, and Peter Sheridan Dodds. 2021. Generalized word shift graphs: a method for visualizing and explaining pairwise comparisons between texts. EPJ Data Science 10, 1 (2021), 4. https://doi.org/10.1140/epjds/s13688-021-00260-3

[12]

Justin Garten, Joe Hoover, Kate M Johnson, Reihane Boghrati, Carol Iskiwitch, and Morteza Dehghani. 2018. Dictionaries and distributions: Combining expert knowledge and large scale textual data content analysis. Behavior Research Methods 50, 1 (2018), 344–361.

[13]

Justin Garten, Brendan Kennedy, Joe Hoover, Kenji Sagae, and Morteza Dehghani. 2019. Incorporating Demographic Embeddings Into Language Understanding. Cognitive science 43, 1 (2019).

[14]

Jesse Graham, Jonathan Haidt, and Brian A Nosek. 2009. Liberals and conservatives rely on different sets of moral foundations.Journal of personality and social psychology 96, 5(2009), 1029. https://doi.org/10.1037/a0015141

[15]

Jonathan Haidt. 2012. The righteous mind: Why good people are divided by politics and religion. Vintage.

[16]

Jonathan Haidt and Craig Joseph. 2004. Intuitive ethics: How innately prepared intuitions generate culturally variable virtues. Daedalus 133, 4 (2004), 55–66.

[17]

Joe Hoover, Gwenyth Portillo-Wightman, Leigh Yeh, Shreya Havaldar, Aida Mostafazadeh Davani, Ying Lin, Brendan Kennedy, Mohammad Atari, Zahra Kamel, Madelyn Mendlen, 2020. Moral Foundations Twitter Corpus: A collection of 35k tweets annotated for moral sentiment. Social Psychological and Personality Science 11, 8 (2020), 1057–1071.

[18]

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.

[19]

Ravi Iyer, Spassena Koleva, Jesse Graham, Peter Ditto, and Jonathan Haidt. 2012. Understanding libertarian morality: The psychological dispositions of self-identified libertarians. PloS one 7, 8 (2012), e42366.

[20]

Kyriaki Kalimeri, Mariano G. Beiró, Alessandra Urbinati, Andrea Bonanomi, Alessandro Rosina, and Ciro Cattuto. 2019. Human values and attitudes towards vaccination in social media. In Companion Proceedings of The 2019 World Wide Web Conference. 248–254.

Digital Library

[21]

Quoc Le and Tomas Mikolov. 2014. Distributed representations of sentences and documents. In International Conference on Machine Learning. 1188–1196.

Digital Library

[22]

Alessandro Lenci. 2018. Distributional models of word meaning. Annual review of Linguistics 4 (2018), 151–171.

[23]

AllSides LLC. 2022. AllSides Media Bias Ratings?https://www.allsides.com/media-bias/media-bias-ratings

[24]

Yelena Mejova and Kyriaki Kalimeri. 2020. COVID-19 on Facebook ads: competing agendas around a public health crisis. In Proceedings of the 3rd ACM SIGCAS Conference on Computing and Sustainable Societies. 22–31.

Digital Library

[25]

Saif M. Mohammad and Svetlana Kiritchenko. 2015. Using Hashtags to Capture Fine Emotion Categories from Tweets. Computational Intelligence 31, 2 (2015), 301–326. https://doi.org/10.1111/coin.12024

Digital Library

[26]

Artur Nilsson, Arvid Erlandsson, and Daniel Västfjäll. 2020. Moral foundations theory and the psychology of charitable giving. European Journal of Personality 34, 3 (2020), 431–447.

[27]

Maria Florencia Prado, Victoria Perez Bustos, Kyriaki Kalimeri, and Mariano G. Beiro. 2022. Narratives Around the Vaccination Discourse on the Facebook Platform. arXiv preprint (2022).

[28]

Ashwin Rao, Fred Morstatter, Minda Hu, Emily Chen, Keith Burghardt, Emilio Ferrara, and Kristina Lerman. 2020. Political Partisanship and Anti-Science Attitudes in Online Discussions about Covid-19. arXiv preprint arXiv:2011.08498(2020).

[29]

Jacopo Staiano and Marco Guerini. 2014. Depeche Mood: a Lexicon for Emotion Analysis from Crowd Annotated News. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). Association for Computational Linguistics, Baltimore, Maryland, 427–433. https://doi.org/10.3115/v1/P14-2070

[30]

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.

[31]

Peter D Turney and Michael L Littman. 2003. Measuring praise and criticism: Inference of semantic orientation from association. ACM Transactions on Information Systems (TOIS) 21, 4 (2003), 315–346.

Digital Library

[32]

Felix JM Welter. 2020. Quantitative comparison of polarity lexicons in sentiment analysis tasks: Using a lexicon overlap score for similarity measurement between lexicons. SKILL 2020-Studierendenkonferenz Informatik(2020).

Cited By

Preniqi VGhinassi IIve JSaitis CKalimeri K(2024)MoralBERT: A Fine-Tuned Language Model for Capturing Moral Values in Social DiscussionsProceedings of the 2024 International Conference on Information Technology for Social Good10.1145/3677525.3678694(433-442)Online publication date: 4-Sep-2024
https://dl.acm.org/doi/10.1145/3677525.3678694
Youk SMalik MChen YHopp FWeber R(2023)Measures of Argument Strength: A Computational, Large-Scale Analysis of Effective Persuasion in Real-World DebatesCommunication Methods and Measures10.1080/19312458.2023.223086618:1(7-29)Online publication date: 10-Jul-2023
https://doi.org/10.1080/19312458.2023.2230866

Index Terms

LibertyMFD: A Lexicon to Assess the Moral Foundation of Liberty.
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Lexical semantics

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cover image ACM Conferences

GoodIT '22: Proceedings of the 2022 ACM Conference on Information Technology for Social Good

September 2022

436 pages

ISBN:9781450392846

DOI:10.1145/3524458

Copyright © 2022 ACM.

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Published: 07 September 2022

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GoodIT 2022: ACM International Conference on Information Technology for Social Good

September 7 - 9, 2022

Limassol, Cyprus

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Cited By

Preniqi VGhinassi IIve JSaitis CKalimeri K(2024)MoralBERT: A Fine-Tuned Language Model for Capturing Moral Values in Social DiscussionsProceedings of the 2024 International Conference on Information Technology for Social Good10.1145/3677525.3678694(433-442)Online publication date: 4-Sep-2024
https://dl.acm.org/doi/10.1145/3677525.3678694
Youk SMalik MChen YHopp FWeber R(2023)Measures of Argument Strength: A Computational, Large-Scale Analysis of Effective Persuasion in Real-World DebatesCommunication Methods and Measures10.1080/19312458.2023.223086618:1(7-29)Online publication date: 10-Jul-2023
https://doi.org/10.1080/19312458.2023.2230866

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