The Morality Machine: Tracking Moral Values in Tweets

Teernstra, Livia; Putten, Peter van der; Noordegraaf-Eelens, Liesbeth; Verbeek, Fons J.

doi:10.1007/978-3-319-46349-0_3

Cited by 17 publications

(17 citation statements)

References 19 publications

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“…For each moral foundation, the MFD includes a list of words and word stems relating to that specific moral foundation (We will include an overview of the MFD in an online repository). As the original MFD does not contain the sixth moral foundation ‘Liberty/Oppression’, we use the list of indicating words for Liberty/Oppression created by Teernstra et al [ 30 ], who analyse tweets on the five original moral foundations and the sixth foundation Liberty/Oppression. Their list is the most established tool in the field for analysing texts on the moral foundation Liberty/Oppression (We include this list in the online S1 Appendix ).…”

Section: Methodsmentioning

confidence: 99%

“…To measure the use of moral foundations, we scan the brochures from RIVM for words included in the Dutch MFD using the software Linguistic Inquiry Word Count Program (LIWC), which was also used by Clifford and Jerit [ 27 ] and Teernstra et al [ 30 ]. The software analyses texts for words indicated by the user of the programme and produces a measure of the extent to which each of the moral foundations is used in the brochure.…”

Section: Methodsmentioning

confidence: 99%

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Using moral foundations in government communication to reduce vaccine hesitancy

Heine

Wolters

2021

PLoS ONE

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Having a vaccine available does not necessarily imply that it will be used. Indeed, uptake rates for existing vaccines against infectious diseases have been fluctuating in recent years. Literature suggests that vaccine hesitancy may be grounded in deeply rooted intuitions or values, which can be modelled using Moral Foundations Theory (MFT). We examine the respective prominence of the MFT dimensions in government communication regarding childhood vaccinations and explore its effect on parents’ vaccine hesitancy. We measure the MFT dimension loading of the vaccination information brochures from the Dutch National Institute for Public Health and the Environment (RIVM) between 2011-2019 and connect this information with the electronic national immunisation register to investigate if the use of moral foundations in government communication has a measurable effect on vaccination uptake. We find the largest positive effect for the dimensions Authority/Subversion and Liberty/Oppression and suggestive evidence in favour of a small positive effect for Purity/Degradation. Conversely, Loyalty/Betrayal actually has a negative effect on vaccination rates. For the dimension Harm/Care, we find no significant effect. While Purity/Degradation and Harm/Care appear to be the two most frequently used moral foundations by RIVM, these dimensions have in fact no or only a minor effect on parents’ vaccine hesitancy. Reducing the use of these moral foundations may be the first step towards optimising government communication in this context. Instead, formulations activating the moral foundations Authority/Subversion and Liberty/Oppression appear to have positive effects on vaccination uptake.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Using moral foundations in government communication to reduce vaccine hesitancy

Heine

Wolters

2021

PLoS ONE

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“…Clifford et al [29] employed the MFD for performing manual text analysis of 12 years of coverage in the New York Times focusing on political debate in the US. Teernstra et al [30] assessed the political debate regarding the "Grexit" from approximately 8,000 tweets. They compared the performance of using the raw data, bi-grams, and the MFD features in employing basic machine learning models, namely, Naive Bayes (NB) and Maximum Entropy (ME).…”

Section: Related Literaturementioning

confidence: 99%

MoralStrength: Exploiting a moral lexicon and embedding similarity for moral foundations prediction

Araque

Gatti

Kalimeri

2020

Knowledge-Based Systems

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Moral rhetoric plays a fundamental role in how we perceive and interpret the information we receive, greatly influencing our decision-making process. Especially when it comes to controversial social and political issues, our opinions and attitudes are hardly ever based on evidence alone. The Moral Foundations Dictionary (MFD) was developed to operationalize moral values in the text. In this study, we present MoralStrength, a lexicon of approximately 1,000 lemmas, obtained as an extension of the Moral Foundations Dictionary, based on Word-Net synsets. Moreover, for each lemma it provides with a crowdsourced numeric assessment of Moral Valence, indicating the strength with which a lemma is expressing the specific value. We evaluated the predictive potentials of this moral lexicon, defining three utilization approaches of increased complexity, ranging from lemmas' statistical properties to a deep learning approach of word embeddings based on semantic similarity. Logistic regression models trained on the features extracted from MoralStrength, significantly outperformed the current state-of-the-art, reaching an F1-score of 87.6% over the previous 62.4% (p-value< 0.01), and an average F1-Score of 86.25% over six different datasets. Such findings pave the way for further research, allowing for an in-depth understanding of moral narratives in text for a wide range of social issues.

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“…This software counts virtue and vice terms related to the five foundations and calculates a score that is a percentage of foundation terms in each text (Graham et al, 2009). This is a technique that has been used in previous research to determine moral foundations in texts (Bowe & Hoewe, 2016; Clifford & Jerit, 2013; Dehghani, Sagae, Sachdeva, & Gratch, 2014; Garten, Boghrati, Hoover, Johnson, & Dehghani, 2016; Graham et al, 2009; Jairam, 2012; Motyl, 2012; Sagi & Dehghani, 2014; Teernstra, van der Putten, Noordegraaf-Eelens, & Verbeek, 2016), thus replicating a well-established method. Lacy et al (2015) argued that algorithmic textual analysis tools (such as LIWC) are best used in studies of well-archived digital data in contexts (such as digitized newspaper stories storied in databases) that are concerned with explicitly manifest variables (like the ones in the MFT dictionary for LIWC).…”

Section: Methodsmentioning

confidence: 99%

Permitted to Build? Moral Foundations in Newspaper Framing of Mosque-Construction Controversies

Bowe

2017

Journalism & Mass Communication Quarterly

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Recent proposals by American Muslims to build mosques have been met with technical and ideological opposition during the permitting process. This article examines the framing of these debates in newspaper stories between 2010 and 2013 to better understand the socially constructed position Muslims hold in American media and public spheres. Connecting framing and Moral Foundations Theory, this analysis identifies five frames: Local Regulation, Political Debate, Muslim Neighbors, Islamic Threat, and Legal Authority. These frames emphasized binding moral foundations related to in-group protection and deference to authority. A binary logistic regression found that moral evaluations were associated with mosque support, but not mosque opposition.

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The Morality Machine: Tracking Moral Values in Tweets

Cited by 17 publications

References 19 publications

Using moral foundations in government communication to reduce vaccine hesitancy

Using moral foundations in government communication to reduce vaccine hesitancy

MoralStrength: Exploiting a moral lexicon and embedding similarity for moral foundations prediction

Permitted to Build? Moral Foundations in Newspaper Framing of Mosque-Construction Controversies

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