Human Values and Attitudes towards Vaccination in Social Media

Kalimeri, Kyriaki; Beiró, Mariano G.; Urbinati, Alessandra; Bonanomi, Andrea; Rosina, Alessandro; Cattuto, Ciro

doi:10.1145/3308560.3316489

Cited by 43 publications

(37 citation statements)

References 46 publications

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“…Leveraging on the immense amount of digital data produced daily, the field of Digital Demography emerged, addressing vital research questions of demographic research via innovative data sources. These new sources of data are demonstrated to be particularly powerful in monitoring a series of demographic phenomena such as birthrates [5], mortality [4], unemployment [10], daily commuting [6], international and internal migration [55], but also modelling more complex socio-demographic issues such as psychological well-being and attitudes towards health [24,30]. Digital data are particularly useful in cases where official data are sparse, incomplete, or even impossible to obtain.…”

Section: Related Workmentioning

confidence: 99%

Facebook Ads as a Demographic Tool to Measure the Urban-Rural Divide

Rama

Mejova

Tizzoni

et al. 2020

Proceedings of the Web Conference 2020

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In the global move toward urbanization, making sure the people remaining in rural areas are not left behind in terms of development and policy considerations is a priority for governments worldwide. However, it is increasingly challenging to track important statistics concerning this sparse, geographically dispersed population, resulting in a lack of reliable, up-to-date data. In this study, we examine the usefulness of the Facebook Advertising platform, which offers a digital "census" of over two billions of its users, in measuring potential rural-urban inequalities. We focus on Italy, a country where about 30% of the population lives in rural areas. First, we show that the population statistics that Facebook produces suffer from instability across time and incomplete coverage of sparsely populated municipalities. To overcome such limitation, we propose an alternative methodology for estimating Facebook Ads audiences that nearly triples the coverage of the rural municipalities from 19% to 55% and makes feasible fine-grained sub-population analysis. Using official national census data, we evaluate our approach and confirm known significant urban-rural divides in terms of educational attainment and income. Extending the analysis to Facebook-specific user "interests" and behaviors, we provide further insights on the divide, for instance, finding that rural areas show a higher interest in gambling. Notably, we find that the most predictive features of income in rural areas differ from those for urban centres, suggesting researchers need to consider a broader range of attributes when examining rural wellbeing. The findings of this study illustrate the necessity of improving existing tools and methodologies to include under-represented populations in digital demographic studies -the failure to do so could result in misleading observations, conclusions, and most importantly, policies.

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Section: Related Workmentioning

confidence: 99%

Facebook Ads as a Demographic Tool to Measure the Urban-Rural Divide

Rama

Mejova

Tizzoni

et al. 2020

Proceedings of the Web Conference 2020

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“…In this study, we propose a lexicon for detecting and quantifying the moral rhetoric behind people's judgments, as reflected in spontaneous digital interactions. Moral values influence the way we rationalize and take a stance upon controversial topics, like abortion [1], homosexuality [1], climate change [2], or even vaccine hesitancy [3,4]. They are also closely related to our political views [5] and the opinion formation mechanisms regarding immigration [6], political extremism [7,1], and poverty [8].…”

Section: Introductionmentioning

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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“…It has been argued that these moral foundations are unlikely to be truly innate, modular, or neurobiologically descriptive (Suhler & Churchland, 2011). However, the theory can be a useful framework for describing and understanding people's motives and justifications, and the ways in which they are influenced by others, in an applied sociopolitical context (inter alia, Feinberg & Willer, 2013;Day et al, 2014;Low & Wui, 2016;Kalimeri et al, 2019;Christie et al, 2019).…”

Section: Introductionmentioning

confidence: 99%