Using Internet-Derived Data to Measure Religion: Understanding How Google Can Provide Insight into Cross-National Religious Differences
Internet and social media data provide new sources of information for examining social issues, but their potential for scholars interested in religion remains unclear. Focusing on cross-national religion data, we test the validity of measures drawn from Google and Twitter against well-known existing...
| Authors: | ; ; |
|---|---|
| Format: | Electronic Article |
| Language: | English |
| Check availability: | HBZ Gateway |
| Journals Online & Print: | Drawer...
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| Fernleihe: | Fernleihe für die Fachinformationsdienste |
| Published: |
Oxford Univ. Press
2022
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| In: |
Sociology of religion
Year: 2022, Volume: 83, Issue: 2, Pages: 222-251 |
| Standardized Subjects / Keyword chains: | B
Religiosity
/ Comparison of cultures
/ Data analysis
/ Twitter (Softwareplattform)
/ Google
/ Mass data
|
| RelBib Classification: | AD Sociology of religion; religious policy AG Religious life; material religion ZG Media studies; Digital media; Communication studies |
| Online Access: |
Volltext (lizenzpflichtig) Volltext (lizenzpflichtig) |
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| 520 | |a Internet and social media data provide new sources of information for examining social issues, but their potential for scholars interested in religion remains unclear. Focusing on cross-national religion data, we test the validity of measures drawn from Google and Twitter against well-known existing data. We find that Google Trend (GT) searches for the dominant religions’ major holidays, along with “Buddhism,” can be validated against traditional sources. We also find that GT and traditional measures account for similar amounts of variation, and the GT measures do not differ substantially from established ones for explaining several cross-national outcomes (e.g., fertility, circumcision, and alcohol use), as well as new ones (e.g., interest in religious buildings and sex). The Twitter measures do not perform as well. Our study provides insight into best practices for generating and using these measures, and offers evidence that internet-generated data can replicate existing measures that are less accessible and more expensive. | ||
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| STA | 0 | 0 | |a Comparison of cultures,Culture,Intercultural comparison,Data analysis,Data evaluation,Data evaluation,Google,Mass data,Religiosity |
| STB | 0 | 0 | |a Big data,Données massives,Données massives,Google,Religiosité,Traitement des données,Études transculturelles,Cultures comparées,Cultures comparées |
| STC | 0 | 0 | |a Comparación intercultural,Datos de masa,Evaluación de los dados,Google,Religiosidad |
| STD | 0 | 0 | |a Analisi dei dati,Valutazione dei dati,Valutazione dei dati,Confronto interculturale,Dati di massa,Google,Religiosità |
| STE | 0 | 0 | |a 宗教性,文化比较,谷歌 |
| STF | 0 | 0 | |a 大數據,宗教性,文化比較,谷歌 |
| STG | 0 | 0 | |a Avaliação dos dados,Comparação intercultural,Dados de massa,Google,Religiosidade |
| STH | 0 | 0 | |a Google,Большой объём данных,Культурное сравнение,Оценка данных,Религиозность |
| STI | 0 | 0 | |a Google,Ανάλυση δεδομένων,Αξιολόγηση δεδομένων,Διαπολιτισμική σύγκριση,Θρησκευτικότητα,Μαζικά δεδομένα |
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| SYG | 0 | 0 | |a Religiösität,Religiösität , Interkultureller Vergleich,Cross-cultural Analysis,Interkultureller Vergleich,Cross-cultural Analysis , X , Massendaten,Mass Data |