A New Web-Based Big Data Analytics for Dynamic Public Opinion Mapping in Digital Networks on Contested Biotechnology Fields
Notice bibliographique
Résumé
The expression "public opinion" has long been part of common parlance. However, its value as a scientific measure has been the topic of abundant academic debates over the past several decades. Such debates have produced more variety and contestations rather than consensus on the very definition of public opinion, let alone on how to measure it. This study reports on the usefulness of web-based big data digital network analytics in deciphering the distributed meanings and sense making related to controversial biotechnology applications. Using stem cell therapies as a case study, we argue that such digital network analysis can complement the traditional opinion polls while avoiding the sampling bias that is typical of opinion polls. Although the polls cannot account for the opinion dynamics, combining them with web-based big data analysis can shed light on three dimensions of public opinion essential for sense making: counts or volume of opinion data, content, and movement of opinions. This approach is particularly promising in the case of ongoing scientific controversies that increasingly overflow into the public sphere morphing into public political debates. In particular, our study focuses as a case study on public controversies over the clinical provision of stem cell therapies. Using web entities specifically addressing stem cell issues, including their dynamic aggregation, the internal architecture of the web corpus we report in this study brings the third dimension of public opinion (movement) into sharper focus. Notably, the corpus of stem cell networks through web connectivity presents hot spots of distributed meaning. Large-scale surveys conducted on these issues, such as the Eurobarometer of Biotechnology, reveal that European citizens only accept research on stem cells if they are highly regulated, while the stem cell digital network analysis presented in this study suggests that distributed meaning is promise centeredness. Although major scientific journals and companies tend to structure public opinion networks, our finding of promise centeredness as a key ingredient of distributed meaning and sense making is consistent with therapeutic tourism that remains as an important facet of the stem cell community despite the lack of material standards. This new approach to digital network analysis has crosscutting corollaries for rethinking the notion of public opinion, be it in electoral preferences or as we discuss in this study, for new ways to measure, monitor, and democratically govern emerging technologies.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».