Public Response to Scientific Misconduct: Assessing Changes in Public Sentiment Toward the Stimulus-Triggered Acquisition of Pluripotency (STAP) Cell Case via Twitter
Notice bibliographique
Résumé
BACKGROUND: In this age of social media, any news-good or bad-has the potential to spread in unpredictable ways. Changes in public sentiment have the potential to either drive or limit investment in publicly funded activities, such as scientific research. As a result, understanding the ways in which reported cases of scientific misconduct shape public sentiment is becoming increasingly essential-for researchers and institutions, as well as for policy makers and funders. In this study, we thus set out to assess and define the patterns according to which public sentiment may change in response to reported cases of scientific misconduct. This study focuses on the public response to the events involved in a recent case of major scientific misconduct that occurred in 2014 in Japan-stimulus-triggered acquisition of pluripotency (STAP) cell case. OBJECTIVES: The aims of this study were to determine (1) the patterns according to which public sentiment changes in response to scientific misconduct; (2) whether such measures vary significantly, coincident with major timeline events; and (3) whether the changes observed mirror the response patterns reported in the literature with respect to other classes of events, such as entertainment news and disaster reports. METHODS: The recent STAP cell scandal is used as a test case. Changes in the volume and polarity of discussion were assessed using a sampling of case-related Twitter data, published between January 28, 2014 and March 15, 2015. Rapidminer was used for text processing and the popular bag-of-words algorithm, SentiWordNet, was used in Rapidminer to calculate sentiment for each sample Tweet. Relative volume and sentiment was then assessed overall, month-to-month, and with respect to individual entities. RESULTS: Despite the ostensibly negative subject, average sentiment over the observed period tended to be neutral (-0.04); however, a notable downward trend (y=-0.01 x +0.09; R ²=.45) was observed month-to-month. Notably polarized tweets accounted for less than one-third of sampled discussion: 17.49% (1656/9467) negative and 12.59% positive (1192/9467). Significant polarization was found in only 4 out of the 15 months covered, with significant variation month-to-month (P<.001). Significant increases in polarization tended to coincide with increased discussion volume surrounding major events (P<.001). CONCLUSIONS: These results suggest that public opinion toward scientific research may be subject to the same sensationalist dynamics driving public opinion in other, consumer-oriented topics. The patterns in public response observed here, with respect to the STAP cell case, were found to be consistent with those observed in the literature with respect to other classes of news-worthy events on Twitter. Discussion was found to become strongly polarized only during times of increased public attention, and such increases tended to be driven primarily by negative reporting and reactionary commentary.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,023 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,000 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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 source (Gemma direct ou Codex distillé), 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 ».