Public Response to Scientific Misconduct: Assessing Changes in Public Sentiment Toward the Stimulus-Triggered Acquisition of Pluripotency (STAP) Cell Case via Twitter

Abstract

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.

Keywords: Japan; data mining; mass media; public opinion; public policy; publication; retraction of publication as a topic; scientific misconduct; social media; stem cells.

Figure 1

Volume and average sentiment over time. Sentiment score calculated using unweighted aggregate sentiment scores found in the SentiWordNet database, for each valid token in each Tweet. For this analysis, verb, adjectives, and adverbs were considered valid for the purpose of sentiment scoring. Volume is based on number of Tweets retrieved per sampling interval. Sentiment increasingly negative over time; one key exception corresponds with the tragedy surrounding Dr Sasai (August to October 2014). Volume is driven by major events.

Figure 2

Month-to-month trinary sentiment or volume density chart. Density plot calculated based on the proportion of negative (N: top left), positive (P: top right), and objective or nonpolarized (O: bottom center) discussion volume (represented by the unlabeled data points). Volume density is calculated via isometric log ratio transformation.

Figure 3

Sentiment comparison for key actors. Month-to-month sentiment for key figures and entities corresponds with associated timeline events. Month-to-month sentiment scores were independently aggregated for Tweets mentioning Ms Obokata, Dr Sasai, or Riken. Data labels shown where mean differences are significant versus total.