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

Abstract

BackgroundIn 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.ObjectivesThe 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.MethodsThe 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.ResultsDespite 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).ConclusionsThese 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.

The Application of Text Mining Algorithms In Summarizing Trends in Anti-Epileptic Drug Research

“…Sentiment Analysis and Tm packages assign three sentiment scores ("positive," "negative," and "neutral") to each word, based on a generalized classification system developed by the authors which uses a combination of human-annotated and Artificial Intelligence based sentiment scoring algorithms (Bagheri & Islam, 2017). Further, we employed the "bag-of-words" approach which has been established to be very dependable for document-level SA, with aggregate-level performance approximately equivalent to more refined methods (Gayle & Shimaoka, 2017).…”

“…All input terms, i.e. the bigrams can thus be assessed in terms of "importance" with respect to a given label (Gayle & Shimaoka, 2017).The classifier was retrained on a 7000-abstract sample curated dataset optimized for misclassification rate, precision and recall metrics.…”

The application of text mining algorithms in summarizing trends in anti-epileptic drug research

“…Sentiment Analysis and Tm packages assign three sentiment scores ("positive," "negative," and "neutral") to each word, based on a generalized classification system developed by the authors which uses a combination of human-annotated and Artificial Intelligence based sentiment scoring algorithms (Bagheri and Islam, 2017). Further, we employed the "bag-of-words" approach which has been established to be very dependable for document-level SA, with aggregate-level performance approximately equivalent to more refined methods (Gayle and Shimaoka, 2017).…”

“…All input terms, i.e. the bigrams can thus be assessed in terms of "importance" with respect to a given label (Gayle and Shimaoka, 2017).The classifier was retrained on a 7000-abstract sample curated dataset optimized for misclassification rate, precision and recall metrics. Manuscript to be reviewed…”

The Role of the Humanitarian Logistics in the Kiss Nightclub Case

“…However, as this attention tends to decline in the medium and long-term, the number of donations also suffers a substantial reduction. In addition, the media is essential to plea for professionals from different backgrounds to assist in the victims care and recovery of the affected area [5] [6]. In addition to the volunteers, a disaster requires the involvement of several professional teams and institutions.…”