Comparative opinion mining of tweets on retracted papers and their valid peers: a semi-experimental follow-up

The underlying motivations for increasingly recognizing invalid papers remain unclear. Previous content-based analyses have revealed the coexistence of negativity and positivity, with each polarity prevailing at times. Comparative analysis may further clarify these trends by evaluating them against those of valid papers. To highlight how a paper’s validity impacts its dissemination and perception on social media, this study applied a semi-experimental paired research design to mine tweet opinions on a corpus of retracted papers and their thematically similar, non-retracted counterparts, serving as experimental and control groups, respectively. The Generalized Linear Mixed Model (GLMM) was employed to compare tweet shares of the paper groups, measured repeatedly in opinion polarities and pre- and post-retraction periods. The findings indicate that retracted papers were tweeted more frequently than their valid counterparts. The GLMM revealed that negative tweets were more prevalent than positive ones among retracted papers in the pre-retraction phase. In contrast, tweets about non-retracted papers were statistically balanced regarding polarity shares. Before retraction, positive tweets were significantly fewer for retracted papers compared to their valid peers. Despite being low in number, positive tweets on retracted papers remained stable after retraction. Consequently, retracted-would-be papers attract substantial attention on social media, particularly negative sentiment, compared to non-retracted papers. The prevalence of negative sentiment before retraction reflects heightened scrutiny and skepticism, which diminishes after retraction, likely because tweeters feel their concerns are addressed. The persistence of positive tweets about retracted papers suggests issues such as the initial reception's resistance to change and the ineffectiveness of retraction notices, calling for improved strategies to mitigate the spread of scientific misinformation.