Sean C. Rife, PhD

Terror-management theory (TMT) proposes that when people are made aware of their own death, they are more likely to endorse cultural values. TMT is a staple of social psychology, featured prominently in textbooks and the subject of much research. The implications associated with TMT are significant because its advocates claim it can partially explain cultural conflicts, intergroup antagonisms, and even war. However, considerable ambiguity regarding effect size exists, and no preregistered replication of death-thought-accessibility findings exists. Moreover, there is debate regarding the role of time delay between the manipulation of mortality salience and assessment of key measures. We present results from 22 labs in 11 countries (total N = 3,447) attempting to replicate and extend an existing study of TMT, Study 3 from Trafimow and Hughes, and the role of time-delay effects. We successfully replicate Trafimow and Hughes and demonstrate that it is possible to prime death-related thoughts and that priming is more effective when there is no delay between the priming and outcome measure. Implications for future research and TMT are discussed.

The past few decades have experienced a decline in the use of traditional news sources as an increasing number of individuals rely on social media for information. Although this change has made it easier to obtain information, individuals often selectively expose themselves to information that confirms their beliefs. The current study examined if this pattern could explain political perceptions during the COVID-19 pandemic. Based on past research, it would be expected that liberals and conservatives would hold differing views of the COVID-19 pandemic. Republicans downplayed the pandemic and were more likely to consider it a hoax, while Democrats exaggerated the pandemic and were more likely to advocate for excessive measures. In this study, we collected two samples at different points during the pandemic in which we asked participants to indicate their political ideology, their perception of the COVID-19 pandemic, and the sources of information that they trusted. Our results indicated that trust in information sources mediated the relationship between political ideology and perceptions of the pandemic, suggesting that the informational sources that an individual trusted was a factor in determining perceptions of the COVID-19 pandemic.

Citation indices are tools used by the academic community for research and research evaluation that aggregate scientific literature output and measure impact by collating citation counts. Citation indices help measure the interconnections between scientific papers but fall short because they fail to communicate contextual information about a citation. The use of citations in research evaluation without consideration of context can be problematic because a citation that presents contrasting evidence to a paper is treated the same as a citation that presents supporting evidence. To solve this problem, we have used machine learning, traditional document ingestion methods, and a network of researchers to develop a “smart citation index” called scite, which categorizes citations based on context. Scite shows how a citation was used by displaying the surrounding textual context from the citing paper and a classification from our deep learning model that indicates whether the statement provides supporting or contrasting evidence for a referenced work, or simply mentions it. Scite has been developed by analyzing over 25 million full-text scientific articles and currently has a database of more than 880 million classified citation statements. Here we describe how scite works and how it can be used to further research and research evaluation.

The 2016 U.S. presidential election was marked by discussions of sexism, particularly in relation to Hillary Clinton’s candidacy. Previous research has suggested that hostile sexism played a role in voter preferences. In this study, we analyzed a large dataset of tweets (N = 5,962,713) collected during the three presidential debates and election night, classifying them for sexist content using BERTweet-large-sexism-detector. Electoral college outcomes and popular vote percentages for Donald Trump were modeled as a function of sexist tweets by state. Results indicated no significant correlation between sexism in tweets and election outcomes. However, a significant increase in sexist tweets was observed over time. Limitations and implications of these findings are discussed.