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1. The More Who Die, the Less We Care: Evidence from Natural Language Analysis of Online News Articles and Social Media Posts

   https://doi.org/10.1111/risa.13582  

   Bhatia, Sudeep ; Walasek, Lukasz ; Slovic, Paul ; Kunreuther, Howard ( January 2020 , Risk Analysis)

   null (Ed.)

   Considerable amount of laboratory and survey‐based research finds that people show disproportional compassionate and affective response to the scope of human mortality risk. According to research on “psychic numbing,” it is often the case that the more who die, the less we care. In the present article, we examine the extent of this phenomenon in verbal behavior, using large corpora of natural language to quantify the affective reactions to loss of life. We analyze valence, arousal, and specific emotional content of over 100,000 mentions of death in news articles and social media posts, and find that language shows an increase in valence (i.e., decreased negative affect) and a decrease in arousal when describing mortality of larger numbers of people. These patterns are most clearly reflected in specific emotions of joy and (in a reverse fashion) of fear and anger. Our results showcase a novel methodology for studying affective decision making, and highlight the robustness and real‐world relevance of psychic numbing. They also offer new insights regarding the psychological underpinnings of psychic numbing, as well as possible interventions for reducing psychic numbing and overcoming social and psychological barriers to action in the face of the world's most serious threats. 

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2. Who is the "Human" in Human-Centered Machine Learning: The Case of Predicting Mental Health from Social Media

   https://doi.org/10.1145/3359249  

   Chancellor, Stevie ; Baumer, Eric P. ; De Choudhury, Munmun ( November 2019 , Proceedings of the ACM on Human-Computer Interaction)
3. Conspiracy and debunking narratives about COVID-19 origins on Chinese social media: How it started and who is to blame

   https://doi.org/10.37016/mr-2020-50  

   Chen, Kaiping ; Chen, Anfan ; Zhang, Jingwen ; Meng, Jingbo ; Shen, Cuihua ( December 2020 , Harvard Kennedy School Misinformation Review)

   null (Ed.)

   This paper studies conspiracy and debunking narratives about the origins of COVID-19 on a major Chinese social media platform, Weibo, from January to April 2020. Popular conspiracies about COVID-19 on Weibo, including that the virus is human-synthesized or a bioweapon, differ substan-tially from those in the United States. They attribute more responsibility to the United States than to China, especially following Sino-U.S. confrontations. Compared to conspiracy posts, debunking posts are associated with lower user participation but higher mobilization. Debunking narratives can be more engaging when they come from women and influencers and cite scientists. Our find-ings suggest that conspiracy narratives can carry highly cultural and political orientations. Correc-tion efforts should consider political motives and identify important stakeholders to reconstruct international dialogues toward intercultural understanding. 

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4. Social media users produce more affect that supports cultural values, but are more influenced by affect that violates cultural values.

   https://doi.org/10.1037/pspa0000282  

   Hsu, Tiffany W. ; Niiya, Yu ; Thelwall, Mike ; Ko, Michael ; Knutson, Brian ; Tsai, Jeanne L. ( November 2021 , Journal of Personality and Social Psychology)
5. A social-belonging intervention improves STEM outcomes for students who speak English as a second language

   https://doi.org/10.1126/sciadv.abb6543  

   LaCosse, Jennifer ; Canning, Elizabeth A. ; Bowman, Nicholas A. ; Murphy, Mary C. ; Logel, Christine ( October 2020 , Science Advances)

   null (Ed.)

   Students who speak English as a second language (ESL) are underserved and underrepresented in postsecondary science, technology, engineering, and math (STEM) fields. To date, most existing research with ESL students in higher education is qualitative. Drawing from this important body of work, we investigate the impact of a social-belonging intervention on anticipated changes in belonging, STEM GPA, and proportion of STEM credits obtained in students’ first semester and first year of college. Using data from more than 12,000 STEM-interested students at 19 universities, results revealed that the intervention increased ESL students’ anticipated sense of belonging and three of the four academic outcomes. Moreover, anticipated changes in belonging mediated the intervention’s effects on these academic outcomes. Robustness checks revealed that ESL effects persisted even when controlling for other identities correlated with ESL status. Overall, results suggest that anticipated belonging is an understudied barrier to creating a multilingual and diverse STEM workforce. 

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