Predicting film success has proven challenging, with prior research examining factors including budget, production studios, and stars, to varied degrees of accuracy. Missing was the impact of film scripts and latent linguistic features, examined here through textual analysis. Recent computational study identified the latent thematic content of nearly 200 romantic comedy films, revealing an increasing focus on romantic relationships and tumultuous courtship. We harness the same linguistic model to test whether changes in thematic content are associated with success in reviews, awards, and financial earnings. We find relationship-centered content is positively associated with earnings, mediated by the number of theaters.

We examined hashtag activism promoting Taiwan’s participation in the global efforts to combat COVID-19. We employed the computational Analysis of Topic Model Networks (ANTMN) to examine the discourse around the #TaiwanCanHelp/#TWforWHO campaign in 2020 ( N = 163,876) on Twitter. Our model identified 35 topics clustered within three frames. The containment frame emphasized strategies used to stop COVID-19’s spread in Taiwan. The geopolitics frame described China’s use of its international power to exclude Taiwan from the World Health Organization. The international cooperation frame emphasized Taiwan’s ability and efficacy to contribute to the global efforts to slow down COVID-19. These results extend our understanding of hashtag activism by examining the intersection of geopolitics and global health crises. We introduce the theoretical concept of a mutually beneficial coalition, one that points to detrimental impacts of oppression on both the oppressed and the allies who are asked to help.

According to the Great Replacement conspiracy theory, nonwhites, globalists and elites are plotting to eliminate the white race and its dominance through anti-white policies and increased immigration. In that context, abortion among white women is perceived by white nationalists (WN) as a betrayal of their “biological” and “traditional” gender role— procreation of white babies. While WN condemn abortion among white women as a murderous sin, at times they encourage the practice among nonwhites to solve demographic threats to white dominance. In this study, we use mixed methods, combining unsupervised machine learning with close textual analysis of 30,725 posts including the term “abortion” published on the WN website Stormfront between 2001 and 2017. We identify three broad themes: White genocide, focused on the conspiracy theory and detailing the active actors in its alleged execution; political, focused on political agendas and laws; and WN reproductive reasoning, articulating and justifying the contradiction between supporting abortion for nonwhites but not for whites via politics of difference that emphasize nonwhites’ supposed inferior morality. We discuss WN’s unique and explicitly racist discourse around a medical topic like abortion, a staple of the conservative and religious right for decades, and how it is used to alleviate their cognitive dissonance resulting from their dual-stance on abortion. Such discourse could be harnessed to recruit members into the movement and normalize extreme, racist ideologies.

Background: Within United States cultures and societies, topics related to death and dying continue to be taboo, and opportunities for presence and engagement during end-of-life that could lead to a good death are avoided as a result. Several efforts have been made to help people engage in advance care planning (ACP) conversations, including completing advance care directives, so they may express their goals-of-care if they become too sick to communicate their wishes. One major effort in the United States towards encouraging such challenging discussions is the annual celebration of National Healthcare Decisions Day (NHDD). Objective: The present study explored ACP from a socio-cultural perspective, using Twitter as a communication tool. Methods: All publicly available tweets published between August 1st 2020 and July 30th 2021 (N= 9,713) were collected and analyzed using the computational, mixed-method, Analysis of Topic Model Network (ANTMN) approach. Results: Results revealed conversations, driven primarily by laypersons (96% of tweets originated from unverified accounts)surrounded three major themes: importance and promotion, surrounding language, and finally, systemic issues. Conclusions: Based on the results, we argue there is a need for awareness of what barriers people may face in engaging in ACP conversations, including systemic barriers, literacy levels, misinformation, policies, including Medicare reimbursements, and trust among health care professionals, in the United States. This is incredibly important for clinicians and scholars across the globeto be aware of as westrive to re-envision ACP so that people are more comfortable engaging in ACP conversations. In terms of content of Tweets, we argue there is a chasm between the biomedical and biopsychosocial elements of ACP, including patient narratives. If used properly, Twitter conversations and NHDD hashtags could be harnessed to serve as a connecting point between organizations, physicians, patients and family members, to lay the groundwork for the trajectory towards a good death.

Resistance to vaccines has hindered attempts to contain and prevent outbreaks of infectious diseases for centuries. More recently, however, the term “vaccine hesitancy” has been used to describe not necessarily outright resistance but also a delay in acceptance or uncertainty regarding vaccines. Given concerns about hesitancy and its impact on vaccine uptake rates, researchers increasingly shifted the focus from resistance to vaccines towards vaccine hesitancy. Acknowledging the urgency to accurately assess the phenomenon, it is critical to understand the state of the literature, focusing on issues of conceptualization and operationalization. To carry out this systematic review, we collected and analyzed all published empirical articles from 2000 to 2021 that explicitly included quantitative self-report measures of vaccine hesitancy (k = 86). Using a mixed-method approach, the review demonstrates and quantifies crucial inconsistencies in the measurement of the construct, lack of clarity in regard to the determination of who should or should not be defined as hesitant, and overreliance on unrepresentative samples. Crucially, our analysis points to a potential systematic bias towards exaggerating the level of hesitancy in the population. Modeling a vaccine hesitancy co-citation network, the analysis also points to the existence of insular academic silos that make it harder to achieve a unified measurement tool. Theoretical and practical implications for academics, practitioners, and policymakers are discussed.