The COVID-19 pandemic has had disproportionately negative impacts on socially disadvantaged and underserved populations around the world. Inequality and the related social determinants that impact certain groups are directly related to the adverse health outcomes of vulnerable populations during the pandemic. People in disadvantaged communities are generally more prone to occupational exposure to the virus and tend to have limited access to health care and higher rates of comorbidities. Outcomes related to widespread school closures are also of particular concern for underserved communities. Additionally, these populations are more susceptible to the negative economic outcomes of the pandemic. There is an urgent need for research and policy solutions regarding the impact of the COVID-19, with particular attention to the needs of disadvantaged and vulnerable populations, a foundation for which is offered in this discussion.

A reflection on the World Medical Association's recent revision of the Declaration of Helsinki.

The recent surge in women reporting sexual assault and harassment (e.g., #metoo campaign) has highlighted a longstanding societal crisis. This injustice is partly due to a culture of discrediting women who report such crimes and also, rape myths (e.g., 'women lie about rape'). Social web can facilitate the further proliferation of deceptive beliefs and culture of rape myths through intentional messaging by malicious actors.This multidisciplinary study investigates Twitter posts related to sexual assaults and rape myths for characterizing the types of malicious intent, which leads to the beliefs on discrediting women and rape myths. Specifically, we first propose a novel malicious intent typology for social media using the guidance of social construction theory from policy literature that includes Accusational, Validational, or Sensational intent categories. We then present and evaluate a malicious intent classification model for a Twitter post using semantic features of the intent senses learned with the help of convolutional neural networks. Lastly, we analyze a Twitter dataset of four months using the intent classification model to study narrative contexts in which malicious intents are expressed and discuss their implications for gender violence policy design.

This analysis investigates the presence of rape myths in the policy context, as reflected in social media, and considers how those myths might manifest in policy related to sexual assault in accordance with the social construction theory of target populations. Contested interpretations of Title IX between the Obama and Trump administrations serve as an example of how underlying assumptions about target populations based on rape myths might influence policy design. The rape myth that women routinely lie about rape is investigated through an examination of its prevalence on Twitter using a dataset collected over a period of 4 months. From the dataset, a typology of tweets was created that identifies emergent themes reflecting the social construction of target populations, including accusers and the accused. These message categories—accusational, validational and sensational—have potential policy relevance, as between 44% and 48% of the tweets are found to be accusational, meaning that they express doubts about or undermine accusers, and/or perpetuate the idea that women lie about rape.

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