Who supports Bernie? Analyzing identity and ideological variation on Twitter during the 2020 democratic primaries.

Using a novel dataset of 590M messages by 21M users, we present the first large-scale examination of the behavior of likely Bernie supporters on Twitter during the 2020 U.S. Democratic primaries and presidential election. We use these data to dispel empirically the notion of a unified, stereotypical Bernie supporter (e.g., the "Bernie Bro"). Instead, our work uncovers significant variation in the identities and ideologies of Bernie supporters who were active on Twitter. Our work makes three contributions to the literature on social media and social movements. Methodologically, we present a novel mixed methods approach to surface identity and ideological variation within a movement via use of patterns in who retweets whom (i.e. who retweets which other users) and who retweets what (i.e. who retweets which specific tweets). Substantively, documentation of these variations challenges a trend in the social movement literature to assume actors within a particular movement are unified in their ideology, identity, and values.

Field-specific ability beliefs as an explanation for gender differences in academics' career trajectories: Evidence from public profiles on ORCID.Org.

Academic fields exhibit substantial levels of gender segregation. Here, we investigated differences in field-specific ability beliefs (FABs) as an explanation for this phenomenon. FABs may contribute to gender segregation to the extent that they portray success as depending on "brilliance" (i.e., exceptional intellectual ability), which is a trait culturally associated with men more than women. Although prior work has documented a relation between academic fields' FABs and their gender composition, it is still unclear what the underlying dynamics are that give rise to gender imbalances across academia as a function of FABs. To provide insight into this issue, we custom-built a new data set by combining information from the author-tracking service Open Researcher and Contributor ID (ORCID) with information from a survey of U.S. academics across 30 fields. Using this expansive longitudinal data set (Ns = 86,879-364,355), we found that women were underrepresented among those who enter fields with brilliance-oriented FABs and overrepresented among those who exit these fields. We also found that FABs' association with women's transitions across academic fields was substantially stronger than their association with men's transitions. With respect to mechanisms, FABs' association with gender segregation was partially explained by the fact that women encounter more prejudice in fields with brilliance-oriented FABs. With its focus on the dynamic patterns shaping segregation and its broad scope in terms of geography, career stage, and historical time, this research makes an important contribution toward understanding the factors driving gender segregation in academia. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Dementia Research on Facebook and Twitter: Current Practice and Challenges.

BACKGROUND: Social media is a powerful tool for engaging diverse audiences in dementia research. However, there is little data summarizing current content exchange in this context. OBJECTIVE: To inform ethical dementia research engagement on social media, we characterized current practices by analyzing public social media posts. METHODS: We retrieved Facebook (2-year period, N = 7,896) and Twitter (1-year period, N = 9,323) posts containing dementia research-related keywords using manual and machine learning-based search strategies. We performed qualitative and quantitative content and sentiment analyses on random samples (10%) of the posts. RESULTS: Top Facebook users were advocacy (45%) and health organizations (25%). On Twitter, academics/researchers were the largest user group. Prevention was the most frequently coded theme (Facebook 30%; Twitter 26%), followed by treatment (Facebook 15%; Twitter 18%). Diagnostics had the highest Facebook engagement. Sharing knowledge was the primary form of content exchange (Facebook 63%; Twitter 80%). Most shared journal articles were peer-reviewed and open access. Emotional tone was overall more positive on Facebook. Justice was a prominent ethics topic regarding inequalities related to identity and intersecting modes of marginalization in dementia research. CONCLUSION: The findings indicate the importance of social media as an engagement tool of current topics in health research and reveal areas of potential for increased engagement. These data can inform consensus-based best practices for ethical social media application in dementia research.

Emergency Control of Large Aneurysmal Bleed During Endoscopic Sinus Surgery: A Case Report.

Functional endoscopic sinus surgery (FESS) is a commonly performed procedure for the treatment of chronic rhinosinusitis. It is most commonly performed as an outpatient procedure. Complications occur, including injury to the orbits, brain, and bleeding. Intraoperative injury to the internal carotid artery is rare and feared by otolaryngologists and skull base surgeons. Controlling this bleed is difficult and could be fatal. We report a case of intraoperative bleed of an internal carotid artery aneurysm during endoscopic sinus surgery. This case demonstrates that multidisciplinary coordination and prompt management can lead to a favorable outcome.

Theory In, Theory Out: The Uses of Social Theory in Machine Learning for Social Science.

Research at the intersection of machine learning and the social sciences has provided critical new insights into social behavior. At the same time, a variety of issues have been identified with the machine learning models used to analyze social data. These issues range from technical problems with the data used and features constructed, to problematic modeling assumptions, to limited interpretability, to the models' contributions to bias and inequality. Computational researchers have sought out technical solutions to these problems. The primary contribution of the present work is to argue that there is a limit to these technical solutions. At this limit, we must instead turn to social theory. We show how social theory can be used to answer basic methodological and interpretive questions that technical solutions cannot when building machine learning models, and when assessing, comparing, and using those models. In both cases, we draw on related existing critiques, provide examples of how social theory has already been used constructively in existing work, and discuss where other existing work may have benefited from the use of specific social theories. We believe this paper can act as a guide for computer and social scientists alike to navigate the substantive questions involved in applying the tools of machine learning to social data.

Fake news on Twitter during the 2016 U.S. presidential election.

The spread of fake news on social media became a public concern in the United States after the 2016 presidential election. We examined exposure to and sharing of fake news by registered voters on Twitter and found that engagement with fake news sources was extremely concentrated. Only 1% of individuals accounted for 80% of fake news source exposures, and 0.1% accounted for nearly 80% of fake news sources shared. Individuals most likely to engage with fake news sources were conservative leaning, older, and highly engaged with political news. A cluster of fake news sources shared overlapping audiences on the extreme right, but for people across the political spectrum, most political news exposure still came from mainstream media outlets.

Online extremism and the communities that sustain it: Detecting the ISIS supporting community on Twitter.

The Islamic State of Iraq and ash-Sham (ISIS) continues to use social media as an essential element of its campaign to motivate support. On Twitter, ISIS' unique ability to leverage unaffiliated sympathizers that simply retweet propaganda has been identified as a primary mechanism in their success in motivating both recruitment and "lone wolf" attacks. The present work explores a large community of Twitter users whose activity supports ISIS propaganda diffusion in varying degrees. Within this ISIS supporting community, we observe a diverse range of actor types, including fighters, propagandists, recruiters, religious scholars, and unaffiliated sympathizers. The interaction between these users offers unique insight into the people and narratives critical to ISIS' sustainment. In their entirety, we refer to this diverse set of users as an online extremist community or OEC. We present Iterative Vertex Clustering and Classification (IVCC), a scalable analytic approach for OEC detection in annotated heterogeneous networks, and provide an illustrative case study of an online community of over 22,000 Twitter users whose online behavior directly advocates support for ISIS or contibutes to the group's propaganda dissemination through retweets.

Training providers: beyond the basics of electronic health records.

BACKGROUND: Training is a critical part of health information technology implementations, but little emphasis is placed on post-implementation training to support day-to-day activities. The goal of this study was to evaluate the impact of post-implementation training on key electronic health record activities. METHODS: Based on feedback from providers and requests for technical support, we developed two classes designed to improve providers' effectiveness with the electronic health record. Training took place at Kaiser Permanente, Mid-Atlantic States. The classes focused on managing patient-level information using problem lists and medication lists, as well as efficient documentation and chart review. Both classes used the blended learning method, integrating concrete scenarios, hands-on exercises and take-home materials to reinforce class concepts. To evaluate training effectiveness, we used a case-control study with a 1:4 match on pre-training performance. We measured the usage rate of two key electronic health record functions (problem list and medication list management) for six months before and after training. Change scores were compared using the Wilcoxon sign rank test. RESULTS: 36 participants and 144 non-participants were included in the training evaluation. Training participants were more likely to manage both medication lists and problem lists after training. Class material is now being incorporated into an enterprise-wide multi-modal training program available to all providers at Kaiser Permanente in the Mid-Atlantic States. CONCLUSIONS: Ongoing information technology training is well-received by healthcare providers, who expressed a clear preference for additional training. Training improved use of two important electronic health record features that are included as part of the Meaningful Use criteria.