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  1. Preventing Violence: Insights from Micro-Sociology

    Micro-sociology of violence looks at what happens in situations where people directly threaten violence, but only sometimes carry it out. This process and its turning points have become easier to see in the current era of visual data: cell-phone videos, long-distance telephoto lenses, CCTV cameras. New cues and instruments are on the horizon as we look at emotional signals, body rhythms, and monitors for body signs such as heart rate (a proxy for adrenaline level).
  2. CASM: A Deep-Learning Approach for Identifying Collective Action Events with Text and Image Data from Social Media

    Protest event analysis is an important method for the study of collective action and social movements and typically draws on traditional media reports as the data source. We introduce collective action from social media (CASM)—a system that uses convolutional neural networks on image data and recurrent neural networks with long short-term memory on text data in a two-stage classifier to identify social media posts about offline collective action. We implement CASM on Chinese social media data and identify more than 100,000 collective action events from 2010 to 2017 (CASM-China).
  3. Collective Social Identity: Synthesizing Identity Theory and Social Identity Theory Using Digital Data

    Identity theory (IT) and social identity theory (SIT) are eminent research programs from sociology and psychology, respectively. We test collective identity as a point of convergence between the two programs. Collective identity is a subtheory of SIT that pertains to activist identification. Collective identity maps closely onto identity theory’s group/social identity, which refers to identification with socially situated identity categories. We propose conceptualizing collective identity as a type of group/social identity, integrating activist collectives into the identity theory model.
  4. Trouble in Tech Paradise

    The structures of the tech industry, with its dependence on highly skilled immigrant workers, and the H-1B visa, with its dependence on sponsoring companies, bind tech workers in a cycle of legal violence.

  5. Are Robots Stealing Our Jobs?

    The media and popular business press often invoke narratives that reflect widespread anxiety that robots may be rendering humans obsolete in the workplace. However, upon closer examination, many argue that automation, including robotics and artificial intelligence, is spreading unevenly throughout the labor market, such that middle-skill occupations that do not require a college degree are more likely to be affected adversely because they are easier to automate than high-skill occupations.

  6. No Longer Discrete: Modeling the Dynamics of Social Networks and Continuous Behavior

    The dynamics of individual behavior are related to the dynamics of the social structures in which individuals are embedded. This implies that in order to study social mechanisms such as social selection or peer influence, we need to model the evolution of social networks and the attributes of network actors as interdependent processes. The stochastic actor-oriented model is a statistical approach to study network-attribute coevolution based on longitudinal data. In its standard specification, the coevolving actor attributes are assumed to be measured on an ordinal categorical scale.
  7. Who Counts as a Notable Sociologist on Wikipedia? Gender, Race, and the “Professor Test”

    This paper documents and estimates the extent of underrepresentation of women and people of color on the pages of Wikipedia devoted to contemporary American sociologists. In contrast to the demographic diversity of the discipline, sociologists represented on Wikipedia are largely white men. The gender and racial/ethnic gaps in likelihood of representation have exhibited little change over time. Using novel data, we estimate the “risk” of having a Wikipedia page for a sample of contemporary sociologists.
  8. Social Space Diffusion: Applications of a Latent Space Model to Diffusion with Uncertain Ties

    Social networks represent two different facets of social life: (1) stable paths for diffusion, or the spread of something through a connected population, and (2) random draws from an underlying social space, which indicate the relative positions of the people in the network to one another. The dual nature of networks creates a challenge: if the observed network ties are a single random draw, is it realistic to expect that diffusion only follows the observed network ties? This study takes a first step toward integrating these two perspectives by introducing a social space diffusion model.
  9. Urban Hospitals as Anchor Institutions: Frameworks for Medical Sociology

    Recent policy developments are forcing many hospitals to supplement their traditional focus on the provision of direct patient care by using mechanisms to address the social determinants of health in local communities. Sociologists have studied hospital organizations for decades, to great effect, highlighting key processes of professional socialization and external influences that shape hospital-based care. New methods are needed, however, to capture more recent changes in hospital population health initiatives in their surrounding neighborhoods.
  10. A Novel Measure of Moral Boundaries: Testing Perceived In-group/Out-group Value Differences in a Midwestern Sample

    The literature on group differences and social identities has long assumed that value judgments about groups constitute a basic form of social categorization. However, little research has empirically investigated how values unite or divide social groups. The authors seek to address this gap by developing a novel measure of group values: third-order beliefs about in- and out-group members, building on Schwartz value theory. The authors demonstrate that their new measure is a promising empirical tool for quantifying previously abstract social boundaries.