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  1. Review Essay: See It with Figures

    The short story is that Kieran Healy’s Data Visualization: A Practical Introduction is a gentle introduction to the effective display of social science data using the R package ggplot2. It is beautifully put together, achingly clear, and effective.
  2. Review Essay: The Digital Surveillance Society

    When hundreds of thousands of protesters filled the streets of Hong Kong this summer, central figures reportedly took no selfies, avoided Facebook and Twitter, installed prepaid SIM cards, stuck to secure messaging apps, and used cash instead of rechargeable subway cards or other cashless payments. It is not clear whether this will help them avoid “conspiracy to commit public nuisance” charges, which led to prison sentences for leaders of the 2014 Umbrella movement (including sociologist Kin-man Chan).
  3. The Geometry of Culture: Analyzing the Meanings of Class through Word Embeddings

    We argue word embedding models are a useful tool for the study of culture using a historical analysis of shared understandings of social class as an empirical case. Word embeddings represent semantic relations between words as relationships between vectors in a high-dimensional space, specifying a relational model of meaning consistent with contemporary theories of culture.
  4. Featured Essay: 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).
  5. 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).
  6. Assessing Differences between Nested and Cross-Classified Hierarchical Models

    Sociological Methodology, Volume 49, Issue 1, Page 220-257, August 2019.
  7. Review Essay: Back to the Future

    In one of my undergraduate courses, I show students a photo of Paul Lazarsfeld and Frank Stanton. Of course, neither social scientist is familiar to them, but I argue to my students that Lazarsfeld had a bigger impact on the daily practice of sociology than any member of the Marx/Weber/Durkheim triumvirate they study in classical theory.

  8. 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.
  9. A General Framework for Comparing Predictions and Marginal Effects across Models

    Many research questions involve comparing predictions or effects across multiple models. For example, it may be of interest whether an independent variable’s effect changes after adding variables to a model. Or, it could be important to compare a variable’s effect on different outcomes or across different types of models. When doing this, marginal effects are a useful method for quantifying effects because they are in the natural metric of the dependent variable and they avoid identification problems when comparing regression coefficients across logit and probit models.
  10. 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.