The Structural Transformation of the Public Sphere: An Inquiry into a Category of Bourgeois Society

(1974). History and Class Consciousness. Studies in Marxist Dialectics. Journal of Economic Issues: Vol. 8, No. 1, pp. 167-170.

History and Class Consciousness. Studies in Marxist Dialectics

Large volumes of spatio-temporal data are increasingly collected and studied in diverse domains, including climate science, social sciences, neuroscience, epidemiology, transportation, mobile health, and Earth sciences. Spatio-temporal data differ from relational data for which computational approaches are developed in the data-mining community for multiple decades in that both spatial and temporal attributes are available in addition to the actual measurements/attributes. The presence of these attributes introduces additional challenges that needs to be dealt with. Approaches for mining spatio-temporal data have been studied for over a decade in the data-mining community. In this article, we present a broad survey of this relatively young field of spatio-temporal data mining. We discuss different types of spatio-temporal data and the relevant data-mining questions that arise in the context of analyzing each of these datasets. Based on the nature of the data-mining problem studied, we classify literature on spatio-temporal data mining into six major categories: clustering, predictive learning, change detection, frequent pattern mining, anomaly detection, and relationship mining. We discuss the various forms of spatio-temporal data-mining problems in each of these categories.

https://dl.acm.org/doi/pdf/10.1145/3161602

Spatio-Temporal Data Mining: A Survey of Problems and Methods

In this article, we explore the potential role of social media in helping movements expand and/or strengthen themselves internally, processes we refer to as scaling up. Drawing on a case study of B...

https://journals.sagepub.com/doi/pdf/10.1177/2056305118807911

Scaling Social Movements Through Social Media: The Case of Black Lives Matter:

Large volumes of spatio-temporal data are increasingly collected and studied in diverse domains including, climate science, social sciences, neuroscience, epidemiology, transportation, mobile health, and Earth sciences. Spatio-temporal data differs from relational data for which computational approaches are developed in the data mining community for multiple decades, in that both spatial and temporal attributes are available in addition to the actual measurements/attributes. The presence of these attributes introduces additional challenges that needs to be dealt with. Approaches for mining spatio-temporal data have been studied for over a decade in the data mining community. In this article we present a broad survey of this relatively young field of spatio-temporal data mining. We discuss different types of spatio-temporal data and the relevant data mining questions that arise in the context of analyzing each of these datasets. Based on the nature of the data mining problem studied, we classify literature on spatio-temporal data mining into six major categories: clustering, predictive learning, change detection, frequent pattern mining, anomaly detection, and relationship mining. We discuss the various forms of spatio-temporal data mining problems in each of these categories.

This article demonstrates the ways in which youth of color played an active role in debates that erupted on Twitter following the tragic deaths of Michael Brown and Eric Garner in 2014. These debates on social media represent a larger struggle over discourse on race and racism across the nation. Drawing from critical theory and race theory, and engaging in the relatively new practice of using Twitter as a source of data for sociological analysis, this article examines Twitter as an emerging public sphere and studies the hashtags “#AllLivesMatter” and “#BlackLivesMatter” as contested signs that represent dominant ideologies. This article consists of a qualitative textual analysis of a selection of Twitter posts from December 3 to 7, 2014, following the nonindictments of officers in the murders of Michael Brown and Eric Garner. The debates on Twitter reveal various strategies that youth of color employed to shape the national discourse about race in the wake of these high-profile tragedies.

All Lives Matter, but so Does Race: Black Lives Matter and the Evolving Role of Social Media

The police killings of Breonna Taylor and George Floyd sparked a resurgence of Black Lives Matter protests throughout the summer of 2020, reminiscent of the wave of Black Lives Matter protests that occurred after several police killings in 2014 including the deaths of Michael Brown and Eric Garner. Based on qualitative analysis of mainstream media coverage of the protests, this paper examines key themes in the discourse surrounding the Black Lives Matter movement in 2014 and 2020. Our findings highlight the ways in which mainstream news sources situate the Black Lives Matter protests within a broader history of Black uprisings. We also emphasize the erasure of violence against Black women in mainstream media depictions of the BLM movement, as well as the erasure of Black women’s leadership in the movement.

Nikita Carney

Papers

All Lives Matter, but so Does Race: Black Lives Matter and the Evolving Role of Social Media

Framing the Black Lives Matter Movement: An Analysis of Shifting News Coverage in 2014 and 2020