Digital White Racial Socialization: Social Media and the Case of Whiteness.

The Internet has become a ubiquitous central element in the lives of adolescents. In this conceptual paper, we focus on digital white racial socialization (D-WRS), arguing: (1) for an expanded conceptualization of WRS as doings, and (2) that social media may be changing processes of WRS through an extension of traditional settings and through the creation of unique social contexts. We highlight the uniqueness of social media contexts due to the designed normalization of whiteness, weak-tie racism, social media affordances, and racialized pedagogical zones allowing adolescents to practice doing race. We introduce a conceptual framework for D-WRS and end with an expressed need for conceptually guided research on the multidimensional relationship between social media and WRS processes.

Contextual Analysis of Social Media: The Promise and Challenge of Eliciting Context in Social Media Posts with Natural Language Processing.

While natural language processing affords researchers an opportunity to automatically scan millions of social media posts, there is growing concern that automated computational tools lack the ability to understand context and nuance in human communication and language. This article introduces a critical systematic approach for extracting culture, context and nuance in social media data. The Contextual Analysis of Social Media (CASM) approach considers and critiques the gap between inadequacies in natural language processing tools and differences in geographic, cultural, and age-related variance of social media use and communication. CASM utilizes a team-based approach to analysis of social media data, explicitly informed by community expertise. We use of CASM to analyze Twitter posts from gang-involved youth in Chicago. We designed a set of experiments to evaluate the performance of a support vector machine using CASM hand-labeled posts against a distant model. We found that the CASM-informed hand-labeled data outperforms the baseline distant labels, indicating that the CASM labels capture additional dimensions of information that content-only methods lack. We then question whether this is helpful or harmful for gun violence prevention.

Artificial Intelligence and Inclusion: Formerly Gang-Involved Youth as Domain Experts for Analyzing Unstructured Twitter Data.

Mining social media data for studying the human condition has created new and unique challenges. When analyzing social media data from marginalized communities, algorithms lack the ability to accurately interpret off-line context, which may lead to dangerous assumptions about and implications for marginalized communities. To combat this challenge, we hired formerly gang-involved young people as domain experts for contextualizing social media data in order to create inclusive, community-informed algorithms. Utilizing data from the Gang Intervention and Computer Science Project-a comprehensive analysis of Twitter data from gang-involved youth in Chicago-we describe the process of involving formerly gang-involved young people in developing a new part-of-speech tagger and content classifier for a prototype natural language processing system that detects aggression and loss in Twitter data. We argue that involving young people as domain experts leads to more robust understandings of context, including localized language, culture, and events. These insights could change how data scientists approach the development of corpora and algorithms that affect people in marginalized communities and who to involve in that process. We offer a contextually driven interdisciplinary approach between social work and data science that integrates domain insights into the training of qualitative annotators and the production of algorithms for positive social impact.

Humanizing Digital Mental Health through Social Media: Centering Experiences of Gang-Involved Youth Exposed to High Rates of Violence.

As the lives of young people expand further into digital spaces, our understandings of their expressions and language on social media become more consequential for providing individualized and applicable mental health resources. This holds true for young people exposed to high rates of community violence who may also lack access to health resources offline. Social media may provide insights into the impacts of community violence exposure on mental health. However, much of what is shared on social media contains localized language and context, which poses challenges regarding interpretation. In this perspective, I offer insights gained from the Beyond the Bullets: The Complexities and Ethical Challenges of Interpreting Social Media Posts workshop during the Digital Interventions in Mental Health conference in London, England: (1) social media as an underutilized environmental context in mental health services; (2) interpreting the meaning of social media posts is challenging, and there are additional challenges when users are exposed to offline violence and (3) the importance of having various perspectives when interpreting social media posts to build contextually nuanced and theoretically based understandings of digital social behavior.