ArticleSocial science computer review2020
Artificial Intelligence and Inclusion: Formerly Gang-Involved Youth as Domain Experts for Analyzing Unstructured Twitter Data.
Article in Social science computer review, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed.
- "I Been Taking Adderall Mixing it With Lean, Hope I Don't Wake Up Out My Sleep": Harnessing Twitter to Understand Nonmedical Prescription Stimulant Use among Black Women and Men Subscribers.medRxiv : the preprint server for health sciences · 2024Article
- Patient Perspectives on AI-Driven Predictions of Schizophrenia Relapses: Understanding Concerns and Opportunities for Self-Care and Treatment.Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference · 2024Article
- Community implications for gun violence prevention during co-occurring pandemics; a qualitative and computational analysis study.Preventive medicine · 2022Article
- Equitable Research PRAXIS: A Framework for Health Informatics Methods.Yearbook of medical informatics · 2022Article
- Ethical Issues in Social Science Research Employing Big Data.Science and engineering ethics · 2022Article
- Social Service Workers' Use of Social Media to Obtain Client Information: Current Practices and Perspectives on a Potential Informatics Platform.Journal of social service research · 2022Article
- Gangbangin on the [Face]Book: Understanding Online Interactions of Chicago Latina/o Gangs.The Journal of research in crime and delinquency · 2021Article
- Contextual Analysis of Social Media: The Promise and Challenge of Eliciting Context in Social Media Posts with Natural Language Processing.Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society · 2020Article
- How We Do Things With Words: Analyzing Text as Social and Cultural Data.Frontiers in artificial intelligence · 2020Review
- Artificial Intelligence, Big Data, and mHealth: The Frontiers of the Prevention of Violence Against Children.Frontiers in artificial intelligence · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.