Accommodating Grief on Twitter: An Analysis of Expressions of Grief Among Gang Involved Youth on Twitter Using Qualitative Analysis and Natural Language Processing. (2nd April 2018)

• Record Type: Journal Article
• Authors: Patton, Desmond Upton; MacBeth, Jamie; Schoenebeck, Sarita; Shear, Katherine; McKeown, Kathleen
• Is Part Of: Biomedical informatics insights. Volume 2018:Number 10(2018)
• Publication Date: 2018-04-02
• Subjects: Social media -- grief -- gangs -- natural language processing; Medical informatics -- Periodicals; Medicine -- Periodicals; Medical Informatics; Medicine; Medical informatics; Medicine; Periodicals; Periodicals; 610.285
• DOI: 10.1177/1178222618763155
• Languages: English
• ISSNs: 1178-2226

Abstract

There is a dearth of research investigating youths' experience of grief and mourning after the death of close friends or family. Even less research has explored the question of how youth use social media sites to engage in the grieving process. This study employs qualitative analysis and natural language processing to examine tweets that follow 2 deaths. First, we conducted a close textual read on a sample of tweets by Gakirah Barnes, a gang-involved teenaged girl in Chicago, and members of her Twitter network, over a 19-day period in 2014 during which 2 significant deaths occurred: that of Raason "Lil B" Shaw and Gakirah's own death. We leverage the grief literature to understand the way Gakirah and her peers express thoughts, feelings, and behaviors at the time of these deaths. We also present and explain the rich and complex style of online communication among gang-involved youth, one that has been overlooked in prior research. Next, we overview the natural language processing output for expressions of loss and grief in our data set based on qualitative findings and present an error analysis on its output for grief. We conclude with a call for interdisciplinary research that analyzes online and offline behaviors to help understand physical and emotional violence and other problematic behaviors prevalent among marginalized communities.