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The process of grieving is a misunderstood phenomenon and is further complicated when layered with complexities found at the intersections of adolescence and urban life in a networked public. Although many aspects of grieving are in fact private, there is often a desire to share the news of death with others. Researchers suggest that social media has reconfigured grieving due to social media platform features that allow for persistence, replicability, scalability, and searchability. In this study, we investigate expressions of grief—defined as the reaction to loss—on Twitter, among Chicago youth who include a group of young people who provide signals of gang knowledge, or affiliations on Twitter.
Methods: We use qualitative analysis and an error analysis of our natural language processing (NLP) system to examine tweets that followed 2 deaths. We analyzed 408 tweets form a seed user and individuals in their twitter network published between Saturday, March 29, and Thursday, April 17, 2014. First, we conduct a close textual analysis of tweets over a 19-day period during which 2 significant deaths occurred. Next, we show the results of a natural language computational system we developed to identify expressions of loss in our data set based on qualitative findings; our focus here is on how well we identify loss, presenting an error analysis of the output related to loss (eg, what types of tweets were correctly identified as loss and why).
Findings: Of the 408 tweets that coders examined, 112 were sent from our seed users account between their friend and their own death; 42 (38%) of these were coded as grief. The remaining 296 tweets were replies to or mentions of the seed user during the data collection period; of these, 232 (78%) were coded as loss, either the seed users or their own. Our NLP analysis indicates which features are most important. “RIP” is the feature that has the most impact by far. The second strongest feature was the word “free,” a term often used to respond to the loss of a friend or family member who is currently in jail or prison followed by the features “damn,” the emoji of praying hands, r.i.p., “rest” and “happen.”
Conclusions and Implications: The results of our study show that Twitter provides gang-involved youth with multiple ways in which to express and communicate about their response to loss. Computational studies of social media have overlooked the nuanced behaviors among social groups, especially marginalized populations such as youth living in vio- lent urban neighborhoods. We highlight the need for future research and designers of social media platforms to acknowledge and attend to the active and diverse demographics of social media users and to identify ways that these platforms can be used to support social work practice.