Social media offer a real-time, unfiltered view of how dis- asters affect communities. Crisis response, disaster mental health, and—more broadly—public health can benefit from automated analysis of the public's mental state as exhibited on social media. Our focus is on Twitter data from a com- munity that lost members in a mass shooting and another community—geographically removed from the shooting— that was indirectly exposed. We show that a common ap- proach for understanding emotional response in text: Lin- guistic Inquiry and Word Count (LIWC) can be substan- tially improved using machine learning. Starting with tweets flagged by LIWC as containing content related to the issue of death, we devise a categorization scheme for death-related tweets to induce automatic text classification of such con- tent. This improved methodology reveals striking differences in the magnitude and duration of increases in death-related talk between these communities. It also detects subtle shifts in the nature of death-related talk. Our results offer lessons for gauging public response and for developing interventions in the wake of a tragedy.
['Kimberly Glasgow', 'Clayton Fink', 'Jordan L. Boyd-Graber']
"Our Grief is Unspeakable": Automatically Measuring the Community Impact of a Tragedy