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- W2182274767 abstract "Social media offer a real-time, unfiltered view of how disasters 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 community that lost members in a mass shooting and another community—geographically removed from the shooting — that was indirectly exposed. We show that a common approach for understanding emotional response in text: Linguistic Inquiry and Word Count (LIWC) can be substantially 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 content. 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." @default.
- W2182274767 created "2016-06-24" @default.
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- W2182274767 date "2014-05-16" @default.
- W2182274767 modified "2023-10-09" @default.
- W2182274767 title "Our Grief is Unspeakable'': Automatically Measuring the Community Impact of a Tragedy" @default.
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- W2182274767 doi "https://doi.org/10.1609/icwsm.v8i1.14535" @default.
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