SN voice and text analysis as a tool for disaster effects estimation &amp;#x2014; A preliminary exploration

Teodorescu, Horia-Nicolai

doi:10.1109/sped.2013.6682650

Cited by 16 publications

(1 citation statement)

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“…They found that in the aftermath of the incident, keywords related to emotional reactions (such as anger and suicide) were prevalent on Twitter. Reference [ 30 ] proposed a disaster assessment model by correlating emotional data in voice and text communications with population density and disaster severity. Reference [ 31 ] found that during the 2007 Virginia Tech shooting, anger was the most frequently expressed emotion on Twitter, followed by fright, sadness, and anxiety.…”

Section: Literature Reviewmentioning

confidence: 99%

Tracking and Analyzing Public Emotion Evolutions During COVID-19: A Case Study from the Event-Driven Perspective on Microblogs

Wei

Dang

et al. 2020

IJERPH

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Objective: Coronavirus disease 2019 (COVID-19) has caused substantial panic worldwide since its outbreak in December 2019. This study uses social networks to track the evolution of public emotion during COVID-19 in China and analyzes the root causes of these public emotions from an event-driven perspective. Methods: A dataset was constructed using microblogs (n = 125,672) labeled with COVID-19-related super topics (n = 680) from 40,891 users from 1 December 2019 to 17 February 2020. Based on the skeleton and key change points of COVID-19 extracted from microblogging contents, we tracked the public’s emotional evolution modes (accumulated emotions, emotion covariances, and emotion transitions) by time phase and further extracted the details of dominant social events. Results: Public emotions showed different evolution modes during different phases of COVID-19. Events about the development of COVID-19 remained hot, but generally declined, and public attention shifted to other aspects of the epidemic (e.g., encouragement, support, and treatment). Conclusions: These findings suggest that the public’s feedback on COVID-19 predated official accounts on the microblog platform. There were clear differences in the trending events that large users (users with many fans and readings) and common users paid attention to during each phase of COVID-19.

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Section: Literature Reviewmentioning

confidence: 99%