Social and geographical disparities in Twitter use during Hurricane Harvey

Zou, Lei; Lam, Nina Siu‐Ngan; Shams, Soheil; Cai, Heng; Meyer, Michelle A.; Yang, Sen; Lee, Kisung; Park, Seung-Jong; Reams, Margaret A.

doi:10.1080/17538947.2018.1545878

Cited by 95 publications

(59 citation statements)

References 36 publications

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“…These results are consistent with previous studies of public responses in emergency situations. The public sentiment in Twitter was highly correlated with external factors, such as the impact from official mass media, important social events, extreme weather, and public holidays [29,30].…”

Section: Temporal Trendmentioning

confidence: 99%

Using Social Media to Mine and Analyze Public Opinion Related to COVID-19 in China

Han

Wang

Zhang

et al. 2020

IJERPH

319

250

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The outbreak of Corona Virus Disease 2019 (COVID-19) is a grave global public health emergency. Nowadays, social media has become the main channel through which the public can obtain information and express their opinions and feelings. This study explored public opinion in the early stages of COVID-19 in China by analyzing Sina-Weibo (a Twitter-like microblogging system in China) texts in terms of space, time, and content. Temporal changes within one-hour intervals and the spatial distribution of COVID-19-related Weibo texts were analyzed. Based on the latent Dirichlet allocation model and the random forest algorithm, a topic extraction and classification model was developed to hierarchically identify seven COVID-19-relevant topics and 13 sub-topics from Weibo texts. The results indicate that the number of Weibo texts varied over time for different topics and sub-topics corresponding with the different developmental stages of the event. The spatial distribution of COVID-19-relevant Weibo was mainly concentrated in Wuhan, Beijing-Tianjin-Hebei, the Yangtze River Delta, the Pearl River Delta, and the Chengdu-Chongqing urban agglomeration. There is a synchronization between frequent daily discussions on Weibo and the trend of the COVID-19 outbreak in the real world. Public response is very sensitive to the epidemic and significant social events, especially in urban agglomerations with convenient transportation and a large population. The timely dissemination and updating of epidemic-related information and the popularization of such information by the government can contribute to stabilizing public sentiments. However, the surge of public demand and the hysteresis of social support demonstrated that the allocation of medical resources was under enormous pressure in the early stage of the epidemic. It is suggested that the government should strengthen the response in terms of public opinion and epidemic prevention and exert control in key epidemic areas, urban agglomerations, and transboundary areas at the province level. In controlling the crisis, accurate response countermeasures should be formulated following public help demands. The findings can help government and emergency agencies to better understand the public opinion and sentiments towards COVID-19, to accelerate emergency responses, and to support post-disaster management.

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Section: Temporal Trendmentioning

confidence: 99%

Using Social Media to Mine and Analyze Public Opinion Related to COVID-19 in China

Han

Wang

Zhang

et al. 2020

IJERPH

319

250

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“…The main classification categories Table 9 that studies use for sentiment classification include negative, positive, and other. Studies that do not classify their contents by categories do so using a range of integers (Wang and Taylor 2018;Caragea et al 2014;Zou et al 2019).…”

Section: Sentiment Classificationmentioning

confidence: 99%

Situational awareness extraction: a comprehensive review of social media data classification during natural hazards

Vongkusolkit

Huang

2020

Annals of GIS

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Social media (e.g., Twitter and Facebook) can be regarded as vital sources of information during disasters to improve situational awareness (SA) and disaster management since they play a significant role in the rapid spread of information in the event of a disaster. Due to the volume of data is far beyond the capabilities of manual examination, existing works utilize natural language processing methods based on keywords, or classification models relying on features derived from text and other metadata (e.g., user profiles) to extract social media data contributing to SA and automatically categorize them into the relevant classes (e.g., damage and donation). However, the design of the classification schema and the associated information extraction methods are far less than straightforward and highly depend on: (1) the event type, (2) the study or analysis purpose, and (3) the social media platform used. To this end, this paper reviews the literature for extracting social media data and provides an overview of classification schemas that have been used to assess SA in events involving natural hazards from five different analytical perspectives (content, temporal, user, sentiment, and spatiotemporal) by discussing the prevalent topic categories, disaster event types, purpose of studies, and platforms utilized from each schema. Finally, this paper summarizes classification methods, and platforms that are most commonly used for each disaster event type, and outlines a research agenda with recommendations for future innovations.

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“…The advantages of social media with respect to the aforementioned sources of digital information are that they are extensive (covering large spatial areas), easily accessible, with less privacy concern, and at low cost [25][26][27][28]. Extracting useful information from social media is not new, as the valuable geospatial insights from social media have been explored in a wide range of fields, including hazard mitigation [29][30][31], evacuation monitoring [27,32,33], urban analytics [34][35][36][37], and public health [38,39], to list a few. Despite the existing applications, the potential of human mobility derived from social media data has not been fully explored.…”

Section: Introductionmentioning

confidence: 99%

Twitter reveals human mobility dynamics during the COVID-19 pandemic

et al. 2020

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The current COVID-19 pandemic raises concerns worldwide, leading to serious health, economic, and social challenges. The rapid spread of the virus at a global scale highlights the need for a more harmonized, less privacy-concerning, easily accessible approach to monitoring the human mobility that has proven to be associated with viral transmission. In this study, we analyzed over 580 million tweets worldwide to see how global collaborative efforts in reducing human mobility are reflected from the user-generated information at the global, country, and U.S. state scale. Considering the multifaceted nature of mobility, we propose two types of distance: the single-day distance and the cross-day distance. To quantify the responsiveness in certain geographic regions, we further propose a mobility-based responsive index (MRI) that captures the overall degree of mobility changes within a time window. The results suggest that mobility patterns obtained from Twitter data are amenable to quantitatively reflect the mobility dynamics. Globally, the proposed two distances had greatly deviated from their baselines after March 11, 2020, when WHO declared COVID-19 as a pandemic. The considerably less periodicity after the declaration suggests that the protection measures have obviously affected people’s travel routines. The country scale comparisons reveal the discrepancies in responsiveness, evidenced by the contrasting mobility patterns in different epidemic phases. We find that the triggers of mobility changes correspond well with the national announcements of mitigation measures, proving that Twitter-based mobility implies the effectiveness of those measures. In the U.S., the influence of the COVID-19 pandemic on mobility is distinct. However, the impacts vary substantially among states.

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Social and geographical disparities in Twitter use during Hurricane Harvey

Cited by 95 publications

References 36 publications

Using Social Media to Mine and Analyze Public Opinion Related to COVID-19 in China

Using Social Media to Mine and Analyze Public Opinion Related to COVID-19 in China

Situational awareness extraction: a comprehensive review of social media data classification during natural hazards

Twitter reveals human mobility dynamics during the COVID-19 pandemic

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