Twitter for disaster relief through sentiment analysis for COVID-19 and natural hazard crises

Behl, Shivam; Rao, Aman; Aggarwal, Sahil; Chadha, Sakshi; Pannu, Husanbir Singh

doi:10.1016/j.ijdrr.2021.102101

Cited by 59 publications

(39 citation statements)

References 17 publications

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“…With the emergence of computer science and sentiment analysis, several sentiment classification technologies have been developed, such as the lexicon-based approach, machine learning-based approach, and hybrid-based approach [ 47 ]. Behl et al [ 48 ] used supervised machine learning approaches and multi-class classification to analyse Twitter data on COVID-19 and found the performance of deep learning algorithms superior among the tested algorithms. Although mainstream research uses the machine learning-based method frequently, the method suffers from insufficient precision and unclear sentiments.…”

Section: Literature Reviewmentioning

confidence: 99%

Spatial evolution patterns of public panic on Chinese social networks amidst the COVID-19 pandemic

Yang

Zhang

et al. 2022

International Journal of Disaster Risk Reduction

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Novel coronavirus pneumonia has had a significant impact on people's lives and psychological health. We developed a stage model to analyse the spatial and temporal distribution of public panic during the two waves of the coronavirus disease 2019 (COVID-19) pandemic. We used tweets with geographic location data from the popular hashtag ‘Lockdown Diary’ recorded from 23 January to April 8, 2020, and ‘Nanjing Outbreak’ recorded from 21 July to 1 September 2021 on Weibo. Combining the lexicon-based sentiment analysis and the grounded theory approach, this panic model could explain people's panic and behavioural responses in different areas at different stages of the pandemic. Next, we used the latent Dirichlet allocation topic model to reconfirm the panic model. The results showed that public sentiments fluctuated strongly in the early stages; in this case, panic and prayers were the dominant sentiments. In terms of spatial distribution, public panic showed hierarchical and neighbourhood diffusion, with highly assertive expressions of sentiment at the outbreak sites, economically developed areas, and areas surrounding the outbreak. Most importantly, we considered that public panic was affected by the 17 specific topics extracted based on the perceived and actual distance of the pandemic, thus stimulating the process of panic from minimal, acute, and mild panic to perceived rationality. Consequently, the public's behavioural responses shifted from delayed, negative, and positive, to rational behavioural responses. This study presents a novel approach to explore public panic from both a time and space perspective and provides some suggestions in response to future pandemics.

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Section: Literature Reviewmentioning

confidence: 99%

Spatial evolution patterns of public panic on Chinese social networks amidst the COVID-19 pandemic

Yang

Zhang

et al. 2022

International Journal of Disaster Risk Reduction

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“…In Li Yang et al [98], the accuracy value of 83.5% scored for SLCABG. Shivam Behl et al [101] states that the accuracy of 82% for CNN -W and 87% for CNN -WP. Various Deep Learning Techniques are:…”

Section: Deep Learning Techniquesmentioning

confidence: 99%

“…Rinki Chatterjee [95],Rincy Jose [96], Md. MokhlesurRahman [100], Shivam Behl [101] Twitter Xingwei Yang [5], Lichen Chang [11],Kanthinee Katchapakiren [22], Md. Rafiqul Islma [24], Renata L. Rosa [34],Hay Mar Su Aung [74], Masum Billah [94], Rinki Chatterjee [95] Facebook Micheal M. Tadasse [16], Subhan Tariq [17], Hameed Jelodhar [54] Reddit Yuwen Lyu [13], Lixia Yu [69], Tianyi Wang [83] Weibo Li Chen Chang Youtube Badr Ait Hammou [15], Marija Stanojevic [81] Yelp Fazeel Abid [14] IMDB Anirban Mukherjee [73], Marija Stanojevic [81] Amazon…”

Section: Data Sourcementioning

confidence: 99%

Sentiment Analysis in Social Media Data for Depression Detection Using Artificial Intelligence: A Review

2021

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“…18,24 Indeed, the field of explainable AI was created largely to address this issue. For example, Behl et al 25 used explainable AI to investigate the potential limitations of an algorithm trained to process Twitter data to identify people's needs after a disaster. However, in general the ''explanations'' provided by explainable AI are dissonant from how human beings typically construct explanations.…”

Section: Potential Concerns Articulated By the Drm Communitymentioning

confidence: 99%

Fairness and accountability of AI in disaster risk management: Opportunities and challenges

et al. 2021

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Twitter for disaster relief through sentiment analysis for COVID-19 and natural hazard crises

Cited by 59 publications

References 17 publications

Spatial evolution patterns of public panic on Chinese social networks amidst the COVID-19 pandemic

Spatial evolution patterns of public panic on Chinese social networks amidst the COVID-19 pandemic

Sentiment Analysis in Social Media Data for Depression Detection Using Artificial Intelligence: A Review

Fairness and accountability of AI in disaster risk management: Opportunities and challenges

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