Online social networks (OSNs) have become a powerful tool to study collective human responses to extreme events such as earthquakes. Most previous research concentrated on a single platform and utilized users' behaviors on a single platform to study people's general responses. In this study, we explore the characteristics of people's behaviors on different OSNs and conduct a cross-platform analysis of public responses to earthquakes. Our findings support the Uses and Gratification theory that users on Reddit and Twitter are engaging with platforms that they may feel best reflect their sense of self. Using the 2019 Ridgecrest earthquakes as our study cases, we collected 510,579 tweets and 45,770 Reddit posts (including 1437 submissions and 44,333 comments) to answer the following research questions: (1) What were the similarities and differences between public responses on Twitter and Reddit? (2) Considering the different mechanisms of Twitter and Reddit, what unique information of public responses can we learn from Reddit as compared with Twitter? By answering these research questions, we aim to bridge the gap of cross-platform public responses research towards natural hazards. Our study evinces that the users on the two different platforms have both different topics of interest and different sentiments towards the same earthquake, which indicates the necessity of investigating cross-platform OSNs to reveal a more comprehensive picture of people's general public responses towards certain disasters. Our analysis also finds that r/conspiracy subreddit is one of the major venues where people discuss the 2019 Ridgecrest earthquakes on Reddit and different misinformation/conspiracies spread on Twitter and Reddit platforms (e.g., "Big one is coming" on Twitter and "Nuclear test" on Reddit). 

https://www.nature.com/articles/s41598-022-05359-9.pdf

Cross-platform analysis of public responses to the 2019 Ridgecrest earthquake sequence on Twitter and Reddit

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. 

https://doi.org/10.1016/j.ijdrr.2021.102762

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

Mining social media text for disaster resource management using a feature selection based on forest optimization

Summary Disaster risk management (DRM) seeks to help societies prepare for, mitigate, or recover from the adverse impacts of disasters and climate change. Core to DRM are disaster risk models that rely heavily on geospatial data about the natural and built environments. Developers are increasingly turning to artificial intelligence (AI) to improve the quality of these models. Yet, there is still little understanding of how the extent of hidden geospatial biases affects disaster risk models and how accountability relationships are affected by these emerging actors and methods. In many cases, there is also a disconnect between the algorithm designers and the communities where the research is conducted or algorithms are implemented. This perspective highlights emerging concerns about the use of AI in DRM. We discuss potential concerns and illustrate what must be considered from a data science, ethical, and social perspective to ensure the responsible usage of AI in this field.

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

The rapid growth in information sharing on social media has defined a new information age in our society. Microblogging sites, such as Twitter, gained immense popularity during the COVID-19 pandemic. We developed an automated framework to extract the positive, negative, and neutral sentiments from tweets, and to further classify the tweets through machine-learning (ML) techniques. The developed framework can help to understand the sentiments in our society during profound events, such as the COVID-19 pandemic. Our framework is novel in that it is a hybrid framework that combines a lexicon-based technique for tweet sentiment analysis and labeling with supervised ML techniques for tweet classification. We have evaluated the hybrid framework with a range of measures, such as precision, accuracy, recall, and F1 score. Our results indicate that the majority of the sentiments are positive (38.5%) or neutral (34.7%). Furthermore, with an accuracy of 83%, the long short-term memory (LSTM) neural network has been selected as the preferred ML technique in the framework. The evaluation results indicate that our hybrid framework has the potential to automatically classify large tweet volumes, such as the tweets on COVID-19, according to the sentiments in the society. 

Automated Classification of Societal Sentiments on Twitter With Machine Learning

In emergencies and disasters, large numbers of people require basic needs and medical attention In such situations, online social media comes as a possible solution to aid the current disaster management methods In this paper, supervised learning approaches are compared for the multi-class classification of Twitter data A careful setting of Multilayer Perceptron (MLP) network layers and the optimizer has shown promising results for classification of tweets into three categories ie ‘resource needs’, ‘resource availability’, and ‘others’ being neutral and of no useful information Public data of Nepal Earthquake (2015) and Italy Earthquake (2016) have been used for training and validation of the models, and original COVID-19 data is acquired, annotated, and used for testing Detailed data analysis of tweets collected during different disasters has also been incorporated in the paper The proposed model has been able to achieve 83% classification accuracy on the original COVID-19 dataset Local Interpretable Model-Agnostic Explanations (LIME) is used to explain the behavior and shortcomings model on COVID-19 data This paper provides a simple choice for real-world applications and a good starting point for future research

Sahil Aggarwal

Papers

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