Real-time social media sentiment analysis for rapid impact assessment of floods

Bryan-Smith, Lydia; Godsall, Jake; George, Franky; Egode, Kelly; Dethlefs, Nina; Parsons, Dan

doi:10.1016/j.cageo.2023.105405

Cited by 18 publications

(3 citation statements)

References 45 publications

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“…This category includes studies focusing on analyzing public sentiment and perceptions towards disasters or crisis situations. The research works in [19,[41][42][43][44][45] fall within this category, as shown in Table 5 (detailed in Appendix A, Table A2). Notably, models such as BERT demonstrate remarkable accuracy in classifying sentiments towards COVID-19 vaccines and reporting symptoms, leveraging contextual embeddings for nuanced understanding [19,42].…”

Section: Sentiment Analysis and Public Perceptionmentioning

confidence: 99%

“…BERT-based (e.g., RoBERTa, Word2Vec, LDA) [42,45] High accuracy in classifying sentiments and emotions, improving disaster response.…”

Section: Sentiment Analysis and Public Perceptionmentioning

confidence: 99%

“…This category represents research works utilizing both textual and visual data from social media for more comprehensive disaster analysis and response. The existing studies [45,[59][60][61][62][63] are grouped under this category, as demonstrated in Table 9. The detailed summary for papers in the "Multimodal Data Analysis" category is provided in Appendix A, Table A6.…”

Section: Multimodal Data Analysismentioning

confidence: 99%

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A Sustainable Way Forward: Systematic Review of Transformer Technology in Social-Media-Based Disaster Analytics

Sufi

2024

Sustainability

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Transformer technologies, like generative pre-trained transformers (GPTs) and bidirectional encoder representations from transformers (BERT) are increasingly utilized for understanding diverse social media content. Despite their popularity, there is a notable absence of a systematic literature review on their application in disaster analytics. This study investigates the utilization of transformer-based technology in analyzing social media data for disaster and emergency crisis events. Leveraging a systematic review methodology, 114 related works were collated from popular databases like Web of Science and Scopus. After deduplication and following the exclusion criteria, 53 scholarly articles were analyzed, revealing insights into the geographical distribution of research efforts, trends in publication output over time, publication venues, primary research domains, and prevalently used technology. The results show a significant increase in publications since 2020, with a predominant focus on computer science, followed by engineering and decision sciences. The results emphasize that within the realm of social-media-based disaster analytics, BERT was utilized in 29 papers, BERT-based methods were employed in 28 papers, and GPT-based approaches were featured in 4 papers, indicating their predominant usage in the field. Additionally, this study presents a novel classification scheme consisting of 10 distinct categories that thoroughly categorize all existing scholarly works on disaster monitoring. However, the study acknowledges limitations related to sycophantic behavior and hallucinations in GPT-based systems and raises ethical considerations and privacy concerns associated with the use of social media data. To address these issues, it proposes strategies for enhancing model robustness, refining data validation techniques, and integrating human oversight mechanisms.

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Section: Sentiment Analysis and Public Perceptionmentioning

confidence: 99%

“…BERT-based (e.g., RoBERTa, Word2Vec, LDA) [42,45] High accuracy in classifying sentiments and emotions, improving disaster response.…”

Section: Sentiment Analysis and Public Perceptionmentioning

confidence: 99%

Section: Multimodal Data Analysismentioning

confidence: 99%

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A Sustainable Way Forward: Systematic Review of Transformer Technology in Social-Media-Based Disaster Analytics

Sufi

2024

Sustainability

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Empowering flood forecasting through meteorological and social media data

Debata,

Elango

2024

Int. j. inf. tecnol.

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Flood of techniques and drought of theories: emotion mining in disasters

Shapouri,

Soleymani,

Rezayi

2024

J Comput Soc Sc

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Emotion mining has become a crucial tool for understanding human emotions during disasters, leveraging the extensive data generated on social media platforms. This paper aims to summarize existing research on emotion mining within disaster contexts, highlighting both significant discoveries and persistent issues. On the one hand, emotion mining techniques have achieved acceptable accuracy enabling applications such as rapid damage assessment and mental health surveillance. On the other hand, with many studies adopting data-driven approaches, several methodological issues remain. These include arbitrary emotion classification, ignoring biases inherent in data collection from social media, such as the overrepresentation of individuals from higher socioeconomic status on Twitter, and the lack of application of theoretical frameworks like cross-cultural comparisons. These problems can be summarized as a notable lack of theory-driven research and ignoring insights from social and behavioral sciences. This paper underscores the need for interdisciplinary collaboration between computer scientists and social scientists to develop more robust and theoretically grounded approaches in emotion mining. By addressing these gaps, we aim to enhance the effectiveness and reliability of emotion mining methodologies, ultimately contributing to improved disaster preparedness, response, and recovery.

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Real-time social media sentiment analysis for rapid impact assessment of floods

Cited by 18 publications

References 45 publications

A Sustainable Way Forward: Systematic Review of Transformer Technology in Social-Media-Based Disaster Analytics

A Sustainable Way Forward: Systematic Review of Transformer Technology in Social-Media-Based Disaster Analytics

Empowering flood forecasting through meteorological and social media data

Flood of techniques and drought of theories: emotion mining in disasters

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