Summary of Quakebert: Accurate Classification Of Social Media Texts For Rapid Earthquake Impact Assessment, by Jin Han et al.
QuakeBERT: Accurate Classification of Social Media Texts for Rapid Earthquake Impact Assessment
by Jin Han, Zhe Zheng, Xin-Zheng Lu, Ke-Yin Chen, Jia-Rui Lin
First submitted to arxiv on: 6 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG); Social and Information Networks (cs.SI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This study proposes a domain-specific large language model (LLM) called QuakeBERT for rapid earthquake impact assessment. The LLM is fine-tuned for accurate classification and filtering of microblogs considering their relationship to physical and social impacts of earthquakes. An integrated method combining public opinion trend analysis, sentiment analysis, and keyword-based physical impact quantification assesses both physical and social impacts based on social media texts. The study uses a dataset comprising 7282 earthquake-related microblogs from twenty earthquakes in different locations. Experimental results show that data diversity and volume dominate QuakeBERT’s performance, increasing the macro average F1 score by 27%. The proposed approach outperforms CNN- or RNN-based models by improving the macro average F1 score from 60.87% to 84.33%. Finally, the study demonstrates the effectiveness of the proposed approach in enhancing impact assessment processes and enabling effective post-disaster emergency responses. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how social media can help with disaster response after earthquakes. Right now, it’s hard to know what’s real and what’s not on social media, which makes it difficult to make good decisions for cities that are trying to be more resilient. To solve this problem, the researchers created a special type of artificial intelligence called QuakeBERT that can quickly look at lots of social media posts and figure out what’s important and what’s not. They also developed a way to use public opinion trends, how people feel about things, and information about physical damage to get a better understanding of the impact of an earthquake. The researchers tested their approach using data from 20 different earthquakes and found that it worked really well. |
Keywords
» Artificial intelligence » Classification » Cnn » F1 score » Large language model » Rnn
