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![Architecture of the BERT model for natural language processing [9]](publication/362647037/figure/fig3/AS:11431281115271070@1674828435448/Architecture-of-the-BERT-model-for-natural-language-processing-9.png)
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![The CBOW and Skip-gram architecture of word2vec [29]](publication/362647037/figure/fig2/AS:11431281115293354@1674828435401/TheCBOW-and-Skip-gram-architecture-of-word2vec-29_Q320.jpg)
![Architecture of the BERT model for natural language processing [9]](publication/362647037/figure/fig3/AS:11431281115271070@1674828435448/Architecture-of-the-BERT-model-for-natural-language-processing-9_Q320.jpg)
Social microblogs are one of the popular platforms for information spreading. However,
with several advantages, these platforms are being used for spreading rumours. At
present, the majority of existing approaches identify rumours at the topic level instead
of at the tweet/post level. Moreover, prior studies used the sentiment and linguistic
featur...
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We study the dynamics of interactions between a traditional medium, the New York Times journal, and its followers in Twitter, using a massive dataset. It consists of the metadata of the articles published by the journal during the first year of the COVID-19 pandemic, and the posts published in Twitter by a large set of followers of the @nytimes acc...
Citations
Identification of infrastructure and human damage assessment tweets is beneficial to disaster management organizations as well as victims during a disaster. Most of the prior works focused on the detection of informative/situational tweets, and infrastructure damage, only one focused on human damage. This study presents a novel approach for detecting damage assessment tweets involving infrastructure and human damages. We investigated the potential of the BERT (Bidirectional Encoder Representations from Transformer) model to learn universal contextualized representations targeting to demonstrate its effectiveness for binary and multi-class classification of disaster damage assessment tweets. The objective is to exploit a pre-trained BERT as a transfer learning mechanism after fine-tuning important hyper-parameters on the CrisisMMD dataset containing seven disasters. The effectiveness of fine-tuned BERT is compared with five benchmarks and nine comparable models by conducting exhaustive experiments. The findings show that the fine-tuned BERT outperformed all benchmarks and comparable models and achieved state-of-the-art performance by demonstrating up to 95.12% macro-f1-score, and 88% macro-f1-score for binary and multi-class classification. Specifically, the improvement in the classification of human damage is promising.
