Exploring the Impact of COVID-19 on Social Life by Deep Learning
| Autor: | Isabel Segura-Bedmar, Jose Antonio Jijon-Vorbeck |
|---|---|
| Přispěvatelé: | Comunidad de Madrid, Universidad Carlos III de Madrid |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Computer science
Information technology Globalization Sentiment analysis Benchmark (surveying) multi-classification Pandemic Machine learning Long short-term memory Long Short-Term Memory Social media natural language processing Bert Lstm Informática business.industry Deep learning Natural language processing deep learning COVID-19 T58.5-58.64 Data science Variety (cybernetics) machine learning Action (philosophy) sentiment analysis Artificial intelligence LSTM business Covid-19 BERT Information Systems Multi-classification |
| Zdroj: | Information, Vol 12, Iss 459, p 459 (2021) Information Volume 12 Issue 11 e-Archivo. Repositorio Institucional de la Universidad Carlos III de Madrid instname |
| ISSN: | 2078-2489 |
| Popis: | Due to the globalisation of the COVID-19 pandemic, and the expansion of social media as the main source of information for many people, there have been a great variety of different reactions surrounding the topic. The World Health Organization (WHO) announced in December 2020 that they were currently fighting an “infodemic” in the same way as they were fighting the pandemic. An “infodemic” relates to the spread of information that is not controlled or filtered, and can have a negative impact on society. If not managed properly, an aggressive or negative tweet can be very harmful and misleading among its recipients. Therefore, authorities at WHO have called for action and asked the academic and scientific community to develop tools for managing the infodemic by the use of digital technologies and data science. The goal of this study is to develop and apply natural language processing models using deep learning to classify a collection of tweets that refer to the COVID-19 pandemic. Several simpler and widely used models are applied first and serve as a benchmark for deep learning methods, such as Long Short-Term Memory (LSTM) and Bidirectional Encoder Representations from Transformers (BERT). The results of the experiments show that the deep learning models outperform the traditional machine learning algorithms. The best approach is the BERT-based model. This work has been supported by the Madrid Government (Comunidad de Madrid) under the Multiannual Agreement with UC3M in the context of “Fostering Young Doctors Research” (NLP4RARE-CM-UC3M), as well as in the context of “Excellence of University Professors” (EPUC3M17) and in the context of the V PRICIT (Regional Programme of Research and Technological Innovation). |
| Databáze: | OpenAIRE |
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