Monitoring Indonesian online news for COVID-19 event detection using deep learning

Even though coronavirus disease 2019 (COVID-19) vaccination has been done, preparedness for the possibility of the next outbreak wave is still needed with new mutations and virus variants. A near real-time surveillance system is required to provide the stakeholders, especially the public, to act in a timely response. Due to the hierarchical structure, epidemic reporting is usually slow particularly when passing jurisdictional borders. This condition could lead to time gaps for public awareness of new and emerging events of infectious diseases. Online news is a potential source for COVID-19 monitoring because it reports almost every infectious disease incident globally. However, the news does not report only about COVID-19 events, but also various information related to COVID-19 topics such as the economic impact, health tips, and others. We developed a framework for online news monitoring and applied sentence classification for news titles using deep learning to distinguish between COVID-19 events and non-event news. The classification results showed that the fine-tuned bidirectional encoder representations from transformers (BERT) trained with Bahasa Indonesia achieved the highest performance (accuracy: 95.16%, precision: 94.71%, recall: 94.32%, F1-score: 94.51%). Interestingly, our framework was able to identify news that reports the new COVID strain from the United Kingdom (UK) as an event news, 13 days before the Indonesian officials closed the border for foreigners. © 2023 Institute of Advanced Engineering and Science. All rights reserved.

Developing COVID-19 Information validation system Using Natural language inference

In the recent COVID-19 pandemic outbreak, a lot of information about the disease spread without being able to validate its truth. In this work, we contribute to develop a COVID-19 information validation system, the truth of which refers to scientific articles. The system consists of two main modules, namely the fact-finding module and the sentence comparison module. The fact-finding module looks for relevant facts to the validated information, while the sentence comparison module leverages Natural Language Inference (NLI) task by using deep learning to compare validated information and facts relevant to that information. This work focuses on building the NLI model, with RoBERTa-Large trained on SNLI and MultiNLI dataset. The model was evaluated on SNLI dev and test set and Multi NLI dev set (matched and mismatched), with median accuracy of 0.931, 0.925, 0.9031, and 0.9042, respectively. The model was also evaluated on stress test for NLI with an accuracy of 0.7466 on the competence test, 0.8776 on the noise test, and 0.6884 on the distraction test. We also created our own NLI dataset for further evaluation, which is created based on rumors and circulating information about COVID-19 and facts from the CORD-19 dataset, containing 100 sentence pairs of premise and hypothesis. Model evaluation on this dataset resulting in an accuracy of 0.84, which means the model already works well on sentences from the CORD-19 dataset. © 2022 IEEE.

Classifying aggravation status of COVID-19 event from short-text using CNN

COVID-19 pandemic is a new precedent that has changed many aspects of human life. With the uncertainty of vaccine availability, stakeholders are required to track the dynamics of COVID-19 events to prepare the necessary response. One sub-task in tracking the dynamics of an event is to identify the aggravation status of the event (i.e., whether an event is worsening or getting better). We experimented with convolutional neural network (CNN) models to classify the status of COVID-19 aggravation status from a short text. CNN without one hot encoding prevailed. Furthermore, we conduct tuning to achieve better performance of CNN. The highest performance was achieved by tuning some of the configuration parameters. As the final result, the model performed at best (accuracy = 87.585% and F1-score = 76%) when using 80 nodes, SGD optimizer, lr = 0.1, and momentum = 0.9.

Comparative Study of Covid-19 Tweets Sentiment Classification Methods

Covid-19 is a disease caused by a virus and has become a pandemic in many countries around the world. The disease not only affects public health, but also affects other aspects of life. People tend to write comments about things happening during the pandemic on social media, one of which is Twitter. Sentiment analysis on Twitter data is not an easy task due to the characteristics of the tweeter text which is user generated content. Therefore, in this paper, a sentiment analysis study is carried out on Twitter data using three schemes, namely the vector space model (Bag of Words and TF-IDF) with Support Vector Machine, word embedding (word2vec and Glove) with Long Short-Term Memory, and BERT (Bidirectional Encoder Representations from Transformers). Based on the conducted experiments, BERT achieved the best performance compared to the other two schemes, reaching 0.85 (weighted F1-score) and 0.83 (macro F1-score) for the classification of three sentiment classes on Kaggle competition data (Coronavirus tweets NLP-Text Classification). © 2021 IEEE.