Sentiment Analysis of Lockdown in India During COVID-19: A Case Study on Twitter

Detecting COVID-19 vaccine hesitancy in India: a multimodal transformer based approach

COVID-19 has emerged as the greatest threat in recent times, causing extensive mortality and morbidity in the entire world. India is among the highly affected countries suffering severe disruptions due this pandemic. To overcome the adverse effects of COVID-19, vaccination has been identified as the most effective preventive measure globally. However, a growing amount of hesitancy has been observed among the general public regarding the efficacy and possible side-effects of vaccination. Such hesitancy may proved to be the greatest hindrance towards combating this deadly pandemic. This paper introduces a multimodal deep learning method for Indian Twitter user classification, leveraging both content-based and network-based features. To explore the fundamental features of different modalities, improvisations of transformer models, BERT and GraphBERT are utilized to encode the textual and network structure information. The proposed approach thus integrates multiple data representations, utilizing the advances in both transformer based deep learning as well as multimodal learning. Experimental results demonstrates the efficacy of proposed approach over state of the art approaches. Aggregated feature representations from multiple modalities embed additional information that improves the classification results. The findings of the proposed model has been further utilized to perform a study on the dynamics of COVID-19 vaccine hesitancy in India.

The COVID-19 Infodemic on Twitter: A Space and Time Topic Analysis of the Brazilian Immunization Program and Public Trust

The context of the COVID-19 pandemic has brought to light the infodemic phenomenon and the problem of misinformation. Agencies involved in managing COVID-19 immunization programs are also looking for ways to combat this problem, demanding analytical tools specialized in identifying patterns of misinformation and understanding how they have evolved in time and space to demonstrate their effects on public trust. The aim of this article is to present the results of a study applying topic analysis in space and time with respect to public opinion on the Brazilian COVID-19 immunization program. The analytical process involves applying topic discovery to tweets with geoinformation extracted from the COVID-19 vaccination theme. After extracting the topics, they were submitted to manual annotation, whereby the polarity labels pro, anti, and neutral were applied based on the support and trust in the COVID-19 vaccination. A space and time analysis was carried out using the topic and polarity distributions, making it possible to understand moments during which the most significant quantities of posts occurred and the cities that generated the most tweets. The analytical process describes a framework capable of meeting the needs of agencies for tools, providing indications of how misinformation has evolved and where its dissemination focuses, in addition to defining the granularity of this information according to what managers define as adequate. The following research outcomes can be highlighted. (1) We identified a specific date containing a peak that stands out among the other dates, indicating an event that mobilized public opinion about COVID-19 vaccination. (2) We extracted 23 topics, enabling the manual polarity annotation of each topic and an understanding of which polarities were associated with tweets. (3) Based on the association between polarities, topics, and tweets, it was possible to identify the Brazilian cities that produced the majority of tweets for each polarity and the amount distribution of tweets relative to cities populations.

Enhanced sentiment analysis regarding COVID-19 news from global channels

For a healthy society to exist, it is crucial for the media to focus on disease-related issues so that more people are widely aware of them and reduce health risks. Recently, deep neural networks have become a popular tool for textual sentiment analysis, which can provide valuable insights and real-time monitoring and analysis regarding health issues. In this paper, as part of an effort to develop an effective model that can elicit public sentiment on COVID-19 news, we propose a novel approach Cov-Att-BiLSTM for sentiment analysis of COVID-19 news headlines using deep neural networks. We integrate attention mechanisms, embedding techniques, and semantic level data labeling into the prediction process to enhance the accuracy. To evaluate the proposed approach, we compared it to several deep and machine learning classifiers using various metrics of categorization efficiency and prediction quality, and the experimental results demonstrate its superiority with 0.931 testing accuracy. Furthermore, 73,138 pandemic-related tweets posted on six global channels were analyzed by the proposed approach, which accurately reflects global coverage of COVID-19 news and vaccination.

Public Health Policy Monitoring through Public Perceptions: A Case of COVID-19 Tweet Analysis

Since the start of the COVID-19 pandemic, government authorities have responded by issuing new public health policies, many of which were intended to contain its spread but ended up limiting economic and social activities. The citizen responses to these policies are diverse, ranging from goodwill to fear and anger. It is challenging to determine whether or not these public health policies achieved the intended impact. This requires systematic data collection and scientific studies, which can be very time-consuming. To overcome such challenges, in this paper, we provide an alternative approach to continuously monitor and dynamically make sense of how public health policies impact citizens. Our approach is to continuously collect Twitter posts related to COVID-19 policies and to analyze the public reactions. We have developed a web-based system that collects tweets daily and generates timelines and geographical displays of citizens’ “concern levels”. Tracking the public reactions towards different policies can help government officials assess the policy impacts in a more dynamic and real-time manner. For this paper, we collected and analyzed over 16 million tweets related to ten policies over a 10-month period. We obtained several findings; for example, the “COVID-19 (General)” and ”Ventilators” policies engendered the highest concern levels, while the “Face Coverings” policy caused the lowest. Nine out of ten policies exhibited significant changes in concern levels during the observation period.

COVID-19 sentiment analysis using college subreddit data

BACKGROUND

The COVID-19 pandemic has affected our society and human well-being in various ways. In this study, we investigate how the pandemic has influenced people's emotions and psychological states compared to a pre-pandemic period using real-world data from social media.

METHOD

We collected Reddit social media data from 2019 (pre-pandemic) and 2020 (pandemic) from the subreddits communities associated with eight universities. We applied the pre-trained Robustly Optimized BERT pre-training approach (RoBERTa) to learn text embedding from the Reddit messages, and leveraged the relational information among posted messages to train a graph attention network (GAT) for sentiment classification. Finally, we applied model stacking to combine the prediction probabilities from RoBERTa and GAT to yield the final classification on sentiment. With the model-predicted sentiment labels on the collected data, we used a generalized linear mixed-effects model to estimate the effects of pandemic and in-person teaching during the pandemic on sentiment.

RESULTS

The results suggest that the odds of negative sentiments in 2020 (pandemic) were 25.7% higher than the odds in 2019 (pre-pandemic) with a p-value < 0.001; and the odds of negative sentiments associated in-person learning were 48.3% higher than with remote learning in 2020 with a p-value of 0.029.

CONCLUSIONS

Our study results are consistent with the findings in the literature on the negative impacts of the pandemic on people's emotions and psychological states. Our study contributes to the growing real-world evidence on the various negative impacts of the pandemic on our society; it also provides a good example of using both ML techniques and statistical modeling and inference to make better use of real-world data.

Understanding Entertainment Trends during COVID-19 in Saudi Arabia

Studying people’s opinions is a growing research field. This area of research is known as sentiment analysis. The COVID-19 pandemic changed everything around the world and reduced social contact among people. Citizens and residents of Saudi Arabia experienced high stress during the pandemic, seeking entertainment via games and publishing their activities on social media platforms such as Twitter. In this paper, we focus on applying the Mazajak sentiment analyzer on tweets containing game keywords in Arabic collected using Twitter API during the lockdown period to decide whether users preferred playing individually or in groups. This can help designers and developers, as well as the Saudi General Entertainment Authority (GEA), focus on creating the most interesting games for individuals and improving them to meet users’ expectations. Our approach has three main stages: tweet collection, tweet preparation, and finally, the application of sentiment analysis to get the desired goal based on people’s behavior toward the games. The result of this paper confirms that people, in general, preferred playing in groups, instead of alone, during this period.