Exploring the Non-Medical impacts of Covid-19 using Natural Language Processing

Agade, Amol; Balpande, Samta

doi:10.20944/preprints202011.0056.v1

Cited by 4 publications

(3 citation statements)

References 41 publications

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“…The model's accuracy was investigated based on the consistency and perplexity score, using LDA algorithms 0.51 and −10.90. Both the algorithm LDA and NMF found common issues in many areas of industry that were affected by the COVID-19 epidemic [ 33 ].…”

Section: Literature Reviewmentioning

confidence: 99%

A Natural Language Processing (NLP) Evaluation on COVID-19 Rumour Dataset Using Deep Learning Techniques

Fatima

Shaikh²,

Riaz

et al. 2022

Computational Intelligence and Neuroscience

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Context and Background: Since December 2019, the coronavirus (COVID-19) epidemic has sparked considerable alarm among the general community and significantly affected societal attitudes and perceptions. Apart from the disease itself, many people suffer from anxiety and depression due to the disease and the present threat of an outbreak. Due to the fast propagation of the virus and misleading/fake information, the issues of public discourse alter, resulting in significant confusion in certain places. Rumours are unproven facts or stories that propagate and promote sentiments of prejudice, hatred, and fear. Objective. The study’s objective is to propose a novel solution to detect fake news using state-of-the-art machines and deep learning models. Furthermore, to analyse which models outperformed in detecting the fake news. Method. In the research study, we adapted a COVID-19 rumours dataset, which incorporates rumours from news websites and tweets, together with information about the rumours. It is important to analyse data utilizing Natural Language Processing (NLP) and Deep Learning (DL) approaches. Based on the accuracy, precision, recall, and the f1 score, we can assess the effectiveness of the ML and DL algorithms. Results. The data adopted from the source (mentioned in the paper) have collected 9200 comments from Google and 34,779 Twitter postings filtered for phrases connected with COVID-19-related fake news. Experiment 1. The dataset was assessed using the following three criteria: veracity, stance, and sentiment. In these terms, we have different labels, and we have applied the DL algorithms separately to each term. We have used different models in the experiment such as (i) LSTM and (ii) Temporal Convolution Networks (TCN). The TCN model has more performance on each measurement parameter in the evaluated results. So, we have used the TCN model for the practical implication for better findings. Experiment 2. In the second experiment, we have used different state-of-the-art deep learning models and algorithms such as (i) Simple RNN; (ii) LSTM + Word Embedding; (iii) Bidirectional + Word Embedding; (iv) LSTM + CNN-1D; and (v) BERT. Furthermore, we have evaluated the performance of these models on all three datasets, e.g., veracity, stance, and sentiment. Based on our second experimental evaluation, the BERT has a superior performance over the other models compared.

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Section: Literature Reviewmentioning

confidence: 99%

A Natural Language Processing (NLP) Evaluation on COVID-19 Rumour Dataset Using Deep Learning Techniques

Fatima

Shaikh²,

Riaz

et al. 2022

Computational Intelligence and Neuroscience

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“…Although a large number of articles have been substantially refined using the summarization technique, some general information in the articles that affects the clustering results may have been lost. Agade and Balpande [78] used the same dataset as ours, but for a shorter period, from January 2020 to May 2020, to explore the non-medical aspects of COVID-19 as reported in the news. This study was limited to static topics and lacked a discussion of the economic implications of the findings.…”

Section: Related Workmentioning

confidence: 99%

Agenda-Setting for COVID-19: A Study of Large-Scale Economic News Coverage Using Natural Language Processing

Businger

Dollfus

et al. 2022

Int J Data Sci Anal

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Over the past two years, organizations and businesses have been forced to constantly adapt and develop effective responses to the challenges of the COVID-19 pandemic. The acuteness, global scale and intense dynamism of the situation make online news and information even more important for making informed management and policy decisions. This paper focuses on the economic impact of the COVID-19 pandemic, using natural language processing (NLP) techniques to examine the news media as the main source of information and agenda-setters of public discourse over an eight-month period. The aim of this study is to understand which economic topics news media focused on alongside the dominant health coverage, which topics did not surface, and how these topics influenced each other and evolved over time and space. To this end, we used an extensive open-source dataset of over 350,000 media articles on non-medical aspects of COVID-19 retrieved from over 60 top-tier business blogs and news sites. We referred to the World Economic Forum’s Strategic Intelligence taxonomy to categorize the articles into a variety of topics. In doing so, we found that in the early days of COVID-19, the news media focused predominantly on reporting new cases, which tended to overshadow other topics, such as the economic impact of the virus. Different independent news sources reported on the same topics, showing a herd behavior of the news media during this global health crisis. However, a temporal analysis of news distribution in relation to its geographic focus showed that the rise in COVID-19 cases was associated with an increase in media coverage of relevant socio-economic topics. This research helps prepare for the prevention of social and economic crises when decision-makers closely monitor news coverage of viruses and related topics in other parts of the world. Thus, monitoring the news landscape on a global scale can support decision-making in social and economic crises. Our analyses point to ways in which this monitoring and issues management can be improved to remain alert to social dynamics and market changes.

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“…Biswas et al [22] used both news and social media data related to COVID-19 and predicted the Indian stock market. Finally, in terms of research that used topic modeling, Agade and Balpande [23] explored the impact of COVID-19 on industries such as business and finance using LDA and nonnegative matrix factorization. The research highlighted the vast impact of COVID-19 and the consequent damage to many industries, as well as to the business and finance sector.…”

Section: Literature Reviewmentioning

confidence: 99%

Relation Between News Topics and Variations in Pharmaceutical Indices During COVID-19 Using a Generalized Dirichlet-Multinomial Regression (g-DMR) Model

2021

KSII TIIS

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Owing to the unprecedented COVID-19 pandemic, the pharmaceutical industry has attracted considerable attention, spurred by the widespread expectation of vaccine development. In this study, we collect relevant topics from news articles related to COVID-19 and explore their links with two South Korean pharmaceutical indices, the Drug and Medicine index of the Korea Composite Stock Price Index (KOSPI) and the Korean Securities Dealers Automated Quotations (KOSDAQ) Pharmaceutical index. We use generalized Dirichlet-multinomial regression (g-DMR) to reveal the dynamic topic distributions over metadata of index values. The results of our analysis, obtained using g-DMR, reveal that a greater focus on specific news topics has a significant relationship with fluctuations in the indices. We also provide practical and theoretical implications based on this analysis.

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Exploring the Non-Medical impacts of Covid-19 using Natural Language Processing

Cited by 4 publications

References 41 publications

A Natural Language Processing (NLP) Evaluation on COVID-19 Rumour Dataset Using Deep Learning Techniques

A Natural Language Processing (NLP) Evaluation on COVID-19 Rumour Dataset Using Deep Learning Techniques

Agenda-Setting for COVID-19: A Study of Large-Scale Economic News Coverage Using Natural Language Processing

Relation Between News Topics and Variations in Pharmaceutical Indices During COVID-19 Using a Generalized Dirichlet-Multinomial Regression (g-DMR) Model

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