Analysis of User Diversity-Based Patterns of Public Discourse on Twitter About Mental Health in the Context of Online Learning During COVID-19

Thakur, Nirmalya; Cho, Hazel; Cheng, Haokun; Lee, Hyunji

doi:10.1007/978-3-031-48044-7_27

Cited by 2 publications

(1 citation statement)

References 65 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Finally, location-prediction models have been gaining popularity [ 15 ], and researchers have recently used geographic online social networks to estimate population movement patterns [ 26 , 27 ], forecast economic activity [ 28 ], and monitor political events [ 29 ], public health [ 30 , 31 ], the spread of diseases [ 32 ], and conspiracy theories [ 33 ]. In this work, we use the location-prediction model in the context of fake news dissemination in capital markets.…”

Section: Introductionmentioning

confidence: 99%

The geography of corporate fake news

Darendeli,

Sun,

Tay

2024

PLoS ONE

View full text Add to dashboard Cite

Although a rich academic literature examines the use of fake news by foreign actors for political manipulation, there is limited research on potential foreign intervention in capital markets. To address this gap, we construct a comprehensive database of (negative) fake news regarding U.S. firms by scraping prominent fact-checking sites. We identify the accounts that spread the news on Twitter (now X) and use machine-learning techniques to infer the geographic locations of these fake news spreaders. Our analysis reveals that corporate fake news is more likely than corporate non-fake news to be spread by foreign accounts. At the country level, corporate fake news is more likely to originate from African and Middle Eastern countries and tends to increase during periods of high geopolitical tension. At the firm level, firms operating in uncertain information environments and strategic industries are more likely to be targeted by foreign accounts. Overall, our findings provide initial evidence of foreign-originating misinformation in capital markets and thus have important policy implications.

show abstract

Section: Introductionmentioning

confidence: 99%

The geography of corporate fake news

Darendeli,

Sun,

Tay

2024

PLoS ONE

View full text Add to dashboard Cite

show abstract

Investigation of the Global Fear Associated with COVID-19 Using Subjectivity Analysis and Deep Learning

Thakur,

Patel,

Poon

et al. 2024

Computation

Self Cite

View full text Add to dashboard Cite

The work presented in this paper makes multiple scientific contributions related to the investigation of the global fear associated with COVID-19 by performing a comprehensive analysis of a dataset comprising survey responses of participants from 40 countries. First, the results of subjectivity analysis performed using TextBlob, showed that in the responses where participants indicated their biggest concern related to COVID-19, the average subjectivity by the age group of 41–50 decreased from April 2020 to June 2020, the average subjectivity by the age group of 71–80 drastically increased from May 2020, and the age group of 11–20 indicated the least level of subjectivity between June 2020 to August 2020. Second, subjectivity analysis also revealed the percentage of highly opinionated, neutral opinionated, and least opinionated responses per age-group where the analyzed age groups were 11–20, 21–30, 31–40, 41–50, 51–60, 61–70, 71–80, and 81–90. For instance, the percentage of highly opinionated, neutral opinionated, and least opinionated responses by the age group of 11–20 were 17.92%, 16.24%, and 65.84%, respectively. Third, data analysis of responses from different age groups showed that the highest percentage of responses indicating that they were very worried about COVID-19 came from individuals in the age group of 21–30. Fourth, data analysis of the survey responses also revealed that in the context of taking precautions to prevent contracting COVID-19, the percentage of individuals in the age group of 31–40 taking precautions was higher as compared to the percentages of individuals from the age groups of 41–50, 51–60, 61–70, 71–80, and 81–90. Fifth, a deep learning model was developed to detect if the survey respondents were seeing or planning to see a psychologist or psychiatrist for any mental health issues related to COVID-19. The design of the deep learning model comprised 8 neurons for the input layer with the ReLU activation function, the ReLU activation function for all the hidden layers with 12 neurons each, and the sigmoid activation function for the output layer with 1 neuron. The model utilized the responses to multiple questions in the context of fear and preparedness related to COVID-19 from the dataset and achieved an accuracy of 91.62% after 500 epochs. Finally, two comparative studies with prior works in this field are presented to highlight the novelty and scientific contributions of this research work.

show abstract

Analysis of User Diversity-Based Patterns of Public Discourse on Twitter About Mental Health in the Context of Online Learning During COVID-19

Cited by 2 publications

References 65 publications

The geography of corporate fake news

The geography of corporate fake news

Investigation of the Global Fear Associated with COVID-19 Using Subjectivity Analysis and Deep Learning

Contact Info

Product

Resources

About