Detecting and Characterizing Trends in Online Mental Health Discussions

Chakravorti, Dante; Law, Kathleen; Gemmell, Jonathan; Raicu, Daniela

doi:10.1109/icdmw.2018.00107

Cited by 19 publications

(14 citation statements)

References 18 publications

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“…McRoy and colleagues [11] investigated ways to automate identification of the information needs of breast cancer survivors based on user-posts of online health forums. Chakravorti and colleagues [12] extracted topics based on various health issues discussed in online forums by evaluating user posts of several subreddits (e.g., r/Depression, r/Anxiety) from 2012 to 2018. VanDam and colleagues [13] presented a classification approach for identifying clinic-related posts in online health communities.…”

Section: Related Workmentioning

confidence: 99%

Deep Sentiment Classification and Topic Discovery on Novel Coronavirus or COVID-19 Online Discussions: NLP Using LSTM Recurrent Neural Network Approach

Jelodar

Wang

Orji

et al. 2020

IEEE J. Biomed. Health Inform.

301

166

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Internet forums and public social media, such as online healthcare forums, provide a convenient channel for users (people/patients) concerned about health issues to discuss and share information with each other. In late December 2019, an outbreak of a novel coronavirus (infection from which results in the disease named COVID-19) was reported, and, due to the rapid spread of the virus in other parts of the world, the World Health Organization declared a state of emergency. In this paper, we used automated extraction of COVID-19-related discussions from social media and a natural language process (NLP) method based on topic modeling to uncover various issues related to COVID-19 from public opinions. Moreover, we also investigate how to use LSTM recurrent neural network for sentiment classification of COVID-19 comments. Our findings shed light on the importance of using public opinions and suitable computational techniques to understand issues surrounding COVID-19 and to guide related decision-making. In addition, experiments demonstrated that the research model achieved an accuracy of 81.15%-a higher accuracy than that of several other well-known machine-learning algorithms for COVID-19-Sentiment Classification.

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Section: Related Workmentioning

confidence: 99%

Deep Sentiment Classification and Topic Discovery on Novel Coronavirus or COVID-19 Online Discussions: NLP Using LSTM Recurrent Neural Network Approach

Jelodar

Wang

Orji

et al. 2020

IEEE J. Biomed. Health Inform.

301

166

View full text Add to dashboard Cite

show abstract

“…Chakravorti [8] and colleagues evaluated user posts over several subreddits from 2012 to 2018 to extract health issue related topics discussed in online forums. VanDam [9] and colleagues adopted a classification approach for identifying clinic-related posts in online health communities.…”

Section: Related Workmentioning

confidence: 99%

“…McRoy[7] and colleagues investigated ways to automate the identification of breast cancer survivors’ information needs basing on online health forums. Chakravorti[8] and colleagues evaluated user posts over several subreddits from 2012 to 2018 to extract health issue related topics discussed in online forums. VanDam[9] and colleagues adopted a classification approach for identifying clinic-related posts in online health communities.…”

Section: Related Workmentioning

confidence: 99%

redBERT: A Topic Discovery and Deep Sentiment Classification Model on COVID-19 Online Discussions Using BERT NLP Model

Pandey

2021

Preprint

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A Natural Language Processing (NLP) method was used to uncover various issues and sentiments surrounding COVID-19 from social media and get a deeper understanding of fluctuating public opinion in situations of wide-scale panic to guide improved decision making with the help of a sentiment analyser created for the automated extraction of COVID-19 related discussions based on topic modelling. Moreover, the BERT model was used for the sentiment classification of COVID-19 Reddit comments. These findings shed light on the importance of studying trends and using computational techniques to assess human psyche in times of distress.

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“…A number of existing works have focused on characterizing patterns of discourse within these mental health communities on Reddit. These include studies that have analyzed longitudinal trends in topic usage and word choice (Chakravorti et al, 2018), the relationship between user participation styles and topic usage (Feldhege et al, 2020), and the discourse patterns specific to self-disclosure, social support, and anonymous posting (Pavalanathan and De Choudhury, 2015;De Choudhury and De, 2014).…”

Section: Studying Mental Health Via Social Mediamentioning

confidence: 99%

Quantifying the Effects of COVID-19 on Mental Health Support Forums

Biester¹,

Matton²,

Rajendran³

et al. 2020

Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020

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The COVID-19 pandemic, like many of the disease outbreaks that have preceded it, is likely to have a profound effect on mental health. Understanding its impact can inform strategies for mitigating negative consequences. In this work, we seek to better understand the effects of COVID-19 on mental health by examining discussions within mental health support communities on Reddit. First, we quantify the rate at which COVID-19 is discussed in each community, or subreddit, in order to understand levels of pandemic-related discussion. Next, we examine the volume of activity in order to determine whether the number of people discussing mental health has risen. Finally, we analyze how COVID-19 has influenced language use and topics of discussion within each subreddit.

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Detecting and Characterizing Trends in Online Mental Health Discussions

Cited by 19 publications

References 18 publications

Deep Sentiment Classification and Topic Discovery on Novel Coronavirus or COVID-19 Online Discussions: NLP Using LSTM Recurrent Neural Network Approach

Deep Sentiment Classification and Topic Discovery on Novel Coronavirus or COVID-19 Online Discussions: NLP Using LSTM Recurrent Neural Network Approach

redBERT: A Topic Discovery and Deep Sentiment Classification Model on COVID-19 Online Discussions Using BERT NLP Model

Quantifying the Effects of COVID-19 on Mental Health Support Forums

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