What Are We Depressed About When We Talk About COVID-19: Mental Health Analysis on Tweets Using Natural Language Processing

Li, Irene; Li, Yixin; Li, Tianxiao; Álvarez-Napagao, Sergio; Garcia-Gasulla, Darío; Suzumura, Toyotaro

doi:10.1007/978-3-030-63799-6_27

Cited by 47 publications

(34 citation statements)

References 9 publications

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“…However, to the best of our knowledge, they appear to use a significantly different methodology. Those works include Sentiment Analysis with Deep Learning Classifiers (Chakraborty et al, 2020;Li et al, 2020), a time-span of much fewer days (Abd-Alrazaq et al, 2020;Xue et al, 2020), and analyses of specific emotions without topic models (Lwin et al, 2020;Mathur et al, 2020). Because topic modeling is an exploratory, bottom-up, data-driven technique of data mining, we believe that a broader and more explorative approach, that takes into account multiple topic modeling solutions and a longer time span, may provide better insights on the themes discussed by Twitter users over time.…”

Section: Topicsmentioning

confidence: 99%

Covid-19 Discourse on Twitter: How the Topics, Sentiments, Subjectivity, and Figurative Frames Changed Over Time

Wicke

Bolognesi

2021

Front. Commun.

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The words we use to talk about the current epidemiological crisis on social media can inform us on how we are conceptualizing the pandemic and how we are reacting to its development. This paper provides an extensive explorative analysis of how the discourse about Covid-19 reported on Twitter changes through time, focusing on the first wave of this pandemic. Based on an extensive corpus of tweets (produced between 20th March and 1st July 2020) first we show how the topics associated with the development of the pandemic changed through time, using topic modeling. Second, we show how the sentiment polarity of the language used in the tweets changed from a relatively positive valence during the first lockdown, toward a more negative valence in correspondence with the reopening. Third we show how the average subjectivity of the tweets increased linearly and fourth, how the popular and frequently used figurative frame of WAR changed when real riots and fights entered the discourse.

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Section: Topicsmentioning

confidence: 99%

Covid-19 Discourse on Twitter: How the Topics, Sentiments, Subjectivity, and Figurative Frames Changed Over Time

Wicke

Bolognesi

2021

Front. Commun.

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“…Several researchers have applied the Valence Aware Dictionary and Sentiment Reasoner (VADER) tool [ 39 , 40 ], machine learning [ 41 ], and deep learning [ 42 ] to perform sentiment analysis on COVID-19–related tweets. Chandrasekaran et al [ 39 ] and Yin et al [ 40 ] employed the VADER tool to calculate the polarity of sentiment in COVID-19–related tweets posted in the first half of 2020.…”

Section: Discussionmentioning

confidence: 99%

Leveraging Transfer Learning to Analyze Opinions, Attitudes, and Behavioral Intentions Toward COVID-19 Vaccines: Social Media Content and Temporal Analysis

Liu¹,

Li²,

Liu³

2021

J Med Internet Res

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Background The COVID-19 vaccine is considered to be the most promising approach to alleviate the pandemic. However, in recent surveys, acceptance of the COVID-19 vaccine has been low. To design more effective outreach interventions, there is an urgent need to understand public perceptions of COVID-19 vaccines. Objective Our objective was to analyze the potential of leveraging transfer learning to detect tweets containing opinions, attitudes, and behavioral intentions toward COVID-19 vaccines, and to explore temporal trends as well as automatically extract topics across a large number of tweets. Methods We developed machine learning and transfer learning models to classify tweets, followed by temporal analysis and topic modeling on a dataset of COVID-19 vaccine–related tweets posted from November 1, 2020 to January 31, 2021. We used the F1 values as the primary outcome to compare the performance of machine learning and transfer learning models. The statistical values and P values from the Augmented Dickey-Fuller test were used to assess whether users’ perceptions changed over time. The main topics in tweets were extracted by latent Dirichlet allocation analysis. Results We collected 2,678,372 tweets related to COVID-19 vaccines from 841,978 unique users and annotated 5000 tweets. The F1 values of transfer learning models were 0.792 (95% CI 0.789-0.795), 0.578 (95% CI 0.572-0.584), and 0.614 (95% CI 0.606-0.622) for these three tasks, which significantly outperformed the machine learning models (logistic regression, random forest, and support vector machine). The prevalence of tweets containing attitudes and behavioral intentions varied significantly over time. Specifically, tweets containing positive behavioral intentions increased significantly in December 2020. In addition, we selected tweets in the following categories: positive attitudes, negative attitudes, positive behavioral intentions, and negative behavioral intentions. We then identified 10 main topics and relevant terms for each category. Conclusions Overall, we provided a method to automatically analyze the public understanding of COVID-19 vaccines from real-time data in social media, which can be used to tailor educational programs and other interventions to effectively promote the public acceptance of COVID-19 vaccines.

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“…Social media reflects public attitudes during the pandemic (Chen et al, 2020). Existing work on sentiment or emotion analysis has considered Twitter (Drias and Drias, 2020;Nemes and Kiss, 2020;Li et al, 2020a;Samuel et al, 2020), Reddit (Biester et al, 2020, Weibo (Li et al, 2020b), and other platforms (Kleinberg et al, 2020). For example, Biester et al (2020) analyzed how the pandemic has influenced the online behavior of Reddit users and found an increase in posts expressing mental health concerns, including anxiety and concerns for health and family.…”

Section: Related Workmentioning

confidence: 99%

Quantifying the Effects of COVID-19 on Restaurant Reviews

Cao¹,

Liu²,

Karamanolakis³

et al. 2021

Proceedings of the Ninth International Workshop on Natural Language Processing for Social Media

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The COVID-19 pandemic has implications beyond physical health, affecting society and economies. Government efforts to slow down the spread of the virus have had a severe impact on many businesses, including restaurants. Mandatory policies such as restaurant closures, bans on social gatherings, and social distancing restrictions have affected restaurant operations as well as customer preferences (e.g., prompting a demand for stricter hygiene standards). As of now, however, it is not clear how and to what extent the pandemic has affected restaurant reviews, an analysis of which could potentially inform policies for addressing this ongoing situation.

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What Are We Depressed About When We Talk About COVID-19: Mental Health Analysis on Tweets Using Natural Language Processing

Cited by 47 publications

References 9 publications

Covid-19 Discourse on Twitter: How the Topics, Sentiments, Subjectivity, and Figurative Frames Changed Over Time

Covid-19 Discourse on Twitter: How the Topics, Sentiments, Subjectivity, and Figurative Frames Changed Over Time

Leveraging Transfer Learning to Analyze Opinions, Attitudes, and Behavioral Intentions Toward COVID-19 Vaccines: Social Media Content and Temporal Analysis

Quantifying the Effects of COVID-19 on Restaurant Reviews

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