Sentiment Analysis and Effect of COVID-19 Pandemic using College SubReddit Data

• URL: http://arxiv.org/abs/2112.04351v3
• Date: Mon, 18 Sep 2023 08:10:41 GMT
• Title: Sentiment Analysis and Effect of COVID-19 Pandemic using College SubReddit Data
• Authors: Tian Yan, Fang Liu
• Abstract summary: We investigate how the pandemic has influenced people's emotions and psychological states compared to a pre-pandemic period. We collected Reddit social media data from 2019 (pre-pandemic) and 2020 (pandemic) from the subreddits communities associated with eight universities. 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.
• Score: 3.5966786737142304
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• The Devil is in the Neurons: Interpreting and Mitigating Social Biases in Pre-trained Language Models [78.69526166193236]
  Pre-trained Language models (PLMs) have been acknowledged to contain harmful information, such as social biases. We propose sc Social Bias Neurons to accurately pinpoint units (i.e., neurons) in a language model that can be attributed to undesirable behavior, such as social bias. As measured by prior metrics from StereoSet, our model achieves a higher degree of fairness while maintaining language modeling ability with low cost.
  arXiv  Detail & Related papers  (2024-06-14T15:41:06Z)
• Do we listen to what we are told? An empirical study on human behaviour during the COVID-19 pandemic: neural networks vs. regression analysis [7.134828408572364]
  We study how compliant a general population is to mask-wearing-related public-health policy during the COVID-19 pandemic. We find that mask-wearing-related government regulations and public-transport announcements encouraged correct mask-wearing-behaviours.
  arXiv  Detail & Related papers  (2023-11-21T23:14:47Z)
• Decoding Susceptibility: Modeling Misbelief to Misinformation Through a Computational Approach [63.67533153887132]
  Susceptibility to misinformation describes the degree of belief in unverifiable claims that is not observable. Existing susceptibility studies heavily rely on self-reported beliefs. We propose a computational approach to model users' latent susceptibility levels.
  arXiv  Detail & Related papers  (2023-11-16T07:22:56Z)
• Has Sentiment Returned to the Pre-pandemic Level? A Sentiment Analysis Using U.S. College Subreddit Data from 2019 to 2022 [3.5966786737142304]
  This study aims to explore how people's emotions have changed from the pre-pandemic to post-emergency period. We collected Reddit data in 2019 (pre-pandemic), 2020 (peak pandemic), 2021, and 2022 (late stages of pandemic, transitioning period to post-emergency period) from subreddits in 128 universities/colleges in the U.S. Our study findings suggest a partial recovery in the sentiment composition in the post-pandemic-emergency era.
  arXiv  Detail & Related papers  (2023-09-16T02:57:30Z)
• Human Behavior in the Time of COVID-19: Learning from Big Data [71.26355067309193]
  Since March 2020, there have been over 600 million confirmed cases of COVID-19 and more than six million deaths. The pandemic has impacted and even changed human behavior in almost every aspect. Researchers have been employing big data techniques such as natural language processing, computer vision, audio signal processing, frequent pattern mining, and machine learning.
  arXiv  Detail & Related papers  (2023-03-23T17:19:26Z)
• Coronavirus statistics causes emotional bias: a social media text mining perspective [4.042350304426975]
  This paper proposes a deep learning model which classifies texts related to the pandemic from text data with place labels. Next, it conducts a sentiment analysis based on multi-task learning. Finally, it carries out a fixed-effect panel regression with outputs of the sentiment analysis.
  arXiv  Detail & Related papers  (2022-11-16T03:36:13Z)
• Bootstrapping Your Own Positive Sample: Contrastive Learning With Electronic Health Record Data [62.29031007761901]
  This paper proposes a novel contrastive regularized clinical classification model. We introduce two unique positive sampling strategies specifically tailored for EHR data. Our framework yields highly competitive experimental results in predicting the mortality risk on real-world COVID-19 EHR data.
  arXiv  Detail & Related papers  (2021-04-07T06:02:04Z)
• Temporal Positive-unlabeled Learning for Biomedical Hypothesis Generation via Risk Estimation [46.852387038668695]
  This paper aims to introduce the use of machine learning to the scientific process of hypothesis generation. We propose a variational inference model to estimate the positive prior, and incorporate it in the learning of node pair embeddings. Experiment results on real-world biomedical term relationship datasets and case study analyses on a COVID-19 dataset validate the effectiveness of the proposed model.
  arXiv  Detail & Related papers  (2020-10-05T10:58:03Z)
• On Analyzing Antisocial Behaviors Amid COVID-19 Pandemic [5.900114841365645]
  Despite the gravity of the issue, very few studies have studied online antisocial behaviors amid the COVID-19 pandemic. In this paper, we fill the research gap by collecting and annotating a large dataset of over 40 million COVID-19 related tweets. We also conduct an empirical analysis of our annotated dataset and found that new abusive lexicons are introduced amid the COVID-19 pandemic.
  arXiv  Detail & Related papers  (2020-07-21T11:11:35Z)
• Pandemic Pulse: Unraveling and Modeling Social Signals during the COVID-19 Pandemic [12.050597862123313]
  We present and begin to explore a collection of social data that represents part of the COVID-19 pandemic's effects on the United States. This data is collected from a range of sources and includes longitudinal trends of news topics, social distancing behaviors, community mobility changes, web searches, and more.
  arXiv  Detail & Related papers  (2020-06-10T17:55:44Z)
• Cross-lingual Transfer Learning for COVID-19 Outbreak Alignment [90.12602012910465]
  We train on Italy's early COVID-19 outbreak through Twitter and transfer to several other countries. Our experiments show strong results with up to 0.85 Spearman correlation in cross-country predictions.
  arXiv  Detail & Related papers  (2020-06-05T02:04:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.