ArticleInformation processing & management2022
Optimism and pessimism analysis using deep learning on COVID-19 related twitter conversations.
Article in Information processing & management, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Increasing Women's Knowledge about HPV Using BERT Text Summarization: An Online Randomized Study.International journal of environmental research and public health · 2022Trial
- An Arabic language benchmark and hybrid BERT-BiGRU-CapsNet model for optimism and pessimism detection.Scientific reports · 2026Article
- A bimodal longitudinal investigation on changes in sentiments over social media interactions owing to COVID-19 pandemic.Scientific reports · 2025Article
- The Impact of Stay-At-Home Mandates on Uncertainty and Sentiments: Quasi-Experimental Study.Journal of medical Internet research · 2025Article
- The effect of fear and situational motivation on online information avoidance: The case of COVID-19.International journal of information management · 2023Article
- Exploring public sentiment and vaccination uptake of COVID-19 vaccines in England: a spatiotemporal and sociodemographic analysis of Twitter data.Frontiers in public health · 2023Article
- Investigating the COVID-19 vaccine discussions on Twitter through a multilayer network-based approach.Information processing & management · 2022Article
- An easy numeric data augmentation method for early-stage COVID-19 tweets exploration of participatory dynamics of public attention and news coverage.Information processing & management · 2022Article
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This paper proposes a new deep learning approach to better understand how optimistic and pessimistic feelings are conveyed in Twitter conversations about COVID-19. A pre-trained transformer embedding is used to extract the semantic features and several network architectures are compared. Model performance is evaluated on two new, publicly available Twitter corpora of crisis-related posts. The best performing pessimism and optimism detection models are based on bidirectional long- and short-term memory networks. Experimental results on four periods of the COVID-19 pandemic show how the proposed approach can model optimism and pessimism in the context of a health crisis. There is a total of 150,503 tweets and 51,319 unique users. Conversations are characterised in terms of emotional signals and shifts to unravel empathy and support mechanisms. Conversations with stronger pessimistic signals denoted little emotional shift (i.e. 62.21% of these conversations experienced almost no change in emotion). In turn, only 10.42% of the conversations laying more on the optimistic side maintained the mood. User emotional volatility is further linked with social influence.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.