Unmasking the conversation on masks: Natural language processing for topical sentiment analysis of COVID-19 Twitter discourse
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Abstract
In this exploratory study, we scrutinize a database of over one million tweets collected from March to July 2020 to illustrate public attitudes towards mask usage during the COVID-19 pandemic. We employ natural language processing, clustering and sentiment analysis techniques to organize tweets relating to mask-wearing into high-level themes, then relay narratives for each theme using automatic text summarization. In recent months, a body of literature has highlighted the robustness of trends in online activity as proxies for the sociological impact of COVID-19. We find that topic clustering based on mask-related Twitter data offers revealing insights into societal perceptions of COVID-19 and techniques for its prevention. We observe that the volume and polarity of mask-related tweets has greatly increased. Importantly, the analysis pipeline presented may be leveraged by the health community for qualitative assessment of public response to health intervention techniques in real time.
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SciScore for 10.1101/2020.08.28.20183863: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Two important limitations of our summarization method should be noted. First, the BART-based decoder is a generative language model which creates summaries autoregressively by repeatedly sampling from next-word probability distributions over an entire vocabulary. For this reason, the output summaries are prone to factual inaccuracy in a manner which extractive summarization approaches are not. Second, large or irregularly shaped …
SciScore for 10.1101/2020.08.28.20183863: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Two important limitations of our summarization method should be noted. First, the BART-based decoder is a generative language model which creates summaries autoregressively by repeatedly sampling from next-word probability distributions over an entire vocabulary. For this reason, the output summaries are prone to factual inaccuracy in a manner which extractive summarization approaches are not. Second, large or irregularly shaped subclusters may be poorly represented by the tweets immediately surrounding the subcluster center. In these situations the generated summary may not be applicable to the entire subcluster. We accept these as limitations of the system and advise readers to regard the summaries as context clues rather than as given facts.
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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