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A Content and Sentiment Analysis of Greek Tweets during the Pandemic
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Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1001/jama.2020.4469openalex-snapshot
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Twitter-based analysis reveals differential COVID-19 concerns across areas with socioeconomic disparities
inferredcites →https://doi.org/10.1016/j.dss.2014.07.003openalex-snapshot
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How Parents and Their Children Used Social Media and Technology at the Beginning of the COVID-19 Pandemic and Associations with Anxiety
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World leaders’ usage of Twitter in response to the COVID-19 pandemic: a content analysis
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Twitter Sentiment Analysis on Worldwide COVID-19 Outbreaks
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inferredcites →https://doi.org/10.3389/fdata.2020.00032openalex-snapshot
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Centrality as a Method for the Evaluation of Semantic Resources for Disaster Risk Reduction
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inferredcites →https://doi.org/10.4337/9781800377486openalex-snapshot
Encyclopedia of Tourism Management and Marketing
inferredcites →https://doi.org/10.48550/arxiv.2004.04315openalex-snapshot
Large Arabic Twitter Dataset on COVID-19
inferredcites →https://doi.org/10.48550/arxiv.2010.14234openalex-snapshot
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inferredcites →https://doi.org/10.5121/ijfcst.2014.4504openalex-snapshot
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SENTIWORDNET: A Publicly Available Lexical Resource for Opinion Mining
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