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  1. Jan 2022
  2. psyarxiv.com psyarxiv.com
    Affect in science communication: A data-driven analysis of TED talks
    1
    1. jasminehollingworth 07 Jan 2022

      Fischer, O., Jeitziner, L., & Wulff, D. U. (2021). Affect in science communication: A data-driven analysis of TED talks. PsyArXiv. https://doi.org/10.31234/osf.io/28yc5

      lang:en is:preprint science communication affect data-driven TED talk peer general public understanding audience scientific content engagement affect valence density popularity polarity association topic sentiment analysis social media
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    psyarxiv.com/28yc5/
  3. Jul 2020
  4. osf.io osf.io
    Initial Effects of the Coronavirus Pandemic on Racial Prejudice in the United States: Evidence from Google Trends
    1
    1. ErikStuchly 26 Jul 2020

      Vachuska, K. (2020). Initial Effects of the Coronavirus Pandemic on Racial Prejudice in the United States: Evidence from Google Trends [Preprint]. SocArXiv. https://doi.org/10.31235/osf.io/bgpk3

      is:preprint lang:en COVID-19 racial prejudice racism USA initial effect Google Trends Asian-American public opinion anti-Chinese anti-Hispanic sentiment discrimination
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    osf.io/preprints/socarxiv/bgpk3/
  5. Jun 2020
  6. www.nature.com www.nature.com
    Has Twitter just had its saddest fortnight ever?
    1
    1. edampf 16 Jun 2020

      Viglione, G. (2020). Has Twitter just had its saddest fortnight ever? Nature. https://doi.org/10.1038/d41586-020-01818-3

      is:news lang:en COVID-19 Twitter social media mental health mood public sentiment mathematics hedonometer unhappiness language
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    nature.com/articles/d41586-020-01818-3
  7. May 2020
  8. www.preprints.org www.preprints.org
    COVID-19 Public Sentiment Insights and Machine Learning for Tweets Classification
    1
    1. edampf 29 May 2020

      Samuel, J.; Ali, G.G.M.N.; Rahman, M.M.; Esawi, E.; Samuel, Y. COVID-19 Public Sentiment Insights and Machine Learning for Tweets Classification. Preprints 2020, 2020050015 (doi: 10.20944/preprints202005.0015.v1)

      is:preprint lang:en COVID-19 public sentiment machine learning tweet Twitter sentiment analysis text analysis misinformation crisis R package fear USA Naive Bayes method logistic regression
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    preprints.org/manuscript/202005.0015/v1