%Aigaion2 BibTeX export from HES SO Valais Publications
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@INPROCEEDINGS{,
        author = {Liu, Zhan and Shabani, Shaban and Yu, Xianyun and Maria, Sokhn and Glassey Balet, Nicole},
      keywords = {Collaborative intelligence, COVID-19, False news, Human-centred approach, Logistic regression, media, News articles},
         month = jun,
         title = {A Collaborative Intelligence Approach to Fighting COVID-19 False News: A Chinese Case},
     booktitle = {15th International Conference on Human-Centered Intelligent Systems},
          year = {2022},
         pages = {3-12},
     publisher = {Springer Nature},
      location = {Rhodes, Greece},
  organization = {KES},
           url = {https://link.springer.com/chapter/10.1007/978-981-19-3455-1_1},
           doi = {10.1007/978-981-19-3455-1_1},
      abstract = {The rapid outbreak of COVID-19 has heightened interest in news about the pandemic. In addition to
obtaining real-time developments about COVID-19, people have learned about prevention methods
through the news media. Ironically, false COVID-19 news has spread faster than the virus, posing an
additional health threat with advice being as dangerous as infection. In this study, we developed a Chinese
news article dataset on COVID-19 misinformation, which contained 1266 verified articles from 118
Chinese digital newspaper platforms from January 2020 to January 2021. This dataset uses machine
learning methods to detect false news in the Chinese language. Because automated classification methods,
combined with human computation-based approaches, are effective for combating digital misinformation,
we applied and evaluated a collaborative intelligence approach that leverages human fact-checking skills
with feedback on news stories using four criteria: source, author, message, and spelling. The results show
that reliable human feedback can help detect false news with high accuracy.}
}