Table 1.
COVID-19 disinformation category data structure.
Table 2.
Label counts and annotation agreements of unfiltered annotation (All) and filtered annotation (Cleaned).
Table 3.
Number of examples per category in the final dataset.
Fig 1.
Overview of model architecture, linear block is the linear transformation (i.e. linear(x) = Wx + b), nonLin is linear transformation with non-linear activation function f(linear(.)), softmax is softmax activated linear function.
Table 4.
Five-fold cross-valuation classification and topic modelling results, n/a stands for not applicable for the model.
The standard deviation is shown in parentheses. The majority class is ‘PubAuth’ at 19.4%.
Table 5.
COVID-19 disinformation class level F1 score, standard deviation in parentheses.
Fig 2.
a. Percentage stacked column chart of CANTM category prediction b. Percentage stacked column chart of human agreements in the pairwise agreement measurement.
Table 6.
Statistics of debunked COVID-19 disinformation by IFCN members.
(1 January—30 June 2020).
Fig 3.
Weekly trends of normalised IFCN debunks, COVID related Google searches and categories.
Fig 4.
Percentage stacked column chart of media type vs. category.
Fig 5.
Percentage stacked column chart of claim origin vs. category.
Fig 6.
Percentage stacked column chart of veracity type vs. category.
Table 7.
COVID-19 classification-associated topics from unlabelled data.