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Github repo for Visual Framing of Science Conspiracy Videos: Integrating Machine Learning with Communication Theories to Study the Use of Color

Figure 2: Saturation comparison between conspiracy vs debunking videos

  • to run t-test and generate boxplot, execute statistics test/t-test.R
  • input: statistics test/latest_video_feature.csv

Table 1: Significant results on COVID-19 conspiracy-related videos: using first 10 seconds

  • run t-test and calculate p-value: statistics test/t-test.R with input statistics test/latest_ten_seconds_features.csv

  • run Benjamini-Hochberg test: statistics test/bh-test.R with input statistics test/latest_ten_seconds_features.csv

  • calculate Cohen's d: statistics test/cohen.R with input statistics test/latest_ten_seconds_features.csv

Table 2: Significant results on COVID-19 conspiracy-related videos: using thumbnails

  • run t-test and calculate p-value: statistics test/t-test.R with input statistics test/COVID19_thumbnails_low_aesthetics.xlsx

  • run Benjamini-Hochberg test: statistics test/bh-test.R with input statistics test/COVID19_thumbnails_low_aesthetics.xlsx

  • calculate Cohen's d: statistics test/cohen.R with input statistics test/COVID19_thumbnails_low_aesthetics.xlsx

Table 3: Significant results on climate change conspiracy-related videos

  • run t-test and calculate p-value: statistics test/t-test.R with input statistics test/climate_feature.csv, statistics test/ten_seconds_featureclimate.csv, statistics test/Climatechange_thumbnails_low_aesthetics.csv,

  • run Benjamini-Hochberg test: statistics test/bh-test.R with input statistics test/climate_feature.csv, statistics test/ten_seconds_featureclimate.csv, statistics test/Climatechange_thumbnails_low_aesthetics.csv,

  • calculate Cohen's d: statistics test/cohen.R with input statistics test/climate_feature.csv, statistics test/ten_seconds_featureclimate.csv, statistics test/Climatechange_thumbnails_low_aesthetics.csv,

Table 4(a). Performance comparison in identifying conspiracy videos from correction videos.

You can find all the model setups and input data files under latest models/models-debunk.

  • (1) .ipynb files are holders to train the models. Within each ipynb folder, you can find the corresponding models and their performance.
  • (2) .h5 files are the output models.

Appendix IV. Correlation matrix of the visual features used in Model 4

Run statistics test/heatmap.ipynb to generate correlation matrix of the visual features with input latest models/handlabel_feature.csv.

Appendix V. Performance comparison in identifying conspiracy videos from normal videos.

You can find all the model setups and input data files under latest models/models-normal.

  • (1) .ipynb files are holders to train the models. Within each ipynb folder, you can find the corresponding models and their performance.
  • (2) .h5 files are the output models.

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