2 ms·

It's Nothing but a Deepfake [pdf]

• jruohonen 3y ago
  "We find that exposure to an accusation of misinformation or disinformation lowered the perceived credibility of the video but did not affect policy preferences related to the content of the video. Furthermore, more extreme disinformation accusations were perceived as less credible than milder misinformation labels. The effects lasted over a period of three days and still occurred when there was a delay in the label attribution. These findings indicate that while mis- and disinformation labels might make authentic content less credible, they are themselves not always deemed credible and are less likely to change substantive policy preferences." Kind of reflects my intuitive opinion. Insofar as it is about politics, people have already made up their minds. Then, you have also the great risk that if you label something as fake that ends up being not fake, or vice versa, you lose your credibility pretty quickly. I haven't seen much discussion about these risk issues from stakeholders. Given that ML models are trained with these fact-checking databases, I'd like to see also some replication checks about the validity of such training data.