The first group of questions are based on Hunt Allcott and Matthew Gentzkow, 2017, Social Media and Fake News in the 2016 Election, Journal of Economic Perspectives, 31(2): 211-236.

  1. What is fake news?
  2. Give a recent example of fake news. How do we know it is fake?
  3. Have technologically developments made fake news easier or harder to produce? Justify your conclusion.
  4. Who is least likely to be fooled by fake news? Use the results from Table 1 to support your conclusions.
  5. To some extent, fake news is encouraged by confirmation bias. What is confirmation bias? How does confirmation bias help us understand why false ideas can persist.
  6. Here is a very recent item about a Turkish lawmaker smashing a cellphone in parliament, as he objects to a law that would ban propagating misleading information. What does this tell us about the difficulty of regulating the truth of information?

Bayes rule.

  • You are afraid you have caught tuberculosis, which hits 0.01% of the population, so you decide to get tested. If you do have tuberculosis, there is a 99.9% chance that the test will return a result of “positive”.  If you don’t have tuberculosis, there is a 0.2% chance that it will show up positive – a “false positive”.
  • If your test shows a positive result, what is the probability that you have tuberculosis?
  • If your test shows a negative result, what is the probability that you do not have tuberculosis?
  • How accurate are PCR tests for COVID19? How accurate are antigen (“at home”) tests for COVID19? If you test positive with a PCR test, what is the probability that you have COVID19? And with an antigen test?  [You will need to look up some information on this, and may need to add some guesstimates of your own.] Show any workings.

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