full transcript

From the Ted Talk by Jennifer Golbeck: Your social media "likes" expose more than you think

Unscramble the Blue Letters


So this is pretty complicated stuff, right? It's a hard thing to sit down and explain to an average user, and even if you do, what can the average user do about it? How do you know that you've liked something that indicates a trait for you that's ttlaloy irrelevant to the ctoennt of what you've liked? There's a lot of power that uesrs don't have to control how this data is used. And I see that as a real problem going forward.

So I think there's a couple phtas that we want to look at if we want to give users some control over how this data is used, because it's not always going to be used for their benefit. An example I often give is that, if I ever get bored being a professor, I'm going to go srtat a compnay that predicts all of these attributes and things like how well you work in teams and if you're a drug user, if you're an alcoholic. We know how to predict all that. And I'm going to sell rporets to H.R. companies and big businesses that want to hire you. We totally can do that now. I could start that business tomorrow, and you would have absolutely no conortl over me using your data like that. That seems to me to be a problem.

Open Cloze


So this is pretty complicated stuff, right? It's a hard thing to sit down and explain to an average user, and even if you do, what can the average user do about it? How do you know that you've liked something that indicates a trait for you that's _______ irrelevant to the _______ of what you've liked? There's a lot of power that _____ don't have to control how this data is used. And I see that as a real problem going forward.

So I think there's a couple _____ that we want to look at if we want to give users some control over how this data is used, because it's not always going to be used for their benefit. An example I often give is that, if I ever get bored being a professor, I'm going to go _____ a _______ that predicts all of these attributes and things like how well you work in teams and if you're a drug user, if you're an alcoholic. We know how to predict all that. And I'm going to sell _______ to H.R. companies and big businesses that want to hire you. We totally can do that now. I could start that business tomorrow, and you would have absolutely no _______ over me using your data like that. That seems to me to be a problem.

Solution


  1. company
  2. reports
  3. users
  4. control
  5. paths
  6. totally
  7. content
  8. start

Original Text


So this is pretty complicated stuff, right? It's a hard thing to sit down and explain to an average user, and even if you do, what can the average user do about it? How do you know that you've liked something that indicates a trait for you that's totally irrelevant to the content of what you've liked? There's a lot of power that users don't have to control how this data is used. And I see that as a real problem going forward.

So I think there's a couple paths that we want to look at if we want to give users some control over how this data is used, because it's not always going to be used for their benefit. An example I often give is that, if I ever get bored being a professor, I'm going to go start a company that predicts all of these attributes and things like how well you work in teams and if you're a drug user, if you're an alcoholic. We know how to predict all that. And I'm going to sell reports to H.R. companies and big businesses that want to hire you. We totally can do that now. I could start that business tomorrow, and you would have absolutely no control over me using your data like that. That seems to me to be a problem.

Frequently Occurring Word Combinations


ngrams of length 2

collocation frequency
social media 6
curly fries 3
media companies 3
social networks 2
personal data 2
people interact 2
totally irrelevant 2
information spreads 2

ngrams of length 3

collocation frequency
social media companies 3

Important Words


  1. absolutely
  2. alcoholic
  3. attributes
  4. average
  5. benefit
  6. big
  7. bored
  8. business
  9. businesses
  10. companies
  11. company
  12. complicated
  13. content
  14. control
  15. couple
  16. data
  17. drug
  18. explain
  19. give
  20. hard
  21. hire
  22. irrelevant
  23. lot
  24. paths
  25. power
  26. predict
  27. predicts
  28. pretty
  29. problem
  30. professor
  31. real
  32. reports
  33. sell
  34. sit
  35. start
  36. stuff
  37. teams
  38. tomorrow
  39. totally
  40. trait
  41. user
  42. users
  43. work