Quantifying the Invisible Audience in Social Networks

1. stanford hci group Quantifying the Invisible Audience in Social Networks Eytan Bakshy, Moira Burke, Brian Karrer Facebook Data Science Team Michael Bernstein Stanford Computer Science Department

2. Sharing on a social network is like giving a talk from behind a curtain.

3. Sharing on a social network is like giving a talk from behind a curtain. 2

5. Quantify the diﬀerence between users’ estimated and actual audience

6. Quantify the diﬀerence between users’ estimated and actual audience Measure audience size uncertainty for 220,000 Facebook users

7. Our perception of audience size aﬀects our behavior We guide our audience’s impression of us [Goﬀman 1959] We manage the boundaries of when to engage [Altman 1975] On social media, we speak to the audience that we expect is listening [Marwick and boyd 2011, Viégas 1999]

8. Our perception of audience size aﬀects our behavior We guide our audience’s impression of us [Goﬀman 1959] We manage the boundaries of when to engage [Altman 1975] On social media, we speak to the audience that we expect is listening [Marwick and boyd 2011, Viégas 1999] What if our audience size estimates are inaccurate?

9. Perceived audience vs. reality - survey - folk theories of audience - desired audience size Predictability of audience size - using friend count - using feedback

11. Method Data 220,000 U.S. Facebook users who share with friends-only privacy Collected audience information for their status updates and link shares over 30 days 150,000,000 viewer-story pairs

12. Method Audience size measurement Javascript tracking whether a story remained in the browser viewport for at least 900ms

14. Method Audience size measurement Javascript tracking whether a story remained in the browser viewport for at least 900ms 900ms

16. Method Audience size measurement Javascript tracking whether a story remained in the browser viewport for at least 900ms Not a direct measure of attention: users remember ~70% of posts they see [Counts and Fisher 2011]

17. Method Survey Recruited users with recent content (2-90 days ago) via a request at the top of news feed N=589; 61% female; mean age 33 Audience size survey Show participants their most recent story “How many people do you think saw it?” “Describe how you came up with that number.” “How many people do you wish saw this content?”

18. Method Analysis Compare participants’ actual audience size to their estimated audience size

19. Method Analysis Compare participants’ actual audience size to their estimated audience size Consider your own most recent status update: What percentage of your social network do you think saw it?

20. Users underestimate by 4x Results

21. Users underestimate by 4x Results 0% 20% 40% 60% 80% 100% 0% 20% 40% 60% 80% 100% Actual audience (% of friends)

22. 0% 20% 40% 60% 80% 100% 0% 20% 40% 60% 80% 100% Actual audience (% of friends) Perceivedaudience(%offriends) Users underestimate by 4x Results

23. Users underestimate by 4x Results Accurate estimations along the diagonal 0% 20% 40% 60% 80% 100% 0% 20% 40% 60% 80% 100% Actual audience (% of friends) Perceivedaudience(%offriends)

24. Users underestimate by 4x Results Accurate estimations along the diagonal 0% 20% 40% 60% 80% 100% 0% 20% 40% 60% 80% 100% Actual audience (% of friends) Perceivedaudience(%offriends) overestimates underestimates

25. 0% 20% 40% 60% 80% 100% 0% 20% 40% 60% 80% 100% Actual audience (% of friends) Perceivedaudience(%offriends) Users underestimate by 4x Results Estimated 20 friends = 6% of network Actual 78 friends = 24% of network R2 = 0.04 overestimates underestimates

26. Folk theories of audience Results Inductive coding on participants’ reasons for how estimating their audience (Fleiss’s Kappa = 0.72)

27. Folk theories of audience Results Inductive coding on participants’ reasons for how estimating their audience (Fleiss’s Kappa = 0.72) Random guess 23%

28. Folk theories of audience Results Inductive coding on participants’ reasons for how estimating their audience (Fleiss’s Kappa = 0.72) Random guess 23% Feedback — likes and comments 21% “I ﬁgured about half of the people who see it will ‘like’ it, or comment on it”

29. Folk theories of audience Results Inductive coding on participants’ reasons for how estimating their audience (Fleiss’s Kappa = 0.72) Random guess 23% Feedback — likes and comments 21% Fraction of friend count 15% “Maybe a third of my friends saw it.”

30. Folk theories of audience Results Inductive coding on participants’ reasons for how estimating their audience (Fleiss’s Kappa = 0.72) Random guess 23% Feedback — likes and comments 21% Fraction of friend count 15% Login timing 9% “Not a lot of people stay up late at night”

31. Folk theories of audience Results Inductive coding on participants’ reasons for how estimating their audience (Fleiss’s Kappa = 0.72) Random guess 23% Feedback — likes and comments 21% Fraction of friend count 15% Login timing 9% Friends seen active on the site 5% Number of close friends and family 3% Who might be interested in the topic 2% Other 10%

32. Folk theories of audience Results No folk theory was more accurate than a random guess Random guess 23% Feedback — likes and comments 21% Fraction of friend count 15% Login timing 9% Friends seen active on the site 5% Number of close friends and family 3% Who might be interested in the topic 2% Other 10%

33. Users want larger audiences Results same more far morefewer far fewer “How many people do you wish saw this content?” 50% 25% 22%

34. Users want larger audiences Results Roughly half want a larger audience... but they already have it. same more far morefewer far fewer “How many people do you wish saw this content?” 50% 25% 22%

35. Users underestimate their audience by 4x Common folk theories use feedback and friend count Users want larger audiences, but already have them

38. Can we predict a post’s audience using public signals? using the full 220,000 user and 150,000,000 view dataset

39. 35% of friends see median post Results More friends means higher variability in audience 0.00 0.01 0.02 0.03 0 50 100 150 200 Number of friends who saw post Density 50th percentile by friend count (266)

40. 0.00 0.01 0.02 0.03 0 50 100 150 200 Number of friends who saw post Density 35% of friends see median post Results More friends means higher variability in audience 25th percentile by friend count (138)

41. 35% of friends see median post Results More friends means higher variability in audience 0.00 0.01 0.02 0.03 0 50 100 150 200 Number of friends who saw post Density 75th percentile by friend count (484)

42. Audience size is highly variable Results Highly variable: 50th percentile range is 20% of friends 0% 20% 40% 60% 80% 100% lllllllllllllllllllllllllllllllll 0 200 400 600 800 Number of friends Percentoffriendswhosawpost

43. Audience size is highly variable Results Highly variable: 50th percentile range is 20% of friends 50th percentile range 90th percentile range0% 20% 40% 60% 80% 100% lllllllllllllllllllllllllllllllll 0 200 400 600 800 Number of friends Percentoffriendswhosawpost

44. Audience size prediction OLS regression Model predictors R2 Mean absolute error Friend count 0.12 8% of friend count Feedback 0.13 8% Friend count and feedback 0.27 7%

45. 0% 20% 40% 60% 80% 100% l l l l l l l l l l l l l l l l l l l l 0 5 10 15 Unique friends liking the post Percentoffriendswhosawpost Feedback is not predictive Results Rapid audience growth until the post receives feedback from ﬁve unique friends Posts with no likes or comments have especially large variance: 90th percentile is 2%–55%

46. Model predictors R2 Mean absolute error Friend count 0.12 8% of friend count Feedback 0.13 8% of friend count Friend count and feedback 0.27 7% of friend count Audience size prediction OLS regression

47. Model predictors R2 Mean absolute error Friend count 0.12 8% of friend count Feedback 0.13 8% of friend count Friend count and feedback 0.27 7% of friend count Audience size prediction OLS regression

48. Model predictors R2 Mean absolute error Friend count 0.12 8% of friend count Feedback 0.13 8% of friend count Friend count and feedback 0.27 7% of friend count Even with access to all user-visible signals, audience size is still unpredictable. Audience size prediction OLS regression

49. How predictable is a user’s cumulative audience? Consider the audience for all of a user’s posts over 30 days instead of a single post 50% of the users in our sample produced ﬁve or more pieces of content during the month

50. 0% 20% 40% 60% 80% 100% l l l llllllllllllllllllllllllllllll 0 200 400 600 800 Number of friends Cumulativeaudience(%offriends) 61% of friends see at least one of the user’s posts each month However, actual audience size is still highly variable

51. Discussion

52. Fundamental mismatch between perceived and actual audience How might a 4x underestimate be impacting user behavior? Type of content shared, sharing volume, motivation Ambiguous whether a more socially transparent design would be desirable

53. Fundamental mismatch between perceived and actual audience How might a 4x underestimate be impacting user behavior? Type of content shared, sharing volume, motivation Ambiguous whether a more socially transparent design would be desirable

54. Why underestimate audience size? The wishful thinking hypothesis: more comfortable to blame a noisy distribution channel than to blame yourself for writing bad content

55. Why underestimate audience size? The wishful thinking hypothesis: more comfortable to blame a noisy distribution channel than to blame yourself for writing bad content What role might be played by... The availability heuristic? Algorithmic feed ﬁltering?

56. Your invisible audience is larger than you probably think.

57. Your invisible audience is larger than you probably think. Users underestimate audience size by 4x Median reach is 35% per post and 61% per month Many want larger audiences but already have them

58. stanford hci group Quantifying the Invisible Audience in Social Networks Eytan Bakshy, Moira Burke, Brian Karrer Facebook Data Science Team Michael Bernstein Stanford Computer Science Department

Quantifying the Invisible Audience in Social Networks

Quantifying the Invisible Audience in Social Networks

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