quantifying the invisible audience in social networks

stanford hci group

Quantifying the Invisible Audience in Social Networks

Eytan Bakshy, Moira Burke, Brian KarrerFacebook Data Science Team

Michael BernsteinStanford Computer Science Department

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

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

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Quantify the difference between users’ estimated and actual audience

Quantify the difference between users’ estimated and actual audience

Measure audience size uncertainty for 220,000 Facebook users

Our perception of audience size affects our behaviorWe guide our audience’s impression of us[Go!man 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]

Our perception of audience size affects our behaviorWe guide our audience’s impression of us[Go!man 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?

Perceived audience vs. reality - survey - folk theories of audience - desired audience size

Predictability of audience size - using friend count - using feedback

Perceived audience vs. reality - survey - folk theories of audience - desired audience size

Predictability of audience size - using friend count - using feedback

MethodData

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

MethodAudience size measurement

Javascript tracking whether a story remained in the browser viewport for at least 900ms

MethodAudience size measurement

Javascript tracking whether a story remained in the browser viewport for at least 900ms

MethodAudience size measurement

Javascript tracking whether a story remained in the browser viewport for at least 900ms

900ms

MethodAudience size measurement

Javascript tracking whether a story remained in the browser viewport for at least 900ms

MethodAudience 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]

MethodSurvey

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 surveyShow 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?”

MethodAnalysis

Compare participants’ actual audience size totheir estimated audience size

MethodAnalysis

Compare participants’ actual audience size totheir estimated audience size

Consider your own most recent status update: What percentage of your social network do you think saw it?

Users underestimate by 4xResults

Users underestimate by 4xResults

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Users underestimate by 4xResults

Users underestimate by 4xResults

Accurate estimations along the diagonal

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Users underestimate by 4xResults

Accurate estimations along the diagonal

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Users underestimate by 4xResults

Estimated20 friends = 6% of network

Actual78 friends = 24% of network

R2 = 0.04

overestimates

underestimates

Folk theories of audienceResults

Inductive coding on participants’ reasons for how estimating their audience (Fleiss’s Kappa = 0.72)

Folk theories of audienceResults

Inductive coding on participants’ reasons for how estimating their audience (Fleiss’s Kappa = 0.72)

Random guess 23%

Folk theories of audienceResults

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”

Folk theories of audienceResults

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.”

Folk theories of audienceResults

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”

Folk theories of audienceResults

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%

Folk theories of audienceResults

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%

Users want larger audiencesResults

same more far morefewerfar fewer

“How many people do you wish saw this content?”

50% 25% 22%

Users want larger audiencesResults

Roughly half want a larger audience...but they already have it.

same more far morefewerfar fewer

“How many people do you wish saw this content?”

50% 25% 22%

Users underestimate their audience by 4x

Common folk theories use feedback and friend count

Users want larger audiences, but already have them

Perceived audience vs. reality - survey - folk theories of audience - desired audience size

Predictability of audience size - using friend count - using feedback

Perceived audience vs. reality - survey - folk theories of audience - desired audience size

Predictability of audience size - using friend count - using feedback

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

35% of friends see median postResults

More friends means higher variability in audience

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50th percentile by friend count (266)

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35% of friends see median postResults

More friends means higher variability in audience

25th percentile by friend count (138)

35% of friends see median postResults

More friends means higher variability in audience

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75th percentile by friend count (484)

Audience size is highly variableResults

Highly variable: 50th percentile range is 20% of friends

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Audience size is highly variableResults

Highly variable: 50th percentile range is 20% of friends

50th percentile range90th percentile range

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Audience size predictionOLS 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%

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0 5 10 15Unique friends liking the post

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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%

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 predictionOLS regression

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 predictionOLS regression

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 predictionOLS regression

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

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61% of friends see at least one of the user’s posts each month

However, actual audience size is still highly variable

Discussion

Fundamental mismatch between perceived and actual audienceHow 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

Fundamental mismatch between perceived and actual audienceHow 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

Why underestimate audience size?

The wishful thinking hypothesis: more comfortable to blame a noisy distribution channel than to blame yourself for writing bad content

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?

Your invisible audience is larger than you probably think.

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

stanford hci group

Quantifying the Invisible Audience in Social Networks

Eytan Bakshy, Moira Burke, Brian KarrerFacebook Data Science Team

Michael BernsteinStanford Computer Science Department