Estimating influence of online activity feeds on people's actions
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Transcript of Estimating influence of online activity feeds on people's actions
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Distinguishing between Personal Preferences and Social Influence in Online
Activity FeedsAmit Sharma* Dan Cosley
Microsoft Research Cornell University
ACM CSCW 2016
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The power of social influence
Kandel (1978), Fisher and Bauman (1988), Cialdini (2001)
Adopting new behavior
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The power of social influence
Kandel (1978), Fisher and Bauman (1988), Cialdini (2001)
Changing people’s behavior
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The power of social influence
Goel et al. (2012), Iyenger et al. (2011)
Spreading ideas, products
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Socio-technical systems: systems as social agents
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Specific problem: How much do people copy their friends’ actions from the feed?
Copy-influence: Copying a friend’s action after being exposed to their activity on a social networking website
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Tricky to estimate influence
• Did A’s friend like Item1 because of influence from A?
• Impossible to disentangle homophily without making further assumptions. (Shalizi and Thomas 2012)
Person A
Person A’s friend
Item1 Item2 Item 3
Item1
t
t
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Key idea: Imagine a counterfactual world
What would have happened if a Last.fm user was not exposed to the activity feed of her friends?
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A general framework: estimating the counterfactual
Estimate counterfactual
Data
ProblemEstimate influence from feed
Observed common actions between
friends (X)
Common actions without
exposure
Xc?
Copy-Influence = X – Xc
Assumptions?
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Matching: A typical way to estimate counterfactuals
Matching Assumption: People similar on observable characteristics are expected to have the same behavior.
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Matching: A typical way to estimate counterfactualsWe can match people by their attributes such as gender, race, age. [Aral et al. 2009]
Person A liked Coldplay after her
friend did so.
A similar person A’ liked Coldplay without
her friend doing so.
Person A would have liked Coldplay without
her friend doing so.
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Can we do better for estimating the effect of activity feeds?
I. Preference Similarity assumption: Past actions of a person are a better proxy for modeling behavior.
Use similarity metrics based on past activity.
II. Feed Exposure assumption: People see friends’ updates in an unfiltered reverse-chronological feed.
Each person is exposed to the last M actions of their friends.
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I. Matching non-friends using preference similarityUse Jaccard similarity in observed preferences to create a proxy for homophily.
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Non-FriendsFriends
f5
u
f1
f4
f3f2
n5
u
n1
n4
n3n2
0.4 0.4
0.70.3
0.60.5
0.7 0.3
0.60.5
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II. Comparing to a counterfactual feed for non-friendsConstruct feed using last M actions of non-friends.
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Friends’ feed
f1 Likes Beatles.
f2 Likes Coldplay.
f3 Likes Adele.
Matched Non-friends’ feed
n1 Likes Eminem.
n2 Likes Beatles.
n3 Likes LillyAllen.
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The full procedure: Estimating the copy-influence from a feedFor each action by a user, construct feeds from friends and non-friends containing their last M actions respectively.
Friends Overlap = Fraction of actions done by u that are also in the friends’ Feed (Naïve measure of Influence).
NonFriends Overlap = Fraction of actions done by u that are also in the non-friends’ Feed.
Copy-Influenceu = FriendsOverlap – NonFriendsOverlap
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Preference-based Matched Estimation (PME)MATCHING STEP (before time T)
For each user:Construct a set of non-friends that are as similar to the user as her friends.
ESTIMATION STEP (after time T) For each user: Influenceu = FriendsOverlap – NonFriendsOverlap
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The Last.fm dataset
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LISTEN SONG LOVE SONG
# Ego Networks 96K# Total Users 312K# Total Songs 23M# Total Actions 656M
# Ego Networks 141K# Total Users 437K# Total Songs 13M# Total Actions 140M
Size of Feed(M) = 10Time T is chosen such that 90% of actions are before T.
Random seeds, Weighted breadth-first crawl for 3 months
*Dataset available at: http://www.amitsharma.in/#resources
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Validation using semi-synthetic Loves data
Personal preference: Choose a song randomly from the last M loves by the k-most similar users (k=10).
Influence process: Choose a song randomly from the last M loves by her friends.
Process FriendsOverlap Influence Std. Error Personal Preference(PP) 0.042 0.001 0.0001
Influence(I) 1.00 0.99 0.0004
I-PP (10%-90%) 0.15 0.102 0.0001
Generate synthetic loves on songs after time T from any of the processes, keeping the timestamps and the social network same as before.
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FriendsOverlap overestimates influence by at least 300% across listen and love actions.
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Is this specific to Last.fm?
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Assumptions of Influence Estimation:Ordinal time, reverse chronological feedPreferences as a proxy for homophily
Can be applied to any sharing platform that shows friends’ activities in a (loosely) reverse chronological order.
RATE BOOKS FAVORITE PHOTOS RATE MOVIES
# Ego Networks 252K# Total Users 252K# Total Items
1.3M# Total Actions 28M
# Ego Networks 49K# Total Users
50K# Total Items
48K# Total Actions 7.9M
# Ego Networks 175K# Total Users 183K# Total Items 11M# Total Actions
33M
[Huang et al. ‘12] [Jamali and Ester ‘10] [Cha et al. ‘09]
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FriendsOverlap overestimates influence in all three domains
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Overestimate by 14% in Flickr, more than 500% in Flixster.
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Influence is overrated(?)
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Not more than 1% of user actions on online sharing networks can be attributed to influence.
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• Focusing on a specific mechanism helps make progress on a tricky estimation problem.
• PME: A broadly applicable method for estimating influence that requires only logged activity data.
• Going forward, modeling counterfactuals can be a viable way to understand activity on socio-technical systems.
Final takeaways
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thank you!
@amt_shrma
• PME: A broadly applicable method for estimating influence that requires only logged activity data.
• Going forward, modeling counterfactuals can be a viable way to understand activity on socio-technical systems.