The document discusses social influence and how social connections shape preferences. It examines three questions: whether friends' activities can predict our own, if friends' activities can help with decisions and opinions, and if friends would suggest items we like. The author has conducted multiple studies on social networks like Facebook and Twitter to analyze how preferences cluster among friends and how social explanations can influence recommendations. Their findings show preferences tend to be localized among friends and certain social explanations like close friends are more influential than popularity-based explanations.

1. The role of social
connections in shaping
our preferences
Understanding sharing and consumption
online
Amit Sharma
Ph.D. Candidate
Dept. of Computer Science
Cornell University
www.cs.cornell.edu/~asharma
@amt_shrma

2. Collaborators
● Dan Cosley, Advisor, Cornell University
● Baoshi Yan, LinkedIn
● Gueorgi Kossinets, Google
● Jake Hofman and Duncan Watts, Microsoft Research
● Students
■ Masters: Meethu Malu, Mevlana Gemici, Michael
Triche
■ Undergraduate: Yulan Miao

3. Finding meaning in social data
People express their connection
with items in myriad ways on the
web
Examples
● Hashtag on Twitter
● Like on Facebook
● Rate on Goodreads
● +1 on Google
● Favorite on Etsy
How do these activities
connect to people’s
decisions on items,
products, opinions?
Connection between retweeting
and influence, liking and buying,
sharing and consuming

4. How items diffuse through social
networks: The “holy grail”
Past work has studied:
● intrinsic attributes of
items
● the influence of certain
individuals
Selection Bias: Studies on
online data show that most
shares propagate only one
level; a tiny fraction get to
more than 1 level

5. Sharing + Independent Consumption
across the network

6. Sharing + Independent Consumption
across the network

7. Sharing + Independent Consumption
across the network
Ego
Networks

8. ● Can our friends’ activities be used to predict
ours?
■ You like this because Jeetu liked it.
● Can information about our friends’ activities
help us make decisions on items, form our
opinions?
■ Amit Sharma and 10 of your friends like this.
● Would our friends suggest items that we
would like?
■ Jeff Bezos shared this to you.
Three questions for research

9. Three questions for research
Can our friends’ activities
be used to predict ours?
Can information about our
friends’ activities help us
make decisions on items,
form our opinions?
Would our friends suggest
items that we would like?
How to design network-aware
recommendation models?
How to present social
information in system
interfaces?
dummy
How to include manual shares
in recommender systems?

10. Ego Network
Subgraph containing a person
and her immediate social
connections.
Friend
Any first-degree connection of a
person as defined by a particular
social network.
Preference
(Partial) ordering over items that
helps a person choose items to
consume.
Definitions

11. Part I: Predicting users’
activities based on
friends’ activities
A study using data from Facebook and
Twitter
ICWSM 2013

12. Datasets from Facebook and Twitter
Preferences: Movie and music Likes on Facebook,
hashtag usage on Twitter
Data collected from people who gave permission to Facebook apps [Sharma

13. What would be good measures of
preference locality?
● User similarity-based: how similar are
people’s activities on items in the ego
network versus the full network
○ Similarity between users
○ Density of the user-item matrix
● Item coverage-based: how widely spread
are items in the network
○ Number of ego networks an item is a part of
○ Comparison with random graphs

14. User A : [Titanic, Braveheart]
User B: [Braveheart, Star Wars]
User C: [Star Wars, Star Trek]
Similarity: Jaccard similarity
Sim(A,B) = ⅓ Sim(A,C) = 0 Sim(B,C) = ⅓
Measures of locality: Similarity

15. Measures of locality: Sparsity
Measured by the density of the user-item matrix
Density = 6/12 = 0.5
Titanic Braveheart Star Wars Star Trek
User A 1 1 - -
User B - 1 1 -
User C - - 1 1

16. Evidence of locality for all three
domains.
Hashtags show higher locality than artists or
movies on Facebook.

17. Measures of locality: Item coverage
Uncovered Ego: Percentage of ego networks
that do not contain a given item.
Random Item/Ego: Compare uncovered ego
of given network with a network constructed by
randomizing the item likes between users.
Random Friend/Ego: Compare uncovered ego
of given network with a network constructed by
randomizing a user’s friends.

18. Hashtags have highest locality.The metrics are
divided between artists and movies on
Facebook.
Similar locality results for item
coverage-based metrics

19. So far, we have seen aggregate
metrics.
How does locality perform on predicting
each user’s preference?
Consider a 70-30 split between train and test.
Two sets of data:
● One using only friends (Friends / Local)
● One using whole network except friends (Non-Friends /
Global)
Algorithms: k-nn, matrix factorization
Evaluated on NDCG metric, widely used in IR and
recommender systems.

20. k-nn similarity is higher for non-
friends than friends

21. k-nn recommender based on friends
outperforms or is comparable to those
using non-friends
Number of
friends ~100-
500
Number of
non-friends
~50k

22. NDCG for 50-nn using friends, non-friends and the full
network. Recommendations from friends are are still
comparable to those from the full network.
Typical use case: Using friends +
non-friends
Useful for recommender systems are exposed only
egocentric slices of the network (e.g. through third
party APIs of Facebook and Twitter)

23. Part II: How social
processes work to
influence our preferences
A specific example: Social explanations
on the web
WWW 2013

24. A specific influence process: Social explanations for a
recommendation.

25. A specific influence process: Social explanations for a
recommendation.
Amit Sharma rated it 5/5!

26. How explanation strategies serve as
proxies for social processes
Overall
Popularity
(OVP)
Social Process: Proof

27. Count of
Friends
(CFR)
How explanation strategies serve as
proxies for social processes
Social Process: Conformity

28. Social Process: Influence
Random Friend
(RFR)
or
Good Friend
(GFR)
How explanation strategies serve as
proxies for social processes

29. Good Friend &
Count
(GCFR)
Social Process: Conformity and Influence
How explanation strategies serve as
proxies for social processes

30. A user study (N = 237)
● Within-subjects design.
● Musical artists recommendation. Chose
artists which users were not aware of.
● Participants were exposed to
recommendation accompanied by different
explanation strategies.
● Each participant rated a maximum of 30
recommendations.
● At the end, participants also answered a
questionnaire.

31. Example Interface
Pink Floyd
+
Social
Explanation
How likely are you
to check out this
artist?
Likelihood Rating
(0-10 Likert)
=
PHASE I

32. Different strategies lead to different
ratings

33. More insights into people's rating
decisions
Showing the right
friend matters
Popularity matters
only if people
identify with the
crowd
People are
differently
susceptible to
explanation
“I found it most powerful when I
could see what friend likes the
artist. I know what kind of music
my friends listen to and that helps
me know if I would like the artist
or not."
“If it was a friend that
I did not think I would have
similarly music taste too, then
I immediately ruled the artist
out...”

34. "The recommendations that
were most convincing to me
were the ones that
displayed that a decent number
of my friends listened to or
liked the artist. I often like to
hear my friends’ feedback on
certain artists..."
"Me and my friends’ music tastes
rarely match up, so I’ve learned
to not care about what music my
friends like."
More insights into people's rating
decisions
Showing the right
friend matters
Popularity matters
only if people
identify with the
crowd
People are
differently
susceptible to
explanation

35. More insights into people's rating
decisions
Showing the right
friend matters
Popularity matters
only if people
identify with the
crowd
People are
differently
susceptible to
explanation

36. More insights into people's rating
decisions
Showing the right
friend matters
Popularity matters
only if people
identify with the
crowd
People are
differently
susceptible to
explanation

37. Social explanation is a secondary
effect
"The albums with the most interesting picture, or interesting
name, with a lot of likes. If the name struck me, such as
‘Formidable Joy’, I found myself wondering more.
If a lot of my friends liked it, it must be good!"

38. Based on a combination of these two decision processes, a
user evaluates a recommendation.
Pink Floyd User's receptiveness
to an explanation.
[Effect of
Explanation]
User's discernment in
music.
[Base Decision Process]
Amit Sharma likes
Pink Floyd.
Modeling the effect of explanations

39. Base Decision Process
f(x) = A e-Ax
A generative process of influence for
explanations
A: Discernment

40. Base Decision Process
f(x) = A e-Ax
A generative process of influence for
explanations
A: Discernment
Effect of Explanations mu : Receptivity
sigma: Variability

41. Base Decision Process
f(x) = A e-Ax
A generative process of influence for
explanations
A: Discernment
Effect of Explanations
Mixture Model
h(x) = a f(x) + (1-a) g(x) a : Rigidness
mu : Receptivity
sigma: Variability

42. Good Friend strategies show lowest rigidness.
Why is this a likely model? All models show same
discernment ~0.4

43. ● The effect of social explanation varies with
different strategies and different people. Can
be used for personalized explanations.
● Explicitly named friends (influence) more
impactful than count of friends (conformity).
● Still, aggregate effects can be modelled. A
generative model gives us a window into
people’s decision process.
Findings

44. Part III: How people
choose items to share
Role of own versus others’ preferences

45. Sharing is common, and often directed to individuals.

46. What is the role of people’s
preferences in sharing?
Past research shows that when broadcasting, people tend
to share only highly liked items [Sharma and Cosley ‘11,
Naaman et al. ‘10]
A lot of sharing still directed at specific people. How do
people choose items to share directly with a recipient?
● Altruism suggests that people will share what they
expect the recipient to like
● Individuation suggests that people will share what they
like themselves
Where does the balance lie, and how can we model it?

47. A paired study (N=87 pairs)
● Facebook users invite a
friend to take part in the study
● See identical
recommendations sourced
from each users’ movie Likes
● Recommended can be rated
and/or shared with the
partner
● To control for social
influence, users do not know
which items were shared to
them

48. Three groups of participants
● Both_shown: Pairs who saw a mix of recommendations
personalized on both partners’ Likes
○ Own_algo: Personalized for partner A
○ Other_algo: Personalized for partner B
● Own_shown: People who saw only recommendations
personalized for them
● Other_shown: People who saw only recommendations
personalized for their partner
Partners of Own_shown are in Other_shown and vice-
versa.

49. Shared items are rated higher by
senders than non-shared items

50. Senders have higher ratings for
shared items than recipients

51. Using people’s preferences for
predicting shares

52. Individuation seems to dominate, but still
participants claimed they were
personalizing for the recipient
“Usually when I suggest, it depends on the item, not
the target individual, because I want to share what I
enjoyed.” [P8]
“I make suggestions to people if I think they might gain
enjoyment. Obviously it really depends on their personality
and their likes/dislikes.” [P22]

53. Preference-Salience model of
sharing
People do not really try to balance individuation and
altruism when they share items.
Rather, they share based on their preference for items and
what is salient to them at the moment. Recipient help
decide whether to share an item or not.
Alternative hypotheses:
High Quality Sharers: Shared items not significantly higher
rated on IMDB than non-shared items.
Misguided Altruists: Shared items have consistently higher
rating by the senders.

54. People’s decisions on items depend on both
preferences and social factors.
Requires mixed methods approach (Data mining +
online experiments).
Models of people’s decision processes can predict what
items are more likely to be adopted or shared.
thank you
Amit Sharma
Dept. of Computer Science
Cornell University
http://www.cs.cornell.edu/~asharma/
@amt_shrma