Modeling the Connection between People’s

Preferences and Content Sharing

Amit Sharma* and Dan Cosley

Cornell University

@amt_shrma

CSCW 2015

Popular ways to share

Directed sharing

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Directed sharing: Questions

Why did she share that item?

Does she like it? Will he like it?

Can we predict what items she will share to him?3

Two motivations for sharing

Word-of-mouth

Individuation• Establish a distinct

identity for oneself

Altruism• Help others

[Dempsey et al. 2010]

Online Content sharing

Sender’s preferences• Sender shares what she

likes

Recipient’s preferences• Sender shares what

recipient would like

Comparing sender’s rating versus recipient’s rating for a shared item can indicate the relative effect of these

motivations. 4

Directed sharing: More altruism?

• Meformers versus informers: ~80% of content shared on Twitter was about the user [Naaman et al. 2008]

• In directed sharing, there is a known recipient• Expect altruism to be more important

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Research questions

• RQ1: To what extent do people tend to share items that they like themselves (individuation) versus those that they perceive to be relevant for the recipient (altruism)?

• RQ2: Can we predict whether an item is shared based on sender’s and recipient’s preferences?

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Person A’s movie Likes

Compute recs.

Person B’smovie Likes

Compute recs.

Combine recs.

A paired experiment on Facebook

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10 Recs.

forme

10 Recs.

for partner

To mitigate social influence effects, my partner is not shown which movies were shared by me.

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Rating more frequent than sharing

118 participants rated, 86 shared at least once.

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People share what they like themselves

Rating by Senders

Fre

qu

en

cy

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Senders rate shared items higher than recipients

Mean sender rating: 4.19Mean recipient rating: 3.88

(Paired t-test)

Sender Rating – Recipient Rating

Fre

qu

en

cy

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Responses support individuation

“Usually when I suggest, it depends on the item, not the target individual, because I want to share what I enjoyed.” (P8)

“I suggest because I like something and I want to see if other people feel the same way about an item.” (P91)

Altruism:

“I make suggestions to people if I think they might gain enjoyment. Obviously it really depends on their personality and their likes/dislikes.” (P22)

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Data from people who did not see all recommendations• Due to lack of Like data or API errors.

Recs. forme

Recs. for

partner

Recs. forme

Recs. for

partner

Both-Shown Other-ShownOwn-Shown

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Ratings for shared items depend on item set shown• Own-Shown: Ratings for shared items by senders

are significantly higher than those by recipients.

• Other-Shown: Ratings for shared items by senders are still high, but recipients ratings are comparable.

• Both-Shown: Same effect when divide items shown to Both-shown participants by the underlying algorithm.

Salience of items impacts what gets shared.

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A preference-salience model

“I try to assess if the individual that I am recommending to would like the movie that I am suggesting. Otherwise, I do not tell them about the movie, and may think of someone else who would like the movie.” (P5)

People’s own preferences determine shareable items.

Among these candidates, some become salient based on the context.

They are shared if sharer thinks they are suitable for the recipient.

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Other plausible models

High Quality Model• No difference between overall IMDB ratings for shared

and non-shared movies.

Misguided Altruism Model• Senders’ ratings are higher for shares than non-shares.

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RQ2: Can we predict what is shared?• Classification task: Given a sender, recipient and an

item, decide whether it was shared or not.

• Features:• IMDB average rating, popularity for item• Recipient’s predicted rating for item• Sender’s predicted rating for item• Sender’s sharing promiscuity

• Randomly sampled an equal number of non-shares. Use 10-fold cross validation and a decision tree classifier.

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Evaluation metrics

Precision• Percentage of items returned by model that were

actually shared

Recall• Percentage of actually shared items that were returned

by model

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Better precision with sender-based features

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IMDB Rating Popularity Recipient-ItemRating

Sender-ItemRating

All

Precision Recall 19

Design implications

Recommender systems for effective sharing• Recommending what to share, who to share it to.

E.g., Feedme system [Bernstein et al. 2010]

Diffusion models with directed sharing• Accounting for sender and recipient preferences

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Conclusions

• RQ1: Individuation (personal preferences) dominate the decision process for directed content sharing.

• RQ2: Based on sender and recipient preferences, we can (noisily) predict what is shared.

thank you!@amt_shrma

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Better prediction with sender-based features

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“something I really, really enjoy”

sharing too frequently “tends to water down my stamp of approval”

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