attention and bias in social information networks

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ATTENTION AND BIAS IN SOCIAL INFORMATION NETWORKS SCOTT COUNTS, MICROSOFT RESEARCH

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Talk at Workshop on Information Neworks, NYU Stern, 2011

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Page 1: Attention and Bias in Social Information Networks

ATTENTION AND BIAS IN SOCIAL INFORMATION NETWORKSSCOTT COUNTS, MICROSOFT RESEARCH

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flickr: alshepmcr

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Looking time per tweet is short, memory is poor.

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Looking time per tweet is short, memory is poor.

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Looking time per tweet is short, memory is poor.

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Including links, RTs, heavy tweeting all decrease attention and/or interest.

Page 8: Attention and Bias in Social Information Networks

Including links, RTs, heavy tweeting all decrease attention and/or interest.

Page 9: Attention and Bias in Social Information Networks

Including links, RTs, heavy tweeting all decrease attention and/or interest.

Page 10: Attention and Bias in Social Information Networks

Including links, RTs, heavy tweeting all decrease attention and/or interest.

Page 11: Attention and Bias in Social Information Networks

Personal contacts increase attention and memory.

Counts, S., & Fisher, K. (2011). Taking It All In? Visual Attention in Microblog Consumption. In Proc. ICWSM ‘11.

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PROBLEM STATEMENT

How does a user’s name influence perception of her and her content?

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ANONYMOUS SURVEY SCREEN

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NON-ANONYMOUS SURVEY SCREEN

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RESULTS – AUTHOR RATINGS

Fairly bimodal distributionsDownward shift in ratings when non-anonymous

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RESULTS – RATING DISTRIBUTION

Good author get higher ratings when non-anon.Bad authors hurt most by namesAverage authors similar to good (KL div = .02) but hurt by name (KL div = .23; p < .001)

Page 18: Attention and Bias in Social Information Networks

RESULTS – RATINGS & FOLLOWER COUNT

Results tighten up with names: R2 = .16 -> .21High follower count people get biggest boostMiddle group hurt

Pal, A., & Counts, S. (2011). What’s In a @Name? How Name Value Biases Judgment of Microblog Authors. In Proc. ICWSM ‘11.

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CREDIBILITY AND TRUTH

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CREDIBILITY AND TRUTH

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CREDIBILITY AND TRUTH

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CREDIBILITY AND TRUTH

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CREDIBILITY AND TRUTH

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CREDIBILITY AND TRUTH*

Name type impacts tweet and author credibilityCorrelations between truth and tweet (r = .39) and author (r = .29) modest

* Morris, M., Counts, S., Roseway, A., Hoff, A., & Schwartz, J. (2011). Under review.

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BRINGING IT TOGETHER

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BRINGING IT TOGETHER

Minimal visual processing/attention

Poor memory encoding

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BRINGING IT TOGETHER

Minimal visual processing/attention

Poor memory encoding

Difficulty in determining truthfulness

Systematic use of heuristics (biases)

Friends

Name value

Page 28: Attention and Bias in Social Information Networks

BRINGING IT TOGETHER

Minimal visual processing/attention

Poor memory encoding

Difficulty in determining truthfulness

Systematic use of heuristics (biases)

Friends

Name value

** Peripheral processing route **

Page 29: Attention and Bias in Social Information Networks

IMPLICATIONS

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IMPLICATIONS

Effective reach of social media

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IMPLICATIONS

Effective reach of social media

Information diffusion

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IMPLICATIONS

Effective reach of social media

Information diffusion

Social contagion: Stickiness* (increased adoption and sustained product use) and memory for content

* Aral, S., & Walker, D. (2010). Creating Social Contagion Through Viral Product Design: A Randomized Trial of Peer Influence Networks. Management Science.

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ATTENTION AND BIAS IN SOCIAL INFORMATION NETWORKS

SCOTT COUNTS, MICROSOFT RESEARCH

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low level :: your brain on facebook*

* Fisher, K., & Counts, S. (2010). Your Brain on Facebook: Neuropsychological Associations with Social Versus Other Media. In Proc. ICWSM ‘10.

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social information networks :: levels of analysis

Math/Theory

Social media analytics

Computer-Mediated Communication

Social Cognition

Physiological

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RESULTS – FACTORS FOR BIAS: GENDER

Most top authors are gender neutral (e.g., Time, Mashable)Men higher than women when anonymous, but drop more when names shownWomen get slight bump when names shown

Pal, A., & Counts, S. (2011). What’s In a @Name? How Name Value Biases Judgment of Microblog Authors. In Proc. ICWSM ‘11.

Page 37: Attention and Bias in Social Information Networks

social information networks :: levels of analysis

Math/Theory

Social media analytics

Computer-Mediated Communication

Social Cognition

Physiological

Page 38: Attention and Bias in Social Information Networks

PROBLEM STATEMENT

How does a user’s name influence perception of her and her content?

Page 39: Attention and Bias in Social Information Networks

PROBLEM STATEMENT

How does a user’s name influence perception of her and her content?