Attention and Bias in Social Information Networks

ATTENTION AND BIAS IN SOCIAL INFORMATION NETWORKSSCOTT COUNTS, MICROSOFT RESEARCH

flickr: alshepmcr

Looking time per tweet is short, memory is poor.

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

Personal contacts increase attention and memory.

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

PROBLEM STATEMENT

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

ANONYMOUS SURVEY SCREEN

NON-ANONYMOUS SURVEY SCREEN

RESULTS – AUTHOR RATINGS

Fairly bimodal distributionsDownward shift in ratings when non-anonymous

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)

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.

CREDIBILITY AND TRUTH

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.

BRINGING IT TOGETHER


Minimal visual processing/attention

Poor memory encoding




Difficulty in determining truthfulness

Systematic use of heuristics (biases)

Friends

Name value




Difficulty in determining truthfulness

Systematic use of heuristics (biases)

Friends

Name value

** Peripheral processing route **

IMPLICATIONS

IMPLICATIONS

Effective reach of social media

IMPLICATIONS


Information diffusion

IMPLICATIONS


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.

ATTENTION AND BIAS IN SOCIAL INFORMATION NETWORKS

SCOTT COUNTS, MICROSOFT RESEARCH

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.

social information networks :: levels of analysis

Math/Theory

Social media analytics

Computer-Mediated Communication

Social Cognition

Physiological

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.

social information networks :: levels of analysis

Math/Theory

Social media analytics

Computer-Mediated Communication

Social Cognition

Physiological

PROBLEM STATEMENT

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

Attention and Bias in Social Information Networks

Technology

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