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Attention and Bias in Social Information Networks
Attention and Bias in Social Information Networks
Attention and Bias in Social Information Networks
Attention and Bias in Social Information Networks
Attention and Bias in Social Information Networks
Attention and Bias in Social Information Networks
Attention and Bias in Social Information Networks
Attention and Bias in Social Information Networks
Attention and Bias in Social Information Networks
Attention and Bias in Social Information Networks
Attention and Bias in Social Information Networks
Attention and Bias in Social Information Networks
Attention and Bias in Social Information Networks
Attention and Bias in Social Information Networks
Attention and Bias in Social Information Networks
Attention and Bias in Social Information Networks
Attention and Bias in Social Information Networks
Attention and Bias in Social Information Networks
Attention and Bias in Social Information Networks
Attention and Bias in Social Information Networks
Attention and Bias in Social Information Networks
Attention and Bias in Social Information Networks
Attention and Bias in Social Information Networks
Attention and Bias in Social Information Networks
Attention and Bias in Social Information Networks
Attention and Bias in Social Information Networks
Attention and Bias in Social Information Networks
Attention and Bias in Social Information Networks
Attention and Bias in Social Information Networks
Attention and Bias in Social Information Networks
Attention and Bias in Social Information Networks
Attention and Bias in Social Information Networks
Attention and Bias in Social Information Networks
Attention and Bias in Social Information Networks
Attention and Bias in Social Information Networks
Attention and Bias in Social Information Networks
Attention and Bias in Social Information Networks
Attention and Bias in Social Information Networks
Attention and Bias in Social Information Networks
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Attention and Bias in Social Information Networks

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Talk at Workshop on Information Neworks, NYU Stern, 2011

Talk at Workshop on Information Neworks, NYU Stern, 2011

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  • alshepmcr
  • Transcript

    • 1. Attention and bias in social information networks
      Scott counts, microsoft research
    • 2. flickr: alshepmcr
    • 3.
    • 4. Looking time per tweet is short, memory is poor.
    • 5. Looking time per tweet is short, memory is poor.
    • 6. Looking time per tweet is short, memory is poor.
    • 7. Including links, RTs, heavy tweeting all decrease attention and/or interest.
    • 8. Including links, RTs, heavy tweeting all decrease attention and/or interest.
    • 9. Including links, RTs, heavy tweeting all decrease attention and/or interest.
    • 10. Including links, RTs, heavy tweeting all decrease attention and/or interest.
    • 11. Personal contacts increase attention and memory.
      Counts, S., & Fisher, K. (2011). Taking It All In? Visual Attention in Microblog Consumption. In Proc. ICWSM ‘11.
    • 12.
    • 13. Problem statement
      How does a user’s name influence perception of her and her content?
    • 14. Anonymous survey screen
    • 15. Non-Anonymous survey screen
    • 16. Results – author ratings
      Fairly bimodal distributions
      Downward shift in ratings when non-anonymous
    • 17. Results – rating distribution
      Good author get higher ratings when non-anon.
      Bad authors hurt most by names
      Average authors similar to good (KL div = .02) but hurt by name (KL div = .23; p < .001)
    • 18. Results – ratings & follower count
      Results tighten up with names: R2 = .16 -> .21
      High follower count people get biggest boost
      Middle group hurt
      Pal, A., & Counts, S. (2011). What’s In a @Name? How Name Value Biases Judgment of Microblog Authors. In Proc. ICWSM ‘11.
    • 19. Credibility and truth
    • 20. Credibility and truth
    • 21. Credibility and truth
    • 22. Credibility and truth
    • 23. Credibility and truth
    • 24. Credibility and truth*
      Name type impacts tweet and author credibility
      Correlations between truth and tweet (r = .39) and author (r = .29) modest
      * Morris, M., Counts, S., Roseway, A., Hoff, A., & Schwartz, J. (2011). Under review.
    • 25. Bringing it together
    • 26. Bringing it together
      Minimal visual processing/attention
      Poor memory encoding
    • 27. Bringing it together
      Minimal visual processing/attention
      Poor memory encoding
      Difficulty in determining truthfulness
      Systematic use of heuristics (biases)
      Friends
      Name value
    • 28. 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 **
    • 29. Implications
    • 30. Implications
      Effective reach of social media
    • 31. Implications
      Effective reach of social media
      Information diffusion
    • 32. 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.
    • 33. Attention and bias in social information networks
      Scott counts, microsoft research
    • 34. 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.
    • 35. social information networks :: levels of analysis
      Math/Theory
      Social media analytics
      Computer-Mediated Communication
      Social Cognition
      Physiological
    • 36. 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 shown
      Women 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.
    • 37. social information networks :: levels of analysis
      Math/Theory
      Social media analytics
      Computer-Mediated Communication
      Social Cognition
      Physiological
    • 38. Problem statement
      How does a user’s name influence perception of her and her content?
    • 39. Problem statement
      How does a user’s name influence perception of her and her content?

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