Attention and Bias in Social Information Networks

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Talk at Workshop on Information Neworks, NYU Stern, 2011

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  • alshepmcr
  • Attention and Bias in Social Information Networks

    1. 1. Attention and bias in social information networks<br />Scott counts, microsoft research<br />
    2. 2. flickr: alshepmcr<br />
    3. 3.
    4. 4. Looking time per tweet is short, memory is poor.<br />
    5. 5. Looking time per tweet is short, memory is poor.<br />
    6. 6. Looking time per tweet is short, memory is poor.<br />
    7. 7. Including links, RTs, heavy tweeting all decrease attention and/or interest.<br />
    8. 8. Including links, RTs, heavy tweeting all decrease attention and/or interest.<br />
    9. 9. Including links, RTs, heavy tweeting all decrease attention and/or interest.<br />
    10. 10. Including links, RTs, heavy tweeting all decrease attention and/or interest.<br />
    11. 11. Personal contacts increase attention and memory.<br />Counts, S., & Fisher, K. (2011). Taking It All In? Visual Attention in Microblog Consumption. In Proc. ICWSM ‘11.<br />
    12. 12.
    13. 13. Problem statement<br />How does a user’s name influence perception of her and her content?<br />
    14. 14. Anonymous survey screen<br />
    15. 15. Non-Anonymous survey screen<br />
    16. 16. Results – author ratings<br />Fairly bimodal distributions<br />Downward shift in ratings when non-anonymous<br />
    17. 17. Results – rating distribution<br />Good author get higher ratings when non-anon.<br />Bad authors hurt most by names<br />Average authors similar to good (KL div = .02) but hurt by name (KL div = .23; p < .001)<br />
    18. 18. Results – ratings & follower count<br />Results tighten up with names: R2 = .16 -> .21<br />High follower count people get biggest boost<br />Middle group hurt<br />Pal, A., & Counts, S. (2011). What’s In a @Name? How Name Value Biases Judgment of Microblog Authors. In Proc. ICWSM ‘11.<br />
    19. 19. Credibility and truth<br />
    20. 20. Credibility and truth<br />
    21. 21. Credibility and truth<br />
    22. 22. Credibility and truth<br />
    23. 23. Credibility and truth<br />
    24. 24. Credibility and truth*<br />Name type impacts tweet and author credibility<br />Correlations between truth and tweet (r = .39) and author (r = .29) modest<br />* Morris, M., Counts, S., Roseway, A., Hoff, A., & Schwartz, J. (2011). Under review.<br />
    25. 25. Bringing it together<br />
    26. 26. Bringing it together<br />Minimal visual processing/attention<br />Poor memory encoding<br />
    27. 27. Bringing it together<br />Minimal visual processing/attention<br />Poor memory encoding<br />Difficulty in determining truthfulness<br />Systematic use of heuristics (biases)<br />Friends<br />Name value<br />
    28. 28. Bringing it together<br />Minimal visual processing/attention<br />Poor memory encoding<br />Difficulty in determining truthfulness<br />Systematic use of heuristics (biases)<br />Friends<br />Name value<br />** Peripheral processing route **<br />
    29. 29. Implications<br />
    30. 30. Implications<br />Effective reach of social media <br />
    31. 31. Implications<br />Effective reach of social media <br />Information diffusion<br />
    32. 32. Implications<br />Effective reach of social media <br />Information diffusion <br />Social contagion: Stickiness* (increased adoption and sustained product use) and memory for content<br />* Aral, S., & Walker, D. (2010). Creating Social Contagion Through Viral Product Design: A Randomized Trial of Peer Influence Networks. Management Science.<br />
    33. 33. Attention and bias in social information networks<br />Scott counts, microsoft research<br />
    34. 34. low level :: your brain on facebook*<br />* Fisher, K., & Counts, S. (2010). Your Brain on Facebook: Neuropsychological Associations with Social Versus Other Media. In Proc. ICWSM ‘10.<br />
    35. 35. social information networks :: levels of analysis<br />Math/Theory<br />Social media analytics<br />Computer-Mediated Communication<br />Social Cognition<br />Physiological<br />
    36. 36. Results – factors for bias: gender<br />Most top authors are gender neutral (e.g., Time, Mashable)<br />Men higher than women when anonymous, but drop more when names shown<br />Women get slight bump when names shown<br />Pal, A., & Counts, S. (2011). What’s In a @Name? How Name Value Biases Judgment of Microblog Authors. In Proc. ICWSM ‘11.<br />
    37. 37. social information networks :: levels of analysis<br />Math/Theory<br />Social media analytics<br />Computer-Mediated Communication<br />Social Cognition<br />Physiological<br />
    38. 38. Problem statement<br />How does a user’s name influence perception of her and her content?<br />
    39. 39. Problem statement<br />How does a user’s name influence perception of her and her content?<br />

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