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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
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    • 1. Attention and bias in social information networks<br />Scott counts, microsoft research<br />
    • 2. flickr: alshepmcr<br />
    • 3.
    • 4. Looking time per tweet is short, memory is poor.<br />
    • 5. Looking time per tweet is short, memory is poor.<br />
    • 6. Looking time per tweet is short, memory is poor.<br />
    • 7. Including links, RTs, heavy tweeting all decrease attention and/or interest.<br />
    • 8. Including links, RTs, heavy tweeting all decrease attention and/or interest.<br />
    • 9. Including links, RTs, heavy tweeting all decrease attention and/or interest.<br />
    • 10. Including links, RTs, heavy tweeting all decrease attention and/or interest.<br />
    • 11. Personal contacts increase attention and memory.<br />Counts, S., &amp; Fisher, K. (2011). Taking It All In? Visual Attention in Microblog Consumption. In Proc. ICWSM ‘11.<br />
    • 12.
    • 13. Problem statement<br />How does a user’s name influence perception of her and her content?<br />
    • 14. Anonymous survey screen<br />
    • 15. Non-Anonymous survey screen<br />
    • 16. Results – author ratings<br />Fairly bimodal distributions<br />Downward shift in ratings when non-anonymous<br />
    • 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 &lt; .001)<br />
    • 18. Results – ratings &amp; follower count<br />Results tighten up with names: R2 = .16 -&gt; .21<br />High follower count people get biggest boost<br />Middle group hurt<br />Pal, A., &amp; Counts, S. (2011). What’s In a @Name? How Name Value Biases Judgment of Microblog Authors. In Proc. ICWSM ‘11.<br />
    • 19. Credibility and truth<br />
    • 20. Credibility and truth<br />
    • 21. Credibility and truth<br />
    • 22. Credibility and truth<br />
    • 23. Credibility and truth<br />
    • 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., &amp; Schwartz, J. (2011). Under review.<br />
    • 25. Bringing it together<br />
    • 26. Bringing it together<br />Minimal visual processing/attention<br />Poor memory encoding<br />
    • 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. 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. Implications<br />
    • 30. Implications<br />Effective reach of social media <br />
    • 31. Implications<br />Effective reach of social media <br />Information diffusion<br />
    • 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., &amp; Walker, D. (2010). Creating Social Contagion Through Viral Product Design: A Randomized Trial of Peer Influence Networks. Management Science.<br />
    • 33. Attention and bias in social information networks<br />Scott counts, microsoft research<br />
    • 34. low level :: your brain on facebook*<br />* Fisher, K., &amp; Counts, S. (2010). Your Brain on Facebook: Neuropsychological Associations with Social Versus Other Media. In Proc. ICWSM ‘10.<br />
    • 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. 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., &amp; Counts, S. (2011). What’s In a @Name? How Name Value Biases Judgment of Microblog Authors. In Proc. ICWSM ‘11.<br />
    • 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. Problem statement<br />How does a user’s name influence perception of her and her content?<br />
    • 39. Problem statement<br />How does a user’s name influence perception of her and her content?<br />

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