This document discusses research on collective attention and information diffusion on social media. It references studies that examined how topics trend on blogs, Twitter, and other social networks. Other research analyzed how the diversity of ideas shared on social networks relates to election outcomes. Additional sections discuss how moral values are expressed differently online versus offline, and how artificial intelligence could help address challenges around misinformation spread through social technologies. The document concludes by noting more work is still needed to understand social dynamics at large scales.

1. JST
sasahara@nagoya‑u.jp

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4. Collective Attention (Sasahara et al. PLoS ONE, 2013)
IC S 2015
(cf. , 30(6), 2015)
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5. IC S 2015
Courtesy of Prof. Fortunato
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6. blog
Lazer et al. Science, 2009
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7. Twitter
Golder and Macy. Science, 2011
cf. Pulse of the Nation
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8. Schich et al. Science, 2014
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9. ×
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11. Figs: https://theconversation.com/misinformation‑on‑social‑media‑can‑
technology‑save‑us‑69264
Botometer
Hoaxy
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12. Qiu et al. Nat. Hum. Behav., 2017
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13. /
(McLuhan)
(Dunber)
150
Dumber, Evol. Anthr., 1998
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14. Twitter
k ~150 (= )
Gonçalves et al. PLoS ONE, 2011
out
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16. SNS
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17. FB
Bakshy et al. Science, 2015
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18. Shi et al, Nat.
Hum. Behav.,
2017
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19. More Tweets, More Votes
?
DiGrazia et al. PLoS ONE, 2013
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20. More Entropy, More Votes
H = − P (X = x) log P (X = x)
, . 2015, 2015
c ∑x∈U c 2 c
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22. (Haidt, 2012)
Fig: http://cct.biola.edu/riding‑moral‑elephant‑review‑jonathan‑haidts‑righteous‑
mind/
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23. (MFQ)
Graham et al., J. Pers. Soc.
Psychol., 2009
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24. MTurk
Dehghani et al. J. Exp. Psychol, ‑.Gen., 2016
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25. Kaur and Sasahara. Proc. of IEEE BigData 2016, 2016
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26. AIoT
Moral Machine (MIT Media Lab)
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Watts.
Pic: https://www.microsoft.com/en‑us/research/people/duncan/
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