1) The document discusses several research papers on crowdsourcing and targeted crowdsourcing. 2) It describes systems like Quizz and VizWiz that gather crowdsourced data or answers to questions. 3) The papers explore methods to optimize crowdsourcing by targeting the right groups of workers, discovering minority viewpoints, modeling truth from crowdsourced data, and designing advertising to attract users.

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-tap to take a photo.
-tap to begin recording
your question and again to stop. side,
User ?
Web Server -
Database -
Local Client
Remote Services and Worker Interface
J. Bigham et al.: VizWiz: Nearly real-time answers to visual questions, In UIST, 2010.

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8. Quizz: Targeted Crowdsourcing with a
Billion (Potential) Users (WWW’14)
Panagiotis G. Ipeirotis (Goodle, NYU)
Evgeniy Gabrilovich (Google)

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Figure 3: Example ad to attract users
time a user clicks on the a
we record a conversion ev
the advertising system. T
the system to optimize th
mizing the number of con
contribution yield, instead
the number of clicks.
Although optimizing fo
Figure 1: Screenshot of the Quizz system.
healthline

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𝑢 𝑞
(𝐻 1/𝑛, 𝑛 = log ( 𝑛), 𝐻 1, 𝑛 = 0
IG(✓u, nq) = H(1/nq, nq) H(✓u, nq)

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Pr [qu] ⇠ Beta (au + 1, bu + 1)

12. The Wisdom of Minority: Discovering and
Targeting the Right Group of Workers for
Crowdsourcing (WWW’14)
Hongwei Li (UC Berkeley)
Bo Zhao (Microsoft Research)
Ariel Fuxman (Microsoft Research)

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xu = (xu1, xu2, . . . , xun)
⌧u = IG(✓u, n)
⌧u ⇠ N w>
x,

15. FaitCrowd: Fine Grained Truth Discovery
for Crowdsourced Data Aggregation
(KDD’15)
Fenglong Ma (SUNY Buffalo) et al.

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18. Pr [yuq = tq] =
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✓uzq
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