Talk at the Second symposium on Biases in Human Computation and Crowdsourcing. Cyprus (online).

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Investigating the Influence of
Crowdworker Attitudes on
Document Annotations
Tim Draws, Nava Tintarev, Ujwal Gadiraju
TU Delft, The Netherlands
t.a.draws@tudelft.nl
timdraws.net

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Biases in web search
My work: measuring and mitigating algorithmic
and cognitive biases in the context of web
search on debated topics
Needed: data sets of search results with
viewpoint annotations
Yes!
Yes!
Yes!
Yes!
Yes!
No!
No!
Problem: potential bias in the viewpoint
annotations due to crowdworkers’ personal
attitudes

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Biased annotations?
Concern: tendency to annotate in line with personal stance
– Confirmation bias
– False consensus effect
Opposing!
Supporting!

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This work
RQ: Do crowdworkers have a tendency to label in
line with their personal attitude when annotating
search results for viewpoints?
Crowdsourcing study to collect viewpoint
annotations
Analyzed the relationship between crowdworker
attitudes and their annotations

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Crowdsourcing viewpoints
• Retrieved search results from Bing for two
different debated topics
– Should zoos exist?
– Are social networking sites good for our society?
• Top 50 results for 14 queries per topic
• Set up task on Amazon Mechanical Turk

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Viewpoint labels
Should we all be vegan?
Extremely
opposing
Opposing Somewhat
opposing
Neutral Somewhat
supporting
Supporting Extremely
supporting
-3 -2 -1 0 +1 +2 +3

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Viewpoint annotation task
• Step 1. Instructions; personal knowledge & attitude
• Step 2. Annotate 14 search results on one topic and
complete two attention checks

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Results
Descriptive
• 717 search result items
• 140 annotators
Spearman correlation analysis
• IV: Crowdworker attitude [-3,3]
• DV: Mean annotation [-3,3]
• ρ = 0.26, p = 0.003
−2
0
2
−2 0 2
Crowdworker Attitude
MeanAnnotation

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A difference between topics?
−2
0
2
−2 0 2
Crowdworker Attitude
MeanAnnotation
−2
0
2
−2 0 2
Crowdworker Attitude
MeanAnnotation
Social Media Zoos
ρ = 0.26, p = 0.025 ρ = 0.27, p = 0.041

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Mild vs. strong attitudes
• Divided crowdworkers into mild and strong attitudes
• Mild attitudes: ρ = -0.03, p = 0.829
• Strong attitudes: ρ = 0.26, p = 0.035
Extremely
opposing
Opposing Somewhat
opposing
Neutral Somewhat
supporting
Supporting Extremely
supporting
-3 -2 -1 0 +1 +2 +3
mildstrong strong

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Worker requirements too low?
• Worker requirements
– HIT approval rate > 95%
– Location: United States
• Second study; higher worker requirements
– HIT approval rate > 98%
– Location: US, AUS, NZ, UK, GER, FIN, SWE, NO, CH, AUT
– Master workers
• Comparing subsets of overlapping items (112)
– Only difference was requirements
– Subset of first study: ρ = 0.22, p = 0.022 (n = 114)
– Subset of second study: ρ = 0.06, p = 0.77 (n = 25)

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Discussion
• Crowdworker attitudes affected viewpoint annotations
– Irrespective of topic
– Workers with stronger opinions and lower qualifications seem to be more
prone to bias
• Other considerations
– Influence of self-reported knowledge?
– Asking workers for sincerity?
– Type of document could also play a role (more ambiguous, more bias?)
• Future work
– What specifically causes the bias?
– Mitigation strategies

13. 13
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Take home and future work
• Cognitive biases can affect (viewpoint)
annotations of crowdworkers
– Be aware (test!)
– Design task to make crowdworkers aware of biases
– If possible, remove ambiguous items
Material related to this research
available at https://osf.io/kbjgp/.
t.a.draws@tudelft.nl
timdraws.net