Date post: | 30-Oct-2014 |
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Preventing Filter Bubbles and Underprovision in Online Communities with Social Curation Algorithms:
Data-driven approaches to measuring “bias”
Jahna Otterbacher Open University Cyprus, Nicosia CYPRUS
Libby Hemphill Illinois Institute of Technology, Chicago USA
Social Curation Algorithms in Online Communities
• Low barriers to entry• Users contribute to a collection of shared content• Users judge the value of content via binary voting• Aggregated votes used in information display(s)
Aarhus University, 3 October 2013
Aarhus University, 3 October 2013
Aarhus University, 3 October 2013
Bias• Content with particular properties systematically ranked
higher/lower than others
• Information display gives users a particular take on “what others think”
• Prominently displayed content is what users see and read• Users often do not change default settings• They place trust in information displays
Aarhus University, 3 October 2013
Gender bias at IMDb
Aarhus University, 3 October 2013
Editing bias at Amazon, IMDb and Yelp
Aarhus University, 3 October 2013
Underprovision problem• When social curation is used:
“too many people rely on others to contribute without doing so themselves.” [Gilbert, 2013]
• Study of Reddit• Most communities suffer from some degree of free riding• At Reddit, users’ contributions being buried led to disincentives for
contributions• “…it’s such an incredible resource when the comments are flowing,
but if your post gets buried for whatever reason, it’s painfully anti-climactic.”
Aarhus University, 3 October 2013
Our perspective• Bias is inevitable and is not necessarily bad
• Presence of bias could be revealed to users
• Research questions• What types of biases may occur? • Under what circumstances?• How can we study bias across systems?
Aarhus University, 3 October 2013
Proposed framework• Find diverse examples of systems• Taxonomy of biases• Participation rates and participant roles• Examine correlations between system/participant
characteristics and observed biases• Generate ideas of how to respond
Aarhus University, 3 October 2013
Aarhus University, 3 October 2013
Bias taxonomy• Contributor characteristics
• Demographics• Level, type of activities• Information disclosure
• Contribution characteristics• Writing style (e.g., narrative/reporting)• Content (e.g., uniqueness/conformity)• Metadata (e.g., time posted)
Aarhus University, 3 October 2013
Participation rates & roles
Aarhus University, 3 October 2013
Correlations• How are system and participant characteristics correlated
to the biases that we observe?
• Are more information displays necessarily better?• Which default display leads to more/less diversity with
respect to a given characteristic of content?
Aarhus University, 3 October 2013
Final thoughts• Can we exploit bias in order to
• Entice users to participate in all activities?• Convince users to question default information displays?
Aarhus University, 3 October 2013