Conference Paper: “Quantifying Search Bias: Investigating Sources of Bias for Political Searches in Social Media”

The following full text paper was recently shared on arXiv. It was presented at the ACM Conference on Computer Supported Cooperative Work & Social Computing (CSCW) in Portland, Oregon (February 2017).
Title
Quantifying Search Bias: Investigating Sources of Bias for Political Searches in Social Media
Authors
Juhi Kulshrestha
Max Planck Institute for Software Systems: MPI SWS
Motahhare Eslami
University of Illinois at Urbana-Champaign
Johnnatan Messias
Max Planck Institute for Software Systems: MPI SWS
Muhammad Bilal Zafar
Max Planck Institute for Software Systems: MPI SWS
Saptarshi Ghosh
Max Planck Institute for Software Systems: MPI SWS
Krishna P. Gummadi
Indian Institute of Engineering Science and Technology, Shibpur
Karrie Karahalios
University of Illinois at Urbana-Champaign
Adobe Research
Source
via arXiv
Article also published in Proceedings of ACM Conference on Computer Supported Cooperative Work & Social Computing (CSCW), Portland, USA, February 2017.
Abstract

Search systems in online social media sites are frequently used to find information about ongoing events and people.
For topics with multiple competing perspectives, such as political events or political candidates, bias in the top ranked results significantly shapes public opinion. However, bias does not emerge from an algorithm alone.
It is important to distinguish between the bias that arises from the data that serves as the input to the ranking system and the bias that arises from the ranking system itself. In this paper, we propose a framework to quantify these distinct biases and apply this framework to politics-related queries on Twitter.
We found that both the input data and the ranking system contribute significantly to produce varying amounts of bias in the search results and in different ways. We discuss the consequences of these biases and possible mechanisms to signal this bias in social media search systems’ interfaces.

Direct to Full Text Article (16 pages; PDF)

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