Quantifying search bias: Investigating sources of bias for political searches in social media

Juhi Kulshrestha, Motahhare Eslami, Johnnatan Messias, Muhammad Bilal Zafar, Saptarshi Ghosh, Krishna P. Gummadi, Karrie Karahalios

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationCSCW 2017 - Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing
PublisherAssociation for Computing Machinery
Pages417-432
Number of pages16
ISBN (Electronic)9781450343350
DOIs
StatePublished - Feb 25 2017
Event2017 ACM Conference on Computer Supported Cooperative Work and Social Computing, CSCW 2017 - Portland, United States
Duration: Feb 25 2017Mar 1 2017

Publication series

NameProceedings of the ACM Conference on Computer Supported Cooperative Work, CSCW

Other

Other2017 ACM Conference on Computer Supported Cooperative Work and Social Computing, CSCW 2017
CountryUnited States
CityPortland
Period2/25/173/1/17

Keywords

  • Political bias inference
  • Search bias
  • Search bias quantification
  • Social media search
  • Sources of search bias
  • Twitter

ASJC Scopus subject areas

  • Software
  • Computer Networks and Communications
  • Human-Computer Interaction

Cite this