TY - GEN
T1 - Quantifying search bias
T2 - 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing, CSCW 2017
AU - Kulshrestha, Juhi
AU - Eslami, Motahhare
AU - Messias, Johnnatan
AU - Zafar, Muhammad Bilal
AU - Ghosh, Saptarshi
AU - Gummadi, Krishna P.
AU - Karahalios, Karrie
N1 - Funding Information:
Acknowledgement. This work was partially supported by the Grant 621-2007-6565 from the Swedish Research Council.
Publisher Copyright:
© 2017 Copyright held by the owner/author(s). Publication rights licensed to ACM.
PY - 2017/2/25
Y1 - 2017/2/25
N2 - 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.
AB - 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.
KW - Political bias inference
KW - Search bias
KW - Search bias quantification
KW - Social media search
KW - Sources of search bias
KW - Twitter
UR - http://www.scopus.com/inward/record.url?scp=85014742889&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85014742889&partnerID=8YFLogxK
U2 - 10.1145/2998181.2998321
DO - 10.1145/2998181.2998321
M3 - Conference contribution
AN - SCOPUS:85014742889
T3 - Proceedings of the ACM Conference on Computer Supported Cooperative Work, CSCW
SP - 417
EP - 432
BT - CSCW 2017 - Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing
PB - Association for Computing Machinery
Y2 - 25 February 2017 through 1 March 2017
ER -