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Beyond Bias : = Three Papers on Bias in Social Networks and Mitigating Bias in Social Science Research.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Beyond Bias :/
Reminder of title:	Three Papers on Bias in Social Networks and Mitigating Bias in Social Science Research.
Author:	Sachs, Jeffrey K.
Description:	1 online resource (192 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-08, Section: B.
Contained By:	Dissertations Abstracts International85-08B.
Subject:	Sociology. -
Online resource:	click for full text (PQDT)
ISBN:	9798381436648

Beyond Bias : = Three Papers on Bias in Social Networks and Mitigating Bias in Social Science Research.
Sachs, Jeffrey K.

Beyond Bias :Three Papers on Bias in Social Networks and Mitigating Bias in Social Science Research. - 1 online resource (192 pages)

Source: Dissertations Abstracts International, Volume: 85-08, Section: B.

Thesis (Ph.D.)--Yale University, 2023.

Includes bibliographical references

The dissertation consists of three relational-based approaches to analyzing bias in social networks and social data. The first study introduces two group fairness metrics that incorporate network structure, revealing its substantial impact on fairness outcomes, which is often overlooked by traditional metrics. The second study investigates information diffusion processes, highlighting the potential for inequality generated by conventional seeding strategies. It focuses on designing equitable network interventions, considering each group's relative influence. I calibrate the diffusion models to actual diffusion processes to assess various network influence measures and their likelihood of producing equitable outcomes under a set of fairness constraints. The analysis additionally brings to light inherent trade-offs in attaining fairness in information dissemination. The third project introduces a novel approach to sampling social media data that helps mitigate bias when using the sample to estimate population level characteristics. At its core, the approach employs an embedding space to identify a large set of sampling locations orthogonal to a particular topic of interest. Multiple evaluations of the approach show that it effectively mitigates selection bias, resulting in representative samples.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798381436648Subjects--Topical Terms:

551705
Sociology.
Subjects--Index Terms:

BiasIndex Terms--Genre/Form:

554714
Electronic books.

Beyond Bias : = Three Papers on Bias in Social Networks and Mitigating Bias in Social Science Research.
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520 $a The dissertation consists of three relational-based approaches to analyzing bias in social networks and social data. The first study introduces two group fairness metrics that incorporate network structure, revealing its substantial impact on fairness outcomes, which is often overlooked by traditional metrics. The second study investigates information diffusion processes, highlighting the potential for inequality generated by conventional seeding strategies. It focuses on designing equitable network interventions, considering each group's relative influence. I calibrate the diffusion models to actual diffusion processes to assess various network influence measures and their likelihood of producing equitable outcomes under a set of fairness constraints. The analysis additionally brings to light inherent trade-offs in attaining fairness in information dissemination. The third project introduces a novel approach to sampling social media data that helps mitigate bias when using the sample to estimate population level characteristics. At its core, the approach employs an embedding space to identify a large set of sampling locations orthogonal to a particular topic of interest. Multiple evaluations of the approach show that it effectively mitigates selection bias, resulting in representative samples.
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538 $a Mode of access: World Wide Web
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653 $a Social network analysis
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