國立高雄大學圖資館 |

Language: English

Back

Towards Analyzing Online Communities of Problematic Information: A Computational Approach.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Towards Analyzing Online Communities of Problematic Information: A Computational Approach.
Author:	Phadke, Shruti.
Published:	Ann Arbor : ProQuest Dissertations & Theses, 2023
Description:	195 p.
Notes:	Source: Dissertations Abstracts International, Volume: 85-01, Section: B.
Notes:	Advisor: Mitra, Tanushree.
Contained By:	Dissertations Abstracts International85-01B.
Subject:	Information science.
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30525161
ISBN:	9798379908423

Towards Analyzing Online Communities of Problematic Information: A Computational Approach.
Phadke, Shruti.

Towards Analyzing Online Communities of Problematic Information: A Computational Approach. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 195 p.

Source: Dissertations Abstracts International, Volume: 85-01, Section: B.

Thesis (Ph.D.)--University of Washington, 2023.

This item must not be sold to any third party vendors.

Problematic information - information that is inaccurate, misleading, inappropriately attributed, or altogether fabricated - prevails in digital societies and spaces. Communities formed around sharing, theorizing, or mobilizing problematic information can lead online users through the path of social distrust, paranoia, or radicalization. Not only that but ideas propagating through such communities spreading conspiracy theories or hateful ideologies can tear at the social fabric and threaten civility in online spaces. Despite the obvious disruptive consequences of online communities of problematic information, we don't have a large-scale, data-driven understanding of deeper social processes that are underway. In this dissertation, I contribute empirical insights into engagement, mobilization, and disengagement from communities of problematic information using theory-guided quantitative methods. My analysis of problematic online communities takes me across multiple social media platforms such as Reddit, Facebook, Twitter, and 4chan and various methodologies ranging from machine learning, and natural language processing to qualitative interviews. Equipped with these theories and methods, I analyze various instances of problematic information, such as conspiracy theories and hate movements in the West, and coordinated political amplification in India. Specifically, I investigate what makes people engage in conspiracy theory discussions, what are the mechanisms of information mobilization and content framing in online hate movements and political campaigns, and what are the ways in which people may leave online conspiracy theory discussions. From a data science perspective, my work uncovers how social media users engage with problematic online content and leverage technologies across platforms. From a social-psychological perspective, my research contributes to the literature on how various social, psychological, and cognitive processes motivate the users' journey through problematic information online.I showcase that studying large-scale digital traces contributes to the discussion on how thousands of online users self-select themselves into conspiracy theory discussion communities, how they take various pathways of engagement inside online conspiracy theory discussions, and how early signs of fracture in conspiracy worldviews result in eventual disengagement from conspiracy theory discussion communities. Moreover, looking into thousands of Facebook groups discussing white supremacy, and anti-LGBTQ ideas, this research also reveals how problematic information is mobilized through accounts playing various social roles within the hate movement. In my ongoing work, I leverage the multidisciplinary understanding from my doctoral research to design online prompts for intervening in online interactions surrounding climate change denial conspiracy theories. More broadly, I am excited about continuing this work by exploring ethical, fair, and effective ways of intervening in open online discussions to reduce problematic content online.

ISBN: 9798379908423Subjects--Topical Terms:

190425
Information science.
Subjects--Index Terms:

Computational social science

Towards Analyzing Online Communities of Problematic Information: A Computational Approach.
LDR:04348nmm a2200409 4500 001 655794
005 20240414211932.5
006 m o d
007 cr#unu||||||||
008 240620s2023 ||||||||||||||||| ||eng d
020 $a 9798379908423
035 $a (MiAaPQ)AAI30525161
035 $a AAI30525161
040 $a MiAaPQ $c MiAaPQ
100 1 $a Phadke, Shruti. $3 966882
245 1 0 $a Towards Analyzing Online Communities of Problematic Information: A Computational Approach.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2023
300 $a 195 p.
500 $a Source: Dissertations Abstracts International, Volume: 85-01, Section: B.
500 $a Advisor: Mitra, Tanushree.
502 $a Thesis (Ph.D.)--University of Washington, 2023.
506 $a This item must not be sold to any third party vendors.
520 $a Problematic information - information that is inaccurate, misleading, inappropriately attributed, or altogether fabricated - prevails in digital societies and spaces. Communities formed around sharing, theorizing, or mobilizing problematic information can lead online users through the path of social distrust, paranoia, or radicalization. Not only that but ideas propagating through such communities spreading conspiracy theories or hateful ideologies can tear at the social fabric and threaten civility in online spaces. Despite the obvious disruptive consequences of online communities of problematic information, we don't have a large-scale, data-driven understanding of deeper social processes that are underway. In this dissertation, I contribute empirical insights into engagement, mobilization, and disengagement from communities of problematic information using theory-guided quantitative methods. My analysis of problematic online communities takes me across multiple social media platforms such as Reddit, Facebook, Twitter, and 4chan and various methodologies ranging from machine learning, and natural language processing to qualitative interviews. Equipped with these theories and methods, I analyze various instances of problematic information, such as conspiracy theories and hate movements in the West, and coordinated political amplification in India. Specifically, I investigate what makes people engage in conspiracy theory discussions, what are the mechanisms of information mobilization and content framing in online hate movements and political campaigns, and what are the ways in which people may leave online conspiracy theory discussions. From a data science perspective, my work uncovers how social media users engage with problematic online content and leverage technologies across platforms. From a social-psychological perspective, my research contributes to the literature on how various social, psychological, and cognitive processes motivate the users' journey through problematic information online.I showcase that studying large-scale digital traces contributes to the discussion on how thousands of online users self-select themselves into conspiracy theory discussion communities, how they take various pathways of engagement inside online conspiracy theory discussions, and how early signs of fracture in conspiracy worldviews result in eventual disengagement from conspiracy theory discussion communities. Moreover, looking into thousands of Facebook groups discussing white supremacy, and anti-LGBTQ ideas, this research also reveals how problematic information is mobilized through accounts playing various social roles within the hate movement. In my ongoing work, I leverage the multidisciplinary understanding from my doctoral research to design online prompts for intervening in online interactions surrounding climate change denial conspiracy theories. More broadly, I am excited about continuing this work by exploring ethical, fair, and effective ways of intervening in open online discussions to reduce problematic content online.
590 $a School code: 0250.
650 4 $a Information science. $3 190425
650 4 $a Web studies. $3 708690
650 4 $a Information technology. $3 184390
650 4 $a Social psychology. $3 175625
653 $a Computational social science
653 $a HCI
653 $a Machine learning
653 $a Natural language processing
653 $a Social computing
690 $a 0723
690 $a 0489
690 $a 0646
690 $a 0451
710 2 $a University of Washington. $b Information School. $3 966801
773 0 $t Dissertations Abstracts International $g 85-01B.
790 $a 0250
791 $a Ph.D.
792 $a 2023
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30525161