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Mining Users' Self-Privacy Violations in Online Public Discourse Using Data Science Techniques.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Mining Users' Self-Privacy Violations in Online Public Discourse Using Data Science Techniques./
Author:	Umar, Prasanna.
Description:	1 online resource (94 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 84-02, Section: A.
Contained By:	Dissertations Abstracts International84-02A.
Subject:	Personal information. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29276598click for full text (PQDT)
ISBN:	9798841572879

Mining Users' Self-Privacy Violations in Online Public Discourse Using Data Science Techniques.
Umar, Prasanna.

Mining Users' Self-Privacy Violations in Online Public Discourse Using Data Science Techniques. - 1 online resource (94 pages)

Source: Dissertations Abstracts International, Volume: 84-02, Section: A.

Thesis (Ph.D.)--The Pennsylvania State University, 2022.

Includes bibliographical references

User engagement in online public discourse often entails self-disclosure - an intentional behavior of sharing personal information with others. Users self-disclose in pursuit of various strategic goals and benefits, both intrinsic and extrinsic. Intrinsically, self-disclosure serves therapeutic functions while extrinsic rewards include social and relational benefits such as social connectedness, validation, relational development, and so on. Personal information released on online public platforms (e.g. commentaries) becomes part of shared knowledge and is subject to detrimental uses by advertisers and malicious parties. This behavior, therefore, poses risks to users' online privacy. Yet, users are often unaware of the sheer amount of personal information they share across online forums, commentaries, and websites, even beyond social network sites. We note that while work on self-disclosure in seemingly bounded environments (e.g. social networks) has shed light on users' motivations for self-disclosure and contextual influences on such behavior, less is known about user disclosures in public commentaries. In this dissertation, we examine self-disclosure in online public commentaries and address the research gap. Specifically, we devise ways to automatically detect online self-disclosure from users' contents and then, contextualize users' disclosures through the study of multiple antecedents to, motivations for, and patterns of self-disclosing behavior. First, we propose a rule-based method of automated self-disclosure detection using a news discourse dataset. We also elucidate the effects of three hypothesized antecedents of self-disclosing behavior: peer influence (reciprocity), topic of conversation, and anonymity. Our results support the role of anonymity and peer influence in eliciting self-disclosure responses from online users. We find that self-disclosure varies across different topical contexts. Second, we detail a generalizable supervised approach to self-disclosure detection that outperforms existing detection methods. Using multiple public discourse datasets, we highlight users' possible attempts at managing privacy risks through alignment with group disclosure norms both in content and self-disclosure rate. Results of our analyses show that users can stand out in conversations because of disproportionate self-disclosure patterns and dissimilar disclosure content. Finally, we analyze the temporal evolution of self-disclosure patterns in public Twitter interaction networks as users maintain persistent social connections for therapeutic, social, and relational benefits amidst the uncertainties of COVID-19 pandemic. We note heightened self-disclosure with the evolution of the pandemic and shifts in users' privacy perceptions.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798841572879Subjects--Topical Terms:

3562412
Personal information.
Index Terms--Genre/Form:

542853
Electronic books.

Mining Users' Self-Privacy Violations in Online Public Discourse Using Data Science Techniques.
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100 1 $a Umar, Prasanna. $3 3705023
245 1 0 $a Mining Users' Self-Privacy Violations in Online Public Discourse Using Data Science Techniques.
264 0 $c 2022
300 $a 1 online resource (94 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 84-02, Section: A.
500 $a Advisor: Squicciarini, Anna.
502 $a Thesis (Ph.D.)--The Pennsylvania State University, 2022.
504 $a Includes bibliographical references
520 $a User engagement in online public discourse often entails self-disclosure - an intentional behavior of sharing personal information with others. Users self-disclose in pursuit of various strategic goals and benefits, both intrinsic and extrinsic. Intrinsically, self-disclosure serves therapeutic functions while extrinsic rewards include social and relational benefits such as social connectedness, validation, relational development, and so on. Personal information released on online public platforms (e.g. commentaries) becomes part of shared knowledge and is subject to detrimental uses by advertisers and malicious parties. This behavior, therefore, poses risks to users' online privacy. Yet, users are often unaware of the sheer amount of personal information they share across online forums, commentaries, and websites, even beyond social network sites. We note that while work on self-disclosure in seemingly bounded environments (e.g. social networks) has shed light on users' motivations for self-disclosure and contextual influences on such behavior, less is known about user disclosures in public commentaries. In this dissertation, we examine self-disclosure in online public commentaries and address the research gap. Specifically, we devise ways to automatically detect online self-disclosure from users' contents and then, contextualize users' disclosures through the study of multiple antecedents to, motivations for, and patterns of self-disclosing behavior. First, we propose a rule-based method of automated self-disclosure detection using a news discourse dataset. We also elucidate the effects of three hypothesized antecedents of self-disclosing behavior: peer influence (reciprocity), topic of conversation, and anonymity. Our results support the role of anonymity and peer influence in eliciting self-disclosure responses from online users. We find that self-disclosure varies across different topical contexts. Second, we detail a generalizable supervised approach to self-disclosure detection that outperforms existing detection methods. Using multiple public discourse datasets, we highlight users' possible attempts at managing privacy risks through alignment with group disclosure norms both in content and self-disclosure rate. Results of our analyses show that users can stand out in conversations because of disproportionate self-disclosure patterns and dissimilar disclosure content. Finally, we analyze the temporal evolution of self-disclosure patterns in public Twitter interaction networks as users maintain persistent social connections for therapeutic, social, and relational benefits amidst the uncertainties of COVID-19 pandemic. We note heightened self-disclosure with the evolution of the pandemic and shifts in users' privacy perceptions.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
538 $a Mode of access: World Wide Web
650 4 $a Personal information. $3 3562412
650 4 $a User behavior. $3 3564661
650 4 $a Audiences. $3 618475
650 4 $a Verbal communication. $3 3560678
650 4 $a Peers. $3 3435329
650 4 $a Labeling. $3 3560710
650 4 $a User generated content. $3 3562474
650 4 $a Computer privacy. $3 3705024
650 4 $a Self expression. $3 3682390
650 4 $a Norms. $3 3562802
650 4 $a Deception. $3 535647
650 4 $a Gender identity. $3 523751
650 4 $a Social research. $3 2122687
650 4 $a COVID-19. $3 3554449
650 4 $a Communication. $3 524709
650 4 $a Gender studies. $3 2122708
650 4 $a Web studies. $3 2122754
655 7 $a Electronic books. $2 lcsh $3 542853
690 $a 0344
690 $a 0459
690 $a 0733
690 $a 0646
710 2 $a ProQuest Information and Learning Co. $3 783688
710 2 $a The Pennsylvania State University. $3 699896
773 0 $t Dissertations Abstracts International $g 84-02A.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29276598 $z click for full text (PQDT)