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Online Social Network Risk Management.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Online Social Network Risk Management./
作者:	Mogensen, Matthew David.
面頁冊數:	1 online resource (164 pages)
附註:	Source: Dissertations Abstracts International, Volume: 84-04, Section: A.
Contained By:	Dissertations Abstracts International84-04A.
標題:	Threats. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29342317click for full text (PQDT)
ISBN:	9798351494524

Online Social Network Risk Management.
Mogensen, Matthew David.

Online Social Network Risk Management. - 1 online resource (164 pages)

Source: Dissertations Abstracts International, Volume: 84-04, Section: A.

Thesis (Ph.D.)--Stanford University, 2022.

Includes bibliographical references

As online social networks become more influential in society, a new form of cyber threat continues to impact its users: platform manipulation. This occurs when social network users attempt to exploit other users through their online behavior on a social network platform. Current methods for detecting and eliminating platform manipulation rely on the use of machine learning (ML) and artificial intelligence (AI) models. While these methods are able to parse through very large data sets efficiently, they are often trained to only recognize specific traits within user behavior (e.g., spam, malicious links, hate speech), and may not be able to incorporate other forms of evidence (e.g., human observations) in real time without being re-trained and validated, which can be costly and time-consuming in practice. To make an accurate risk assessment of a user's long-term behavior which can be updated over time, decision-makers need a method for integrating multiple forms of evidence (AI and human) across space (by malicious trait) and time into a probability distribution over possible scenarios, as well as knowledge of the potential consequences to the platform from each scenario. In this research, we use probabilistic risk analysis to combine both AI-generated and human-generated evidence in order to determine the risk of individual users to the platform. This allows decision-makers to not only consider the probability that users are malicious, but also their range of potential consequences. We also develop a decision analysis framework for platform referral decisions (whether to request human intervention) using the value of clairvoyance on human agent observations to determine when to involve human agents in the decision-making process (and when to rely on automated decisions to remove users from the platform).

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

Mode of access: World Wide Web

ISBN: 9798351494524Subjects--Topical Terms:

594889
Threats.
Index Terms--Genre/Form:

542853
Electronic books.

Online Social Network Risk Management.
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500 $a Source: Dissertations Abstracts International, Volume: 84-04, Section: A.
500 $a Advisor: Ashlagi, Itai; Shachter, Ross D.; Pate-Cornel, Elisabeth.
502 $a Thesis (Ph.D.)--Stanford University, 2022.
504 $a Includes bibliographical references
520 $a As online social networks become more influential in society, a new form of cyber threat continues to impact its users: platform manipulation. This occurs when social network users attempt to exploit other users through their online behavior on a social network platform. Current methods for detecting and eliminating platform manipulation rely on the use of machine learning (ML) and artificial intelligence (AI) models. While these methods are able to parse through very large data sets efficiently, they are often trained to only recognize specific traits within user behavior (e.g., spam, malicious links, hate speech), and may not be able to incorporate other forms of evidence (e.g., human observations) in real time without being re-trained and validated, which can be costly and time-consuming in practice. To make an accurate risk assessment of a user's long-term behavior which can be updated over time, decision-makers need a method for integrating multiple forms of evidence (AI and human) across space (by malicious trait) and time into a probability distribution over possible scenarios, as well as knowledge of the potential consequences to the platform from each scenario. In this research, we use probabilistic risk analysis to combine both AI-generated and human-generated evidence in order to determine the risk of individual users to the platform. This allows decision-makers to not only consider the probability that users are malicious, but also their range of potential consequences. We also develop a decision analysis framework for platform referral decisions (whether to request human intervention) using the value of clairvoyance on human agent observations to determine when to involve human agents in the decision-making process (and when to rely on automated decisions to remove users from the platform).
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
538 $a Mode of access: World Wide Web
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650 4 $a User behavior. $3 3564661
650 4 $a Enforcement. $3 3564162
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650 4 $a False information. $3 3564362
650 4 $a Hate speech. $3 737070
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650 4 $a Extrasensory perception--ESP. $3 3694655
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710 2 $a Stanford University. $3 754827
773 0 $t Dissertations Abstracts International $g 84-04A.
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