Assisting Users on the Privacy Decision-Making Process in an OSN for Educational Purposes

Alemany, José; Val, Elena del; García-Fornes, Ana

doi:10.1007/978-3-030-49778-1_31

Cited by 4 publications

(7 citation statements)

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“…Questionnaires were used to retrieve the personality traits of the participants (Big Five personality test), the persuasive power of types of arguments (Questionnaire A), and the persuasive power of argumentation schemes (Questionnaire B). Participants also used our social network PESEDIA (Argente et al, 2017;Alemany et al, 2020) during one month from where we collected the online social interaction data. The context of the study, the measures and instruments, the procedure, and the participants are presented below.…”

Section: Methodsmentioning

confidence: 99%

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On the Prevention of Privacy Threats: How Can We Persuade Our Social Network Users?

Ruiz-Dolz¹,

Alemany²,

Heras³

et al. 2021

Preprint

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Complex decision making problems such as the privacy policy selection when sharing content in online social networks can significantly benefit from artificial intelligence systems. With the use of Computational Argumentation, it is possible to persuade human users to modify their initial decisions to avoid potential privacy threats and violations. In this paper, we present a study performed over 186 teenage users aimed at analysing their behaviour when we try to persuade them to modify their initial decisions in online social networks with different arguments. The results of the study revealed that the personality traits and the social interaction data (e.g., number of comments, friends, and likes) of our participants were significantly correlated with the persuasive power of the arguments. The findings from this analysis will help to model OSN users and define persuasive strategies for argumentation dialogues in this domain.

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Section: Methodsmentioning

confidence: 99%

“…This theoretical framework was proposed to be integrated into PESEDIA, an educational social network (Argente et al, 2017;Alemany et al, 2020). However, deciding the dialogue strategies when interacting with human users is still a challenge.…”

Section: Research Questionsmentioning

confidence: 99%

On the Prevention of Privacy Threats: How Can We Persuade Our Social Network Users?

Ruiz-Dolz¹,

Alemany²,

Heras³

et al. 2021

Preprint

Self Cite

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“…Their results revealed a reduction of downloaded malware without reducing productivity. • Alemany et al [3] included personalized privacy nudges into an online social network called PESEDIA that also had the purpose to educate users about privacy and to enhance awareness for privacy risks.…”

Section: Choose a Combination Of Approachesmentioning

confidence: 99%

Privacy Nudges and Informed Consent? Challenges for Privacy Nudge Design

Zimmermann

2023

Human Factors in Privacy Research

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Privacy nudges aim to support the “wise” choice concerning digital privacy decisions. Examples include nudges to encourage privacy-friendly app choices or to discourage unintended location disclosure. Yet, a general nudge criticism concerns the potential manipulation of users by targeting automatic and perhaps unaware cognitive processes. This calls for guidance in designing ethically favorable and transparent nudges. Therefore, this chapter first reviews and presents guidelines for designing ethical privacy nudges. Second, encouraging users to make the “wise” privacy choice is not easy. Initially, the more privacy-friendly choice for the user appears to be clear in many cases. However, with regard to privacy, the choice is less clear when considering legal requirements. Current EU regulations depart from generally favoring the more privacy-friendly option and instead suggest enabling users to make an informed decision. This is not necessarily the most privacy-preserving option but for some users might be a trade-off considering convenience or other factors. Apart from “privacy-preserving” nudges, this chapter thus discusses options for and challenges associated with designing privacy nudges that can support users in making informed privacy decisions. This includes nudges that target reflective thinking, interventions based on the users’ preferences, or a combination of nudges with other approaches.

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“…• Interrogative sentences are analysed as ADUs, because they can be used to support an argument (12), except when they are generic questions (13).…”

Section: Annotation Methodologymentioning

confidence: 99%

Computational Argumentation for the Automatic Analysis of Argumentative Discourse and Human Persuasion

Dolz¹

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Finalment, m'agradaria acabar expressant els meus agraïments a les dues persones fonamentals en la definició i consolidació de la meua carrera acadèmica, les meues tutores. Des que vaig comenc ¸ar a treballar al VRAIN, el suport rebut per part d'Ana i de Stella ha sigut incommensurable. Sota la seua tutela excepcional ha sigut possible desenvolupar totes les idees i propostes presents en aquesta tesi doctoral.Moltes gràcies a totes i tots.Ramon

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Assisting Users on the Privacy Decision-Making Process in an OSN for Educational Purposes

Cited by 4 publications

References 6 publications

On the Prevention of Privacy Threats: How Can We Persuade Our Social Network Users?

On the Prevention of Privacy Threats: How Can We Persuade Our Social Network Users?

Privacy Nudges and Informed Consent? Challenges for Privacy Nudge Design

Computational Argumentation for the Automatic Analysis of Argumentative Discourse and Human Persuasion

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