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Item type:Publication, Access status: with files , Cybersecurity discussions in Stack Overflow: a developer-centred analysis of engagement and self-disclosure behaviour(2023-12-22); ; ; Stack Overflow (SO) is a popular platform among developers seeking advice on various software-related topics, including privacy and security. As for many knowledge-sharing websites, the value of SO depends largely on users’ engagement, namely their willingness to answer, comment or post technical questions. Still, many of these questions (including cybersecurity-related ones) remain unanswered, putting the site’s relevance and reputation into jeopardy. Hence, it is important to understand users’ participation in privacy and security discussions to promote engagement and foster the exchange of such expertise. Objective: Based on prior findings on online social networks, this work elaborates on the interplay between users’ engagement and their privacy practices in SO. Particularly, it analyses developers’ self-disclosure behaviour regarding profile visibility and their involvement in discussions related to privacy and security. Method: We followed a mixed-methods approach by (i) analysing SO data from 1239 cybersecurity-tagged questions along with 7048 user profiles, and (ii) conducting an anonymous online survey (N=64). Results: About 33% of the questions we retrieved had no answer, whereas more than 50% had no accepted answer. We observed that proactive users tend to disclose significantly less information in their profiles than reactive and unengaged ones. However, no correlations were found between these engagement categories and privacy-related constructs such as perceived control or general privacy concerns. Implications: These findings contribute to (i) a better understanding of developers’ engagement towards privacy and security topics, and (ii) to shape strategies promoting the exchange of cybersecurity expertise in SO.Publicationtype: Journal Article43 69 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , ENAGRAM : an app to evaluate preventative nudges for InstagramOnline self-disclosure is perhaps one of the last decade's most studied communication processes, thanks to the introduction of Online Social Networks (OSNs) like Facebook. Self-disclosure research has contributed significantly to the design of preventative nudges seeking to support and guide users when revealing private information in OSNs. Still, assessing the effectiveness of these solutions is often challenging since changing or modifying the choice architecture of OSN platforms is practically unfeasible. In turn, the effectiveness of numerous nudging designs is supported primarily by self-reported data instead of actual behavioral information. Objective: This work presents ENAGRAM, an app for evaluating preventative nudges, and reports the first results of an empirical study conducted with it. Such a study aims to showcase how the app (and the data collected with it) can be leveraged to assess the effectiveness of a particular nudging approach. Method: We used ENAGRAM as a vehicle to test a risk-based strategy for nudging the self-disclosure decisions of Instagram users. For this, we created two variations of the same nudge (i.e., with and without risk information) and tested it in a between-subjects experimental setting. Study participants (N=22) were recruited via Prolific and asked to use the app regularly for 7 days. An online survey was distributed at the end of the experiment to measure some privacy-related constructs. Results: From the data collected with ENAGRAM, we observed lower (though non-significant) self-disclosure levels when applying risk-based interventions. The constructs measured with the survey were not significant either, except for participants' External Information Privacy Concerns (EIPC). Implications: Our results suggest that (i) ENAGRAM is a suitable alternative for conducting longitudinal experiments in a privacy-friendly way, and (ii) it provides a flexible framework for the evaluation of a broad spectrum of nudging solutions.Publicationtype: Conference Paper54 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Meeting Strangers Online: Feature Models for Trustworthiness Assessment(2022-08); ; Getting to know new people online to later meet them offline for neighbourhood help, carpooling, or online dating has never been as easy as nowadays by social media performing computer-mediated introductions (CMIs). Unfortunately, interacting with strangers poses high risks such as unfulfilled expectations, fraud, or assaults. People most often tolerate risks if they believe others are trustworthy. However, conducting an online trustworthiness assessment usually is a challenge. Online cues differ from offline ones and people are either lacking awareness for the assessment’s relevance or find it too complicated. On these grounds, this work aims to aid software engineers to develop CMI that supports users in their online trustworthiness assessment. We focus on trust-related software features and nudges to i) increase user awareness, ii) trigger the trustworthiness assessment and iii) enable the assessment online. For that reason, we extend feature models to provide software engineers the possibility to create and document software features or nudges for trustworthiness assessment. The extended feature models for trustworthiness assessments can serve as reusable catalogues for validating features in terms of their impact on the trustworthiness assessment and for configuring CMI software product lines. Moreover, this work provides an example of how the extended feature models can be applied to catfishing protection in online dating.Publicationtype: Conference Paper45 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Community detection for access-control decisions : analysing the role of homophily and information diffusion in online social networks(Elsevier, 2022-05); ; ; ; Access-Control Lists (ACLs) (a.k.a. friend lists) are one of the most important privacy features of Online Social Networks (OSNs) as they allow users to restrict the audience of their publications. Nevertheless, creating and maintaining custom ACLs can introduce a high cognitive burden on average OSNs users since it normally requires assessing the trustworthiness of a large number of contacts. In principle, community detection algorithms can be leveraged to support the generation of ACLs by mapping a set of examples (i.e. contacts labelled as untrusted) to the emerging communities inside the user's ego-network. However, unlike users' access-control preferences, traditional community-detection algorithms do not take the homophily characteristics of such communities into account (i.e. attributes shared among members). Consequently, this strategy may lead to inaccurate ACL configurations and privacy breaches under certain homophily scenarios. This work investigates the use of community-detection algorithms for the automatic generation of ACLs in OSNs. Particularly, it analyses the performance of the aforementioned approach under different homophily conditions through a simulation model. Furthermore, since private information may reach the scope of untrusted recipients through the re-sharing affordances of OSNs, information diffusion processes are also modelled and taken explicitly into account. Altogether, the removal of gatekeeper nodes is further explored as a strategy to counteract unwanted data dissemination.Publicationtype: Journal ArticleScopus© Citations 5 92 325