Meeting Strangers Online: Feature Models for Trustworthiness Assessment

• Published in: Human-Centered Software Engineering Vol. 13482; pp. 3 - 22
• Main Authors: Borchert, Angela, Ferreyra, Nicolás E. Díaz, Heisel, Maritta
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2022; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Feature models; Nudging; Social media; Trustworthiness; User-centred design
• ISBN: 3031147847; 9783031147845
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-031-14785-2_1

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.