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Carla F. Griggio

Publications and source records attributed to Carla F. Griggio.

3 recordsLinked to original sources

User Perceptions and Attitudes Toward Untraceability in Messaging Platforms

Mainstream messaging platforms offer a variety of features designed to enhance user privacy, such as password-protected chats and end-to-end encryption, which primarily protect message contents. Beyond contents, a lot can be inferred about people simply by tracing who sends and receives messages, when, and how often. This paper explores user perceptions of and attitudes toward "untraceability", defined as preventing third parties from tracing who communicates with whom, to inform the design of privacy-enhancing technologies and untraceable communication protocols. Through a vignette-based qualitative study with 189 participants, we identify a diverse set of features that users perceive to be useful for untraceable messaging, ranging from using aliases instead of real names to VPNs. Through a reflexive thematic analysis, we uncover three overarching attitudes that influence the support or rejection of untraceability in messaging platforms and that can serve as a set of new privacy personas: privacy fundamentalists, who advocate for privacy as a universal right; safety fundamentalists, who support surveillance for the sake of accountability; and optimists, who advocate for privacy in principle but also endorse exceptions in idealistic ways, such as encryption backdoors. We highlight a critical gap between the threat models assumed by users and those addressed by untraceable communication protocols. Many participants understood untraceability as a form of anonymity, but interpret it as senders and receivers hiding their identities from each other, rather than from external network observers. We discuss implications for design of strategic communication and user interfaces of untraceable messaging protocols, and propose framing untraceability as a form of "altruistic privacy", i.e., adopting privacy-enhancing technologies to protect others, as a promising strategy to foster broad adoption.

cs.CR

Chatbots for Data Collection in Surveys: A Comparison of Four Theory-Based Interview Probes

Surveys are a widespread method for collecting data at scale, but their rigid structure often limits the depth of qualitative insights obtained. While interviews naturally yield richer responses, they are challenging to conduct across diverse locations and large participant pools. To partially bridge this gap, we investigate the potential of using LLM-based chatbots to support qualitative data collection through interview probes embedded in surveys. We assess four theory-based interview probes: descriptive, idiographic, clarifying, and explanatory. Through a split-plot study design (N=64), we compare the probes' impact on response quality and user experience across three key stages of HCI research: exploration, requirements gathering, and evaluation. Our results show that probes facilitate the collection of high-quality survey data, with specific probes proving effective at different research stages. We contribute practical and methodological implications for using chatbots as research tools to enrich qualitative data collection.

cs.HC

Speejis: Enhancing User Experience of Mobile Voice Messaging with Automatic Visual Speech Emotion Cues

Mobile messaging apps offer an increasing range of emotional expressions, such as emojis to help users manually augment their texting experiences. Accessibility of such augmentations is limited in voice messaging. With the term "speejis" we refer to accessible emojis and other visual speech emotion cues that are created automatically from speech input alone. The paper presents an implementation of speejis and reports on a user study (N=12) comparing the UX of voice messaging with and without speejis. Results show significant differences in measures such as attractiveness and stimulation and a clear preference of all participants for messaging with speejis. We highlight the benefits of using paralinguistic speech processing and continuous emotion models to enable finer grained augmentations of emotion changes and transitions within a single message in addition to augmentations of the overall tone of the message.

cs.HC