Navigating misinformation in voice messages: Identification of user‐centered features for digital interventions

Abstract

Misinformation presents a challenge to democracies, particularly in times of crisis. One way in which misinformation is spread is through voice messages sent via messenger groups, which enable members to share information on a larger scale. Gaining user perspectives on digital misinformation interventions as countermeasure after detection is crucial. In this paper, we extract potential features of misinformation in voice messages from literature, implement them within a program that automatically processes voice messages, and evaluate their perceived usefulness and comprehensibility as user‐centered indicators. We propose 35 features extracted from audio files at the character, word, sentence, audio, and creator levels to assist (1) private individuals in conducting credibility assessments, (2) government agencies faced with data overload during crises, and (3) researchers seeking to gather features for automatic detection approaches. We conducted a think‐aloud study with laypersons () to provide initial insight into how individuals autonomously assess the credibility of voice messages, as well as which automatically extracted features they find to be clear and convincing indicators of misinformation. Our study provides qualitative and quantitative insights into valuable indicators, particularly when they relate directly to the content or its creator, and uncovers challenges in user interface design.

Governing the uses of social media and crowdsourcing in crisis and disaster (risk) management

“…The challenges around managing misinformation emerge throughout this special issue, and remain top priorities for authorities working with SMCS in crisis and disaster (risk) management. In Navigating Misinformation in Voice Messages: Identification of User-Centered Features for Digital Interventions, the authors take on these challenges directly through a study which examines and evaluates features of misinformation in voice messages (Hartwig et al, 2024). Using a program to automatically process and identify features of misinformation in messages, the paper employs a think-aloud study with participants to provide insights as to how individuals autonomously assess the credibility of voice messages, as well as which automatically extracted features they find to be convincing indicators of misinformation.…”