The spread of online misinformation undermines responsible opinion formation, this risk being exacerbated for heavily debated topics where emotionally-driven reasoning and the formation of echo chambers weaken critical scrutiny. Visual misinformation labels which nudge users towards more mindful information-seeking behaviors are common-place, yet, despite its increasing popularity, the podcast medium remains overlooked in these efforts compared to Web search. This thesis examines the design of auditory misinformation warnings: brief, non-verbal sound cues embedded in podcast audio to indicate that a nearby statement is misleading. To verify the relevant criteria for these signals’ effectiveness and the factors influencing their reception by listeners, two exploratory crowdsourcing studies were conducted. The first assessed 15 auditory icons in terms of recognizability, consistency of conceptual mapping, and induced disruption, isolating the most favorably perceived candidates. The second embedded the latter as purposeful interventions within AI-generated podcast dialogues on three controversial scientific topics interspersed with myths and falsehoods, employing a between-subjects design across four warning-placement configurations (before, after, enclosing, and concurrent with a misinformation occurrence) and treating several psychometric traits as exploratory factors. The findings indicate salient auditory icons that alert listeners are a viable choice provided they do not severely depart from the podcast’s setting or listeners’ expectations of traditional podcast-editing effects; otherwise, they may trigger prolonged dissatisfaction and ultimately break immersion. No conclusive data on the cues’ role in misinformation recognition emerged, although some listeners correctly inferred their intended function when these followed or enclosed a misleading claim. Warnings placed after a falsehood were least disruptive while concurrent placement was associated with the lowest content recall. Regarding contextual factors, listeners’ prior stance on a debated topic and a podcast’s perceived density and pace exhibited significant correlations with several Likert-scale sound-cue evaluations. Participant’s reactions and follow-up attitudes towards the interventions diverged significantly – anticipation, habituation, and active resistance against perceived paternalism all being expressed. These results yield preliminary recommendations for auditory misinformation warnings and, given the many non-trivial trade-offs at play, outline a multitude of directions for future research. ... Read more

As a point estimate of the similarity score between two possibly indefinite rankings, extrapolated rank-biased overlap (RBOEXT) uses the assumption that the agreement observed at the last evaluation depth continues indefinitely across the unseen tails of the two lists. This assumption does not account for any patterns that occur in the visible prefixes, imposing a strict restriction on the extrapolation. In an effort to improve the accuracy of RBOEXT, three reformulations with a relaxed theoretical basis are proposed in this paper: one continually re-uses the agreement from the previous depth while the other two rely on regression to fit a function on the seen agreements. Using synthetic data, the performance of these new extrapolation methods is compared to the original's in terms of closeness to the true RBO score as well as the average distance between assumed and actual agreement in the rankings' unseen tails. Overall, an impactful difference is observed in the estimates of agreement generated by the four approaches: as the trends from the visible prefixes are barely captured by the simpler techniques or closely-reproduced by the more flexible ones, the trade-off between under- and overfitting becomes increasingly relevant. The results thus indicate a need for some middle-ground to be established such that it factors in the observed patterns while also generalizing well for the tails. ... Read more