Hourigan, A., Thomson, T., Notley, T., Park, S., & Dezuanni, M. (2026). Everyday encounters with misinformation online: examining sources, topics and modes. Information, Communication & Society, 1–22. https://doi.org/10.1080/1369118x.2026.2636132
Summary
This paper investigates how ordinary Australian adults encounter and make sense of misinformation in the course of their everyday online routines. Using a digital diary methodology that deliberately avoids imposing scholarly definitions of misinformation, the authors let participants flag content they themselves considered false, misleading, or untrustworthy. The central argument is that misinformation research has been too narrowly topic-driven (health, politics, science) and methodologically detached from lived experience, thereby missing the “everyday” texture of misinformation encounters. Contrary to prevailing framings, they find that perceived misinformation extends into mundane domains like business and economics, is overwhelmingly text-based, and is most often attributed to mainstream and alternative news outlets rather than fringe or anonymous sources — a finding that complicates the standard view of news media as remediators of information disorder.
Key Contributions
- Empirical account of the everyday, in situ texture of misinformation encounters, departing from single-issue and big-data approaches.
- Demonstrates the value of diary-based, participant-driven methods that avoid pre-imposed definitions of misinformation.
- Documents mainstream news as a leading perceived source of misinformation, unsettling the “news-as-corrective” framing.
- Offers a three-dimensional analytic framework — topic, mode, and source — for studying misinformation perceptions.
- Draws out policy and pedagogical implications favouring whole-of-society media literacy responses and greater journalistic transparency.
Methods
A digital diary study conducted via the Indeemo platform across two seven-day waves in mid-2024. A purposive sample of 55 Australian adults (balanced across age, gender, education, region, cultural/linguistic diversity, and self-reported misinformation exposure) captured screenshots and recordings of online content as they encountered it, narrating spoken reflections on trustworthiness plus opening (day 1) and closing (day 7) videos. This yielded 1,564 examples and ~221,735 words of reflection, from which a subsample of 322 user-flagged false/misleading/untrustworthy examples was analysed via deductive thematic analysis across topic, mode, and source dimensions (Krippendorff’s α = 1.0 for topic/source, 0.91 for mode). An independent verification analysis assessed the objective veracity of 10% of flagged claims.
Findings
- Business/economics was the most-flagged topic (18%), often exploiting cost-of-living anxieties (e.g. misleading “tax hacks”); celebrity (16%) and crime/crisis (16%) followed.
- Text dominated (68% of examples), with news headlines making up 53% of text-based claims; multimodal 18%, video 11%, image 3%, audio 1% — challenging alarmism about AI-generated and audiovisual content.
- Mainstream and alternative news outlets accounted for 62% of flagged sources, 82% of them domestic (News Corp Australia 25%, Seven West Media 17%, Nine Entertainment 15%).
- Social media accounts/groups were the second-largest source category (19%), dominated by Meta platforms (Facebook 35%).
- Untrustworthiness was attributed chiefly to clickbait, sensationalism, perceived bias, and headline–article disconnect — termed “Clickbait Concern.”
- Independent verification found only 9% of a sampled 10% of claims were objectively false/misleading, 28% indeterminate, and 25% contained no verifiable claim — user perception often diverges from objective falsity.
Connections
This audience-centered, perception-driven approach contrasts with the platform- and big-data-scale mapping of exposure found in work like Gonzalez-Bailon2024-rq and Budak2024-ef, offering an experiential counterpoint. Its emphasis on trust in journalism and everyday encounters connects to research on news reliability and audience trust such as Rossini2026-jn and Cazzamatta2026-lo, while its skepticism toward alarmism over AI-generated and audiovisual misinformation speaks to debates addressed in Marwick2026-qd.
Podcast
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