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We build data-driven digital products that help navigate complex choices.","metaImage":{"url":"https://s3.ca-central-1.amazonaws.com/site.voxpoplabs.com/vpl_logo_56477a84de.png"},"twitterHandle":null},"sections":[{"uuid":"productSection","title":"Designing Evidence Infrastructure to Measure Trust in Media","description":{"data":{"description":"Since 2024 Vox Pop Labs has partnered with the Google News Initiative, Cossette Media, and Plus Company to deliver the News Effectiveness Study, a national research program designed to produce solid evidence on one of the most contested questions in modern media systems: trust.\n\n## When credibility becomes uncertain\n\nTrust has become a scarce resource. Audiences now navigate an information environment where professional journalism competes—and often collides—with social platforms, influencers, and increasingly sophisticated AI‑assisted systems. This fragmentation has blurred traditional signals of authority and made it harder for people to distinguish credible reporting from persuasive content, opinion, or outright manipulation.\n\nAt the same time, publishers, advertisers, and platforms are operating with growing uncertainty about where credibility actually resides. Long‑standing assumptions about trusted brands or established formats no longer hold in a landscape where attention is fluid, loyalty is fragile, and audiences often encounter news outside its original context. As a result, organizations are questioning whether trust still meaningfully shapes how people interpret information, engage with brands, or make decisions. The stakes are high, yet the evidence guiding these decisions has been surprisingly thin.\n\nDespite the central role of trust in news consumption, reliable evidence has been limited. Stakeholders have lacked clear causal insight into how source, context, and content interact to shape perceptions of trustworthiness—particularly across Canada’s distinct English- and French-language media ecosystems. The News Effectiveness Study was designed to address these gaps.\n\nRather than producing a single descriptive report, the project was conceived as a research infrastructure: a repeatable system for measuring how trust is produced, amplified, or eroded across media environments — and how those dynamics affect downstream outcomes.\n\n## From insight to experimental platform\n\nVox Pop Labs designed and delivered the News Effectiveness Study as a productized research platform, built to support ongoing decision-making rather than one-off insight. The aim was not simply to observe correlations, but to construct an experimental system capable of isolating the drivers of trust in complex information ecosystems.\n\nThe research program began with **Phase I**, a pan-Canadian study examining how trust in news environments influences advertiser brand perception. Conducted in both English and French, Phase I tested the hypothesis that advertising appearing within trusted news environments benefits from a measurable credibility transfer—a phenomenon the research identified as the “trust premium.” By quantifying how publisher credibility affects downstream brand perception, the first phase established the empirical foundation for a broader program of research into the mechanisms through which trust operates in modern information systems. \n\n## Measuring trust under controlled conditions\n\nIn **Phase II**, the focus expanded to the underlying mechanisms: what actually produces trust, how source cues and contextual signals interact, and under what conditions evidence itself can shift audience perceptions. To answer these questions, Vox Pop Labs designed a survey-based experiment and employed a large-scale sample of 3,421 Canadian adults, fielded in English and French. Participants evaluated short informational vignettes in which source, context, and evidentiary depth were independently varied, allowing causal effects to be identified while holding underlying claims constant.\n\nTrust was measured using standardized credibility and accuracy scales. Because participants evaluated multiple vignettes, analysis relied on multilevel statistical models accounting for repeated measures, topic variation, and individual trust orientations, with controls for language, political orientation, baseline institutional trust, and demographics.\nMethodological transparency was treated as a core deliverable. Data were weighted using quasi-randomization and post-stratification against census benchmarks, with modeled error estimates, design effects, and full technical documentation published alongside results.\n\n## What the evidence shows\n\nThe News Effectiveness Study produced a set of clear, actionable findings. Journalists and recognized experts were consistently perceived as more credible than influencers or AI systems. Context mattered: traditional news formats amplified credibility relative to social feeds and chatbot interfaces, with trusted sources benefiting disproportionately from trusted environments.\n\nThe study also showed that evidence multiplies trust. Providing extended information from peer-reviewed scientific studies substantially increased perceived credibility and accuracy across all sources, with the largest gains among those starting from lower trust baselines. This effect highlights the persuasive value of transparent, verifiable information and suggests that audiences reward efforts to substantiate claims, even when they are initially skeptical. Findings from the experiment challenged assumptions about audience behaviour, revealing that younger Canadians often expressed higher trust in traditional media than older cohorts. This counterintuitive result points to a generational shift in how credibility is evaluated, with younger audiences potentially distinguishing more sharply between professional journalism and the broader mix of online content.\n\nTaken together, the results reframed trust as a system property, produced through the interaction of source, context, content, and audience predispositions. Trust was not located in any single element but emerged from the alignment—or misalignment—of these factors. This system perspective underscores the need for stakeholders to consider how credibility is constructed dynamically, rather than treating it as a static attribute of brands or platforms.\n\n## Looking ahead: Trust in an AI-mediated news ecosystem\n\nAs the information environment continues to evolve, one of the most consequential questions facing publishers, platforms, and audiences concerns the role of artificial intelligence in the production and distribution of news. Advances in generative AI have made it possible to produce highly realistic text, images, and video at scale, raising new questions about how audiences evaluate credibility when the boundaries between human and machine authorship become blurred.\n\n**Phase III** of the News Effectiveness Study will extend the research platform to examine how AI-generated or AI-assisted news content shapes perceptions of trust. While earlier phases focused on the credibility effects of publishers, contexts, and evidentiary cues, Phase III will explore how these dynamics interact with emerging forms of machine-generated media.\n\nAt the center of this phase are several core questions. Do audiences distrust AI-generated news because of specific characteristics of the content itself, or does the mere presence of an AI label trigger skepticism? Are certain elements of AI-assisted production—such as automated writing, synthetic imagery, or algorithmic editing—more likely to erode trust than others? And critically, can the reputational capital of an established, trusted publisher offset any credibility penalties associated with AI involvement?\n\n## What this platform makes possible\n\nFor publishers, the platform was able to demonstrate that journalistic rigour and context remain powerful trust assets in a fragmented media environment. For advertisers, it showed that trust shapes brand perception independently of creative execution. For platforms and policymakers, it provided a rigorous foundation for understanding how emerging technologies, including AI, can either erode or rebuild credibility depending on deployment.\n\n## Vox Pop Labs capabilities demonstrated\n\nThe News Effectiveness Study illustrates Vox Pop Labs’ ability to design and execute rigorous research as decision-making infrastructure, including large-scale survey experiments, nationally representative sampling, advanced weighting and multilevel modeling, academic-grade methodological transparency, and translation of complex findings into usable decision frameworks.\n\nThese capabilities are designed to be deployed across sectors where trust, legitimacy, and evidence quality are central to high-stakes decisions.\n\n## Why trust needs evidence\n\nThe News Effectiveness Study demonstrates what becomes possible when questions about trust are approached not as abstract debates or communications challenges, but as measurable systems that can be studied, tested, and improved over time.\n\nFor Vox Pop Labs, it exemplifies a core service offering: building research platforms that combine experimental rigour, ethical standards, and real-world relevance, and delivering them in forms that persist beyond a single report to inform ongoing decisions in complex information ecosystems.\n"}},"link":{"text":"Project link","url":"https://trustinmedia.ca/ ","page":null},"media":[{"uuid":"productLogo","name":null,"image":{"url":"https://s3.ca-central-1.amazonaws.com/site.voxpoplabs.com/trustinmedia_logo_3ec6f17750.png"},"imageAlt":"The news effectiveness study logo"},{"uuid":"productImage","name":null,"image":{"url":"https://s3.ca-central-1.amazonaws.com/site.voxpoplabs.com/news_effectiveness_product_new_e8bd7ba606.png"},"imageAlt":null}],"partners":[],"timeline":[],"statistics":[],"miscText":[]}]},"bottomNav":{"menus":[{"uuid":"","name":"Democratic engagement","navMenuLinks":[{"text":"Vote Compass","url":null,"page":{"path":"/vote-compass"}},{"text":"The Signal","url":null,"page":{"path":"/the-signal"}},{"text":"Polimeter","url":null,"page":{"path":"/polimeter"}}],"link":null},{"uuid":null,"name":"Public opinion research","navMenuLinks":[{"text":"OurCare","url":null,"page":{"path":"/our-care"}},{"text":"COVID-19 Monitor","url":null,"page":{"path":"/covid19-monitor"}},{"text":"News effectiveness study","url":null,"page":{"path":"/news-effectiveness-study"}},{"text":"Australia Talks","url":null,"page":{"path":"/australia-talks"}}],"link":null},{"uuid":"decisionTools","name":"Decision science","navMenuLinks":[{"text":"Vote Compass","url":null,"page":{"path":"/vote-compass"}},{"text":"DegreeHub","url":null,"page":{"path":"/degreehub"}},{"text":"Career Myth Buster","url":null,"page":{"path":"/career-myth-buster"}}],"link":{"url":null,"page":null}}],"sections":[{"uuid":"bottomNav_panel","title":"Join the Thought Leader Community","description":{"data":{"description":"Become a member of our online respondent panel, made up of people from all over the world and all walks of life. 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many young people, most Australians say(Stephanie Chalmers)"},{"product":"australiaTalks","headline":"VIDEO: Does Australia respect its older citizens?","region":"Australia","subRegion":null,"sourceName":"ABC","sourceUrl":"https://www.abc.net.au/news/2021-06-20/does-australia-respect-its-older-citizens/13401434","date":"2021-06-19","image":{"url":"https://s3.ca-central-1.amazonaws.com/site.voxpoplabs.com/Screen_Shot_2022_08_04_at_11_06_18_AM_2ff6bc9a3d.png"},"imageAlt":"https://www.abc.net.au/news/2021-06-20/does-australia-respect-its-older-citizens/13401434"}]},"methodologies":{"nodes":[{"id":"a40f1cbf-2108-5d21-bac2-f19d8dff2794","path":"/the-signal/methodology","title":"The Signal Methodology","locale":"en","content":{"data":{"content":"The Signal is based on the mechanics of a Bayesian dynamic linear model. Our variant of the model accounts for two biases in the polling industry. First, we account for the fact that pollsters differ systematically between each other with respect to whether they over- or under-represent certain voters. For example, compared to the polling industry average, some pollsters might over-represent Progressive Conservative party voters; others, NDP voters. The model accounts for these differences dynamically, such that each poll that is released is filtered for our current estimate of that bias. Polls over multiple years are used to calculate these “house biases” so that they themselves are recalculated each time a new poll is released. Second, we account for bias in the polling industry as a whole by using data from previous elections. In the Canadian context, these biases are relatively small, although not insignificant: even small differences in national vote share can have relatively large effects on seat share.\n\nBecause there are many days on which polls are not released and because polls contain sampling error, the model uses information about where vote intention stood on one day to inform where it stands the next day. If a new poll is released, vote share estimates for that day effectively become a weighted average of information from the newly released poll and from information about where vote intention stood on the previous day. This means that outlier (and all other) polls are effectively pulled in toward the previous day’s forecast. Visually, this means that vote intention across time will appear relatively smooth, as we would expect it to in reality.\n\nThis differs from other forecasting models in Canada where one might see forecasts jump around relatively drastically from poll release to poll release. Unfortunately, this leads many commentators in Canada to speculate that large changes are occurring in the electorate for various reasons related to the campaign even if no substantial changes are occurring in reality. An added benefit is that a new forecast can be released for each day of the campaign, even if no new poll is released, and we are able to estimate vote intention for every day of the campaign.\n\nTo estimate regional-level vote share, we run a separate model with the same basic structure as the national model. We then adjust the regional vote share results proportionally such that they match estimates from the national-level forecast. For riding-level predictions, given that the federal electoral boundaries have been updated since the last federal election, we use Elections Canada data to map the results of the previous election to current electoral boundaries. To produce estimates in each riding, we adjust all party results for each seat proportionally to match the estimated regional (and national) vote share forecast. In addition to these uniform swings, we account for incumbents running as candidates in a riding they have previously held. It is worth noting that the degree of uncertainty with respect to these projections is highest at the individual riding level. As riding-level predictions are derived from federal vote share estimates and vote share in the last federal election rather than local polling data (which do not exist in sufficient numbers), they should be interpreted with due caution."}},"productLogo":null,"product":{"id":"870a2f03-3913-5049-9b8c-07b41b4af46c","path":"/the-signal","name":"The Signal","uuid":"thesignal"},"seo":{"metaTitle":"Vox Pop Labs - The Signal - Methodology","metaDescription":"Empowering informed decisions. 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