Philip N. Howard

Threats to public understanding of climate arise in every language, but scientists publish the evidence about such trends almost entirely in English, from and about a narrow set of countries (SR2025.1). What do researchers who are writing in other languages find? Where do findings converge, and what gaps remain? Through the Global Initiative for Information Integrity on Climate Change, the United Nations and UNESCO asked the IPIE to evaluate global research trends. This is a strategic review of the most prominent science published in Arabic, Chinese, French, Portuguese, Russian, and Spanish, alongside the main body of English-language research. The assessment applied nearly 90 translated search terms, including simplified and traditional Chinese script, across 32 scientific databases between June and July 2026. The review provides four key findings: Research is converging on how evidence about climate change is manipulated and misused, and on organized resistance to science across all studied languages. Each literature independently finds similar tactical evolutions, from climate denialism to policy obstruction. Initiatives defending climate information integrity are emerging slowly. None appear in Chinese, French, or Russian research, though the Intergovernmental Panel on Climate Change (IPCC) remains a credible reference globally. Shared blind spots mark every literature. Few studies track trends across platforms, and researchers rarely have the data access needed to study them systematically in their own languages. The uneven geography of knowledge production threatens climate action. The communities most exposed to climate impacts are the least visible in the published evidence. Two policy priorities follow: Researcher access to diverse data, including social media data held by technology firms, remains a critical policy priority. Climate strategy must include information integrity, with multilingual responses informed by the global research community. This strategic review provides the first multilingual map of research on information integrity about climate. Sound climate policy requires protecting public understanding of climate issues worldwide, regardless of language.

How much do experts on the information environment agree about the scale, spread, and effects of misinformation? We drew on three annual waves of the International Panel on the Information Environment expert survey to examine agreement with seven popular claims about misinformation (2023: N = 262; 2024: N = 413; 2025: N = 442; spanning 76 countries). Agreement varied widely across statements and was stable across waves. About 60% of experts agreed that falsehoods spread faster than the truth and that misinformation is widespread. About half agreed that misinformation has a substantial influence on behavior and that people readily believe what they see online; about 40% agreed that more people have become misinformed over time; and only 25% agreed that misinformation is primarily a social media problem. The surveys also revealed a persistent divide between experts who saw misinformation as widespread and consequential and those more skeptical, with sharpest disagreement on its behavioral impact and increase over time. In 2025, some experts distinguished between misperception prevalence and misinformation severity. Overall agreement varied by respondent background: agreement was higher among experts on developing countries than those on developed countries, higher among STEM researchers than social scientists, and lower among political scientists than communication scholars. Expert consensus on misinformation is partial, varies across claims and experts, and is shaped by geography and discipline.

The information environment is under great pressure, and so are the researchers who study it. People increasingly receive information already sorted for them, by recommendation algorithms or by generative AI systems that answer directly rather than link to sources. How do experts around the world assess threats to the information environment? In May and June 2026, the IPIE surveyed 470 researchers with expertise on 71 countries in the fourth and most diverse wave of its annual expert survey. Nearly half the experts study developing economies. This Summary for Policymakers presents the main findings of Trends in the Information Environment: 2026 Expert Survey Results (SR2026.3). The report reaches four main conclusions: AI summaries deliver speed at the expense of visits to news websites and viewpoint diversity. Conditions for research on the information environment have worsened by every measure, most notably in the USA. Experts are most worried about unaccountable platform power, followed by misinformation and polarization. Pessimism stands at a record level, and it is deepest where research conditions are rated better. The full report presents these findings in detail, with further observations on the technologies experts view most favorably and on how perceived risks from AI-generated content compare with experimental evidence.

In May and June 2026, the IPIE’s fourth annual expert survey included 470 experts studying the information environment across 71 countries, almost half of whom focused on developing economies. Pessimism has climbed to its highest level yet. This year, 76% of experts expect the information environment in their country of expertise to worsen over the coming year, up from 53% in 2023. For four years running, experts agree on what a healthy information environment needs most: accurate information and a diversity of voices. Most experts, 76%, say people can find diverse viewpoints online, but only 28% think a majority of people in their country trust the main news outlets to report politics accurately. Lack of platform accountability and misinformation are seen as the biggest threats to the information environment, and experts consider generative AI a less urgent threat. For three years in a row, experts expect generative AI to make the information environment worse over the next five years, with AI-generated video their biggest worry and AI-generated text their smallest. Experts expect AI summaries in search engines to speed up how quickly people find what they are looking for, but to cut traffic to news sites and narrow the range of viewpoints they see. Search engines again rank as the technology seen most positively, 80% positive, while views on social media, generative AI, and recommender systems are mixed to negative, 35–45% positive. Research funding remains the most frequent barrier to studying the information environment, 67%, ahead of data access, 60%, and roughly a third of experts report government-related pressures on their work. Behind these overall figures, experts in developing economies and the USA show similar patterns: they rate threats higher, report more government censorship and surveillance, and view the effects of technologies, including AI, similarly. USA experts are nearly twice as likely as experts in all other countries to report government control of funding and pressure to align research with a political agenda as main barriers to studying the information environment. Experts fall into three different groups based on their concerns: about half worry about almost every threat to the information environment, a third worry mainly about platform accountability and misinformation but not AI, and one in ten hardly worry at all.

Political information environments around the world are facing the twin challenges of misinformation and disinformation, which degrade the quality of public discourse, undermine political stability, and weaken social cohesion. It is therefore important to understand the conditions under which countries are at risk from misinformation and disinformation. In recent years, several indexes have been developed by scholars, think tanks, advocacy groups, and technology companies aimed at diagnosing and comparing these risk and protective conditions. Yet, systematic cross-index comparison remains limited. This Synthesis Report (TP2026.1) addresses this gap by developing a typology of existing measures, providing a systematic comparative analysis of ten published indexes, and offering a fitness-for-purpose guide for researchers, policymakers, and index producers. Our analysis reveals three key insights: The concepts of vulnerability and resilience provide a foundational framework for understanding how political misinformation and disinformation are amplified or mitigated and for comparing countries over time. However, because most indexes rely on structural, perceptual, or behavioral proxies rather than direct measures, their scores should therefore be read as comparative diagnostics rather than estimates of disinformation prevalence. Several indexes offer valuable and complementary insights, but they diverge in their theoretical assumptions, methodologies, and the aspects of the political information environment they emphasize. Caution is therefore required to avoid overgeneralizing cross-national rankings, and triangulation across complementary measures is often advisable. Although the ten indexes considered in this report were designed to address political misinformation and disinformation, their underlying logic can be applied to the broader task of measuring political information environments and information integrity. By evaluating the strengths, good practices, and shortcomings of these ten indexes, this report highlights the critical dimensions and measures that can guide a theoretically informed, methodologically robust, and globally sensitive next-generation index. It concludes with guidance for using index results responsibly.

This report synthesizes experimental evidence on the effects of generative artificial intelligence (GenAI) misinformation on individuals’ ability to evaluate information and the effectiveness of interventions designed to mitigate its influence. It draws on a meta-analysis of experimental studies (60 effects from 24 publications and 33,801 participants) published between 2018 and 2025. The findings indicate a divergence in public responses to different modalities of GenAI-produced misinformation (referred to in the report as GenAI misinformation), alongside substantial heterogeneity across studies. More recent research tends to report higher perceived accuracy and credibility of textual GenAI misinformation, whereas individuals’ perceptions of visual GenAI misinformation—primarily deepfakes—have become more skeptical over time. These patterns are associated with the time of data collection rather than model capacity and are accompanied by wide variation in effect sizes across contexts. The report also evaluates two countermeasures: preventive corrective information and content labeling. In studies conducted after 2020, corrective information yields small-to-moderate (0.17 to 0.43 standard deviation units) reductions in the perceived accuracy or credibility of GenAI misinformation. Pooled estimates in this subset are significant and comparatively stable, though prediction intervals show substantial variation, especially when outcomes focus on detection rather than evaluative judgment. Content labeling interventions vary more than corrective information. Althoughlabels are associated with modest average reductions in perceived credibility, effect sizes vary widely across modalities, designs, presentations, and contexts,and prediction intervals often include null effects. Key conclusions from the synthesis are as follows: Textual GenAI misinformation may currently pose greater persuasive risks than visual GenAI misinformation. Preventive corrective information appears to be more a consistently effective countermeasure. Content labeling policies yield highly variable outcomes and require careful design and evaluation. The available evidence base is geographically narrow and methodologically constrained, with most studies focusing on populations in Western Europe and North America Based on these findings, policy responses should consider the following: Prioritize addressing text-based GenAI misinformation. Treat preventive corrections as a core intervention. Approach content labeling cautiously

Generative artificial intelligence (GenAI) can now rapidly produce large volumes of misleading text, images, audio, and video with readily accessible tools. This matters for policymakers because false or misleading content can be tailored to specific audiences. Such content can spread quickly and mimic authentic communication, making it difficult for people to judge what is true. This Summary for Policymakers distills the main findings of the IPIE Synthesis Report (SR2026.2) on the effects of misinformation produced with GenAI and the measures most likely to reduce its influence. The assessment draws on a large-scale meta-analysis of experimental scientific evidence. It is based on 60 randomized controlled trial effect estimates from 24 peer-reviewed publications involving 33,801 participants, published between 2018 and 2025. The report reaches four main conclusions: Text-based GenAI misinformation currently poses greater persuasive risks than visual misinformation. The current evidence base excludes most of the world. Research is concentrated in English-language and high-income countries, leaving significant gaps in our knowledge. The most consistently effective intervention is to provide users with corrective information, preventively, so they can evaluate accuracy and credibility themselves. Labeling is effective when it is consistent. Content labeling generally decreases perceived credibility, but its impact varies greatly depending on how the firms create and apply labels. The policy implications are clear. Policymakers should prioritize addressing misinformation from text-based GenAI and support preventive and corrective information as a fundamental strategy. Labeling should be treated as a measure that requires careful design and testing. We must expand independent research well beyond English-language and high-income contexts. Researcher access to platform and model data is crucial for enhancing public understanding of GenAI misinformation and for testing which safeguards are effective in practice.

Reports of online grooming, financial sextortion, livestreamed abuse, and AI-generated child sexual abuse material continue to rise across many regions. Existing interventions struggle to keep pace with technological change, allowing offenders to operate across borders with limited risk of detection or disruption. Using standard PRISMA protocols, the review provides a methodologically rigorous synthesis of more than 100 studies on interventions published over the past decade. The review assesses technical, legal, policing, behavioral, and educational interventions and provides four key findings: Most interventions focus on detecting abuse after it occurs. Far fewer disrupt the systems that enable exploitation and abuse, such as payment mechanisms and advertising and recruitment pathways, or address limited judicial capacity that falls short of deterrence or enforcement. Technical tools may reduce harm at scale but depend on legal authority, secure data access, safeguards, and effective enforcement. Without these, such automated and AI-assisted tools have limited impact. Behavioral and educational interventions can reduce risk and increase awareness but cannot replace platform accountability. Evidence of sustained behavior change remains limited without funding, including for longitudinal studies. Financial systems are the most underused leverage point against TF-CSEA. Few interventions disrupt payments financing abuse, focusing instead on tracing transactions after harm occurs. The synthesis provides the most comprehensive assessment of TF-CSEA interventions to date and was led by the IPIE’s Scientific Panel on Child Protection and Social Media. The findings demonstrate a clear consensus on the need for coordinated legal authority, scalable technical tools, sustained enforcement, and action to interrupt financial flows.

The occurrence of technology-facilitated child sexual exploitation and sexual abuse (TF-CSEA) is well documented. However, there is no comprehensive overview of interventions, how well they work, and which aspects of the TF- CSEA offending chains these interventions address. This gap makes it difficult to set up policy, planning, and coordination of interventions across agencies and countries to prevent, detect, deter, investigate, and prosecute these crimes. This systematic review of academic peer-reviewed literature (n = 86) and organizations’ reports (n = 18) bridges this gap. The review follows the PRISMA protocol on evidence sampling, screening, and analysis. This Synthesis Report offers global insights into types of TF-CSEA interventions, gaps and challenges in existing intervention arrangements, and implications for the TF-CSEA offending chain. There are four key findings: Technological tools and protocols are the most studied and documented types of intervention, followed by policing and investigation, and legal and regulatory interventions. As a result, detection and investigation; child sexual abuse material (CSAM) generation, storage, and distribution; and grooming are the most studied aspects of TF-CSEA offending chains and intervention pathways. Given the growing uses of digital payment and gift exchange platforms in financing TF-CSEA, legal duties to report suspicious transactions should be extended to these platforms. Technological tools and protocols show promising results. However, these interventions are only as good as their databases of suspected or verified CSAM materials, communication logs and training data. But access to these materials is riddled with ethical and legal dilemmas. Protocols and approaches are needed to develop and test technological tools and protocols in ways that restrict direct access to CSAM data and ensure they do not leave the storage facilities of law enforcement agencies. Legal and regulatory interventions that have positive effects harmonize definitions of TF-CSEA, and set criminal sanctions globally. Impactful laws and regulations tend to have extraterritorial scope, imposing transparency and accountability measures on digital providers. However, the limits of these interventions are set by varying levels of enforcement and the absence of standardized practice interventions. Education, literacy or awareness raising campaigns for children also show promising results as the enable children to recognize behaviors and online interactions that could put them in danger. These findings indicate the need for further behavioral research to establish whether such intervention leads to desired behavioral outcomes. Funding priorities should, therefore, be recalibrated.

This Summary for Policymakers provides a high-level précis of the synthesis report, "Confronting Misinformation Produced with Generative AI: A Meta-Analysis of Experimental Scientific Evidence." Generative artificial intelligence (GenAI) can now rapidly produce large volumes of misleading text, images, audio, and video with readily accessible tools. This matters for policymakers because false or misleading content can be tailored to specific audiences. Such content can spread quickly and mimic authentic communication, making it difficult for people to judge what is true. This Summary for Policymakers distills the main findings of the IPIE Synthesis Report (SR2026.2) on the effects of misinformation produced with GenAI and the measures most likely to reduce its influence. The assessment draws on a large-scale meta-analysis of experimental scientific evidence. It is based on 60 randomized controlled trial effect estimates from 24 peer-reviewed publications involving 33,801 participants, published between 2018 and 2025.