Misinformation has become one of the defining concerns of the digital age, but for all the public worry, there is surprisingly little agreement about what the term actually covers. In a recent effort to map the expert landscape, researchers surveyed 150 academic specialists across political science, psychology, communication, sociology, and computational science. They asked how misinformation should be defined, why people believe and share it, what works against it, and where the field should go next. The results paint a picture of broad consensus on some points, sharp disagreement on others, and a clear sense that the conversation needs to become more global and more interdisciplinary. On the most basic question—what is misinformation?—experts converged on a simple definition: false and misleading information. They also largely agreed that pseudoscience and conspiracy theories belong under that umbrella, while satirical and parodical news does not. But the simplicity ended there. Some experts insisted that misinformation must be unintentional, to distinguish it from disinformation, while others saw no need for that condition. Qualitative researchers were more likely than quantitative researchers to include the sharer’s intent in their definition. Psychologists tended to have broader definitions, including propaganda and hyperpartisan headlines, while political scientists were much more cautious about labeling such content misinformation. These may sound like academic quibbles, but they have real consequences. How misinformation is defined shapes what platforms flag, what researchers study, what journalists call out, and what governments try to regulate—and a vague or weaponized definition can be used to justify silencing political opponents. The authors do not claim to settle the debate; instead, they want to make the disagreements visible so that researchers, policymakers, and practitioners can be more careful about the words they use and the assumptions they bring.
The definitional disagreements are not just a matter of taste; they affect the very findings that make headlines. For example, most experimental studies of misinformation focus on articles that fact-checkers have rated false. But the experts in this survey were clear that misinformation is not only falsehoods—it is also misleading information, which is far harder to measure and much easier to spread. This suggests that the field may be missing subtle but significant forms of misinformation, such as biased or partisan news that is technically accurate but designed to deceive. The experts were also divided on whether propaganda, rumors, clickbait headlines, and hyperpartisan news should be considered misinformation. Political scientists were far less likely than psychologists to include hyperpartisan headlines or propaganda, while computational scientists were more open to classifying clickbait as misinformation. Again, these are not merely semantic disagreements. They can create contradictory findings across studies, since two teams of researchers may think they are studying the same phenomenon when in fact they are looking at very different content. The authors suggest that researchers should be much more explicit about their definitions and should broaden their scope beyond the narrow category of fact-checked false news. If the field continues to focus only on blatantly false articles, it risks ignoring the everyday gray areas where most people actually encounter misleading information: emotionally charged headlines, selectively edited clips, out-of-context quotes, and partisan commentary dressed up as news. The experts’ responses also carry a warning: if scholars cannot agree on what misinformation is, policy responses based on their work may be easily exploited. The solution is not necessarily one universal definition, but rather a shared awareness that definitions matter and should be stated clearly in every study, article, and policy document.
When it came to why people believe and share misinformation, the experts were much more united. The single most agreed-upon explanation was partisanship: 96 percent of experts said it was a key reason people share misinformation, and 93 percent said it was a key reason people believe it. Close behind were identity, confirmation bias, motivated reasoning, and lack of trust in institutions. Repeated exposure, inattention, lack of cognitive reflection, and poor digital or media literacy also received substantial support. But the least popular explanations were lack of education and lack of access to reliable news. This is striking because public debate often assumes that misinformation spreads because people are uneducated or because they simply do not know any better. The experts reject that framing. They see misinformation as less a problem of ignorance and more a problem of social identity and political motivation: people believe and share content that affirms who they are and what they already want to be true. This has major implications for interventions. Many current efforts focus on improving media literacy, teaching critical thinking, or helping people spot false headlines. Those tools can help, but the experts suggest that they target only a small part of the problem. If partisanship is the main driver, then interventions that ignore politics and group identity may have limited impact. In fact, the mismatch between where experts think the problem lies and where current solutions are directed is one of the most important messages of the study. Platforms, journalists, and educators may need to rethink their strategies: instead of simply telling people to fact-check more, they may need to address the deeper motivations that make misinformation attractive in the first place.
The survey also explored how experts view the broader digital media landscape, and here their opinions were often more measured than public alarm might suggest. A large majority—79 percent—agreed that social media platforms have made the misinformation problem worse. Most experts also agreed that people are exposed to more opposing viewpoints online than offline, which complicates the popular image of social media as a sealed echo chamber. The experts were less convinced by the claim that falsehoods generally spread faster than the truth on social media; only 53 percent agreed. And only 47 percent believed that people sincerely believe the misinformation they report believing in surveys. In other words, experts suspect that some survey respondents may exaggerate or perform their beliefs, and that alarmist survey numbers should be taken with a grain of salt. Perhaps most surprisingly, only 32 percent of experts agreed that belief in misinformation and conspiracy theories is higher than it was ten years ago, while 36 percent disagreed and 32 percent were unsure. This is a far cry from the common narrative that we are living in a new “post-truth era” of unprecedented delusion. The most polarizing question was whether misinformation played a decisive role in the 2016 U.S. presidential election. Overall, experts were split, but the split was highly revealing: 54 percent of psychologists agreed that misinformation was decisive, while 73 percent of political scientists disagreed. The authors suggest that this disagreement may reflect different disciplinary assumptions, different definitions of misinformation, and different evidentiary standards. For journalists and policymakers, the lesson is clear: don’t rely on a single expert or a single discipline. The truth about misinformation’s political power is complicated, and even the experts themselves do not agree.
On the question of solutions, the experts were surprisingly optimistic. They generally believed that existing individual-level interventions—such as media literacy training, fact-checking, and inoculation—would be effective if they were deployed at scale and widely adopted by social media companies and institutions. The most popular individual-level intervention was digital and media literacy training, supported by 72 percent of experts, while the least popular was inoculation, still supported by 58 percent. But the experts were even more enthusiastic about system-level changes. Platform design changes received 89 percent support, algorithmic changes 84 percent, and content moderation on social media 80 percent. They also favored de-platforming prominent repeat offenders (73 percent), stronger regulation to hold platforms accountable (70 percent), crowdsourcing misinformation detection (66 percent), and removing misinformation (63 percent). Even penalizing people for sharing misinformation on social media received majority support, at 53 percent. The only action that clearly failed to win approval was shadow banning, which was opposed by 46 percent of experts and supported by only 33 percent. This enthusiasm for structural solutions suggests that the experts do not see misinformation as a problem that individuals can solve alone. They are wary of placing the burden on ordinary users and instead believe that platforms, regulators, and institutions must take responsibility. At the same time, the experts’ optimism was tempered: they mostly selected “tend to agree” rather than “strongly agree,” which may reflect an awareness that no single intervention is a silver bullet. The real hope lies in combining approaches—better design, better algorithms, better moderation, better regulation, and better public education—so that each one reinforces the others.
Looking forward, the experts were remarkably united on where the field should go. The most supported future direction, endorsed by 95 percent of experts, was to collect more data outside the United States. This is a direct response to a major imbalance in misinformation research, which has long been dominated by American samples, American platforms, and American political contexts. The experts also called for more interdisciplinary work (93 percent), better theories of why people believe and share misinformation (88 percent), more interventions (85 percent), and a move beyond Twitter and Facebook to study platforms like TikTok, WhatsApp, and Telegram (85 percent). They supported studying subtler forms of misinformation (81 percent), developing better detection tools (81 percent), and moving away from social media data to study offline misinformation (59 percent). The last point revealed another disciplinary divide: 69 percent of psychologists wanted more offline research, while only 38 percent of computational scientists agreed. The authors note that these recommendations will require structural support, not just good intentions. Researchers in underrepresented countries need funding, infrastructure, and access to platform data. Universities need to break down silos between departments. Journals and funding agencies need to reward interdisciplinary and internationally diverse research. Without those changes, the field will continue to tell a narrow story about a global phenomenon. The authors also acknowledge limitations: most experts in the survey came from the Global North, so the findings may not reflect perspectives from the Global South, and the differences between disciplines could be partly semantic rather than substantive. Still, the overall message is hopeful and constructive. If researchers can embrace clearer definitions, broader collaborations, and a more global outlook, they will be far better equipped to understand misinformation—and to help the rest of us navigate a world where false and misleading information is everywhere.