There is a growing expectation for individuals to challenge misinformation, especially as platforms alter their governance processes around false content, shifting towards community-flagging approaches.  While research on how individuals respond to exposure to information is developing, findings often taker a broad-brush approach, treating receivers as a single, undifferentiated category, identifying overarching barriers, motivations and coping mechanisms relating to challenging misinformation as opposed to highlighting how these differ between groups of individuals.

We conducted a design evaluation exploring whether a structured crowdsourcing system using trained volunteers could monitor and categorize election-related misinformation during the 2025 Australian federal election campaign. Over six weeks, 90 active participants surfaced 592 potentially misinforming posts and provided over 3,000 assessments across multiple platforms and recurring narratives.

Due to extensive regulations, pseudoscience and accusations of censorship are increasingly used as persuasive strategies in health misinformation. How effective are these? This experiment examines the effects of pseudoscientific misinformation about COVID-19 vaccines and genetically modified organisms (GMOs) by accounting for the rhetoric of the “persecuted hero narrative” (Baker, 2022), which involves accusations of censorship against media, platforms, and governments.

Open-source investigators and OSINV journalists have spent the last decade turning publicly available data into evidence of war crimes, environmental harms, and human rights abuses. More recently, they have trained the same lens on a different target: the algorithms, bot networks, and generative AI systems that industrialise deception and information disorder.

Fact-checking on social media matters more than ever. When Meta abandoned professional fact checkers in 2025 in favour of crowd-sourced annotations, it bet that community participation could substitute for institutional expertise. Our systematic review of 21 empirical studies of social media fact-checking (2022–2025) suggests the picture is more complicated than it appears.

When 54 international experts assessed AI-generated disinformation threats, they revealed a surprising pattern: while video deepfakes received the highest average threat ratings in the political domain (M = 6.31/7), the pattern differed in the health domain, where AI-generated text received the highest average rating (M = 5.80).