How might Collective Intelligence help close the climate misinformation distance gap to support better decision-making?

Most existing examples of collective intelligence to combat misinformation combine automated approaches and crowdsourcing for fact checking and moderation of online content. For example, CoFacts is a Taiwanese platform that invites the public to check any text they suspect contains misinformation on the popular messaging platform Line using a chatbot. When statements are submitted for fact checking, they’re verified by other CoFacts volunteers. CoFacts aims to curb the spread of misinformation on closed social networks such as chat groups where it can often be difficult to track. In 2018, CoFacts helped users verify messages about LGBTQI+ rights prior to a divisive vote on same-sex marriage. Factmata is another general-purpose tool that can be used to identify harmful online content. They use AI models that are regularly retrained by a community of experts to detect propaganda, hate speech and misinformation in near real-time.

Another promising Collective Intelligence approach to curtail the amplification of inflammatory and false content in online spaces is crowdsourced community moderation. For example, the r/Science community on Reddit showed that actions taken by existing members, for example regular reposting of community principles, helped to reduce the spread of fake news and increased rule compliance for posted content by eight percent. Crowdsourcing can also help with early detection of misinformation offline. Medicins Sans Frontier (MSF), the International Federation for Red Cross and UN Global Pulse are already developing tools that use social media analysis, crowdsourcing or community reporting to identify and verify rumors that might interfere with response operations during crises. For example, the Wikirumours platform that was developed by the Sentinel Project to crowdsource damaging rumors in conflict-affected regions, has been adapted by MSF to identify disinformation. Spotting new rumors at an early stage allows frontline organizations to adapt so they don’t interfere with active programmes in the field.

Online discussions about climate are highly reactive to real-world attitudes and policies around climate. Analyzing how social media narratives change over time could provide valuable insights into policy interventions and agreements that are most successful at shifting societal norms around climate. For example, sentiment analysis of public social media discourse showed an increase of 30-40 percent in negative sentiments such as “fear” and “sadness” following the publication of high-profile IPCC reports. Social media can also be used to understand the spread of health-related misinformation. In the wake of the Zika outbreak in 2016, researchers demonstrated the potential of using machine learning and crowdsourcing of social media data to tailor the containment actions of health officials.