The technology nonprofit is working with Noticias Telemundo, Univision, Agence France Presse and Animal Politico on the effort supported by the Knight Foundation.
Using Meedan’s flagship app, Check, the newsrooms participating in the midterm elections project can solicit and respond to high volumes of questions from their WhatsApp audiences. Machine learning lets the newsrooms effectively manage the influx of requests by grouping and sorting them, so reporters and fact-checkers can respond quickly to audience members with similar questions.
Since 2012, Meedan has won three ONA awards and a 2022 IFCN Impact and Innovation Award for collaborative election reporting and fact-checking. This is the latest project aimed at providing newsrooms with necessary technical infrastructure and programmatic support to improve the integrity of our digital media and information spaces ahead of a critical political event.
“The U.S. midterms project is designed as a runway to the 2024 presidential primaries and general election, during which we plan to develop robust pathways for misinformation data sharing between newsrooms,” said Meedan CEO Ed Bice.
This midterm election cycle is fueling misinformation on closed messaging apps like WhatsApp— especially in non-English languages. The goal of the 2022 project is to help stabilize the information ecosystem at scale as Americans head to the polls.
“The 2022 U.S. midterms initiative will boost Spanish-language audiences with high-quality information and test a model for a higher-profile collaborative reporting project in 2024,” said Pierre Forcoli-Conti, Meedan’s Director of Product.
“It is essential to provide Spanish-only speakers with a tool they can easily manage to face the hoaxes they get from their familia y amigos on Whatsapp groups,” said Gemma Garcia, SVP of Digital News at Noticias Telemundo.
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- Online conversations are heavily influenced by news coverage, like the 2022 Supreme Court decision on abortion. The relationship is less clear between big breaking news and specific increases in online misinformation.
- The tweets analyzed were a random sample qualitatively coded as “misinformation” or “not misinformation” by two qualitative coders trained in public health and internet studies.
- This method used Twitter’s historical search API
- The peak was a significant outlier compared to days before it using Grubbs' test for outliers for Chemical Abortion (p<0.2 for the decision; p<0.003 for the leak) and Herbal Abortion (p<0.001 for the decision and leak).
- All our searches were case insensitive and could match substrings; so, “revers” matches “reverse”, “reversal”, etc.