Free flow of reliable and independent information is absolutely critical to public health and accountability. However, with several restrictions imposed on media across different regions, people have limited access to independent information, and few spaces to dialogue and discuss the adequacy of measures being taken by their governments to contain the pandemic. This challenge is taking place amid a backdrop of an incredibly complex and evolving information environment, in which journalists and fact-checkers are expected to distill complicated research on epidemiology and infectious disease.
People need news and information they can trust. Instead of making this available, governments are censoring news, curbing the free flow of information and scrutinizing voices that seek accountability.
In order to analyze and discuss potential solutions to the current misinfodemic, Meedan hosted an online event on June 18, 2020 entitled, ‘Reporting Barriers During COVID-19. The session had veteran journalist and co-founder of Rappler, Maria Ressa in conversation with Dima Saber, Meedan’s Director of Check Global Program and Impact.
About the Series: Women, Media and the Pandemic
This event is first in the Check Global Network’s webinar series ‘Women, Media and the Pandemic’. Launched on May 5, 2020, Check Global members are currently located in the Philippines, Indonesia, Pakistan, Syria, Yemen, Western Sahara, Kenya, Zimbabwe and Mexico. As a community of independent media, technology and human rights groups, the Check Global Network plans to meet regularly to discuss solutions to challenges, to design collaborative projects and to respond proactively to the needs of partners in emerging economies.
For more information about joining the network, please write to us at checkglobalnetwork@meedan.com with a statement of interest.
Footnotes
- 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.
