Fighting disinformation and misinformation

Empower users to recognize, find, and correct content that was created based on misinformation

Last updated: 2022-12-16 by APaskulin (WMF)
Status: v1 published September 2021

Narrative

Marguerite is a long-time editor interested in articles that touch on current events. She discovers that a certain magazine has been involved in disinformation attempts and is no longer considered a valid source. She uses the system to look for all pieces of content that have been cited to this magazine, and uses that information to create an analysis of the source misinformation attempts, as well as to remove all now unsourced information in the various articles.

Enabled by these architecture patterns

Canonical data model: Treating citations as a piece of knowledge rather than “just” metadata to other pieces allows us to utilize them in the search for information, and in the addition of strong verifiable and transparent information.
Event-based interactions: In an event-based interaction, an edit to a page triggers an event. Different subsystems can respond to this event and manipulate the data appropriately. Each edit can be processed to extract the citations as standalone objects, ready to be categorized and contextualized. This means that at the time of fetching, we can give the user a robust experience based on the relationships between the content and the citations themselves.