According to Reuters, the company is working with Indian startup Proto to provide the judgment, which will return ‘true’, ‘false’, ‘misleading’, or ‘disputed’. It’s also working with Dig Deeper Media and Meedan to provide tools to verify content. The messages collected during the tipline’s functioning will be used to build a database of content to better detect it in the future. It’s clear that the misinformation is a concern in the country, with Facebook recently cracking down on 712 accounts linked to Indian political groups and Pakistani military. Executives at WhatsApp have previously admitted there has been ‘inauthentic behavior’ from various parties. Marketing companies in the country have been known to sell the ability to mass message thousand of groups with little oversight.
The Challenges of a Closed System
Due to WhatsApp’s encrypted personal messaging focus, it can be harder to promote logical discussions when misinformation is shared. Unlike Facebook, Reddit, or Twitter, posts aren’t broadcast to millions for comment. For now, Checkpoint Tipline is one of the best solutions available, but it seems it’s having some growing pains. Reuters says a message to the service wasn’t answered for over two hours, and TNW reports a similar experience. It’s likely there are currently a high volume of users utilizing the service. As the amount of data and AI proficiency improves, systems like this could be fully automated with a high degree of accuracy. WhatsApp could then implement a verification feature that would require only a few taps. However, it doesn’t look like that future will be a reality any time soon. It’s much harder and more expensive to combat fake news than it is to create it, so the best option is a concentrated effort during sensitive periods. Previously, though, WhatsApp has limited the number of times a message can be forwarded and improved its reporting features. It also recommends fact-checking services Boom Live, Alt News, and Etka. It will be interesting to see what conclusions WhatsApp and its partners pull from the service’s data. By reacting quickly as an article trends, it may be able to get the word out about misleading content before it reaches the masses.
