|MetaFact's fact-checking tool aims to detect and monitor fake news in real-time / Credits: sdecoret via Shutterstock|
Fake news is now commonly seen across all media platforms, but with great repercussions. For instance, disinformation in India has led to grave crimes and even communal and political unrest. A report showed that the country had at least 31 murders in 2017 and 2018, triggered by fake news on social media. Last July 2019, the country witnessed one of the worst impacts of disinformation, killing five innocent men.
The incident has led to a furor in India. “Modern means of communication are for sharing information and knowledge, and ought to be used judiciously. It is indeed sad that five people lost their lives only because of rumors,” Devendra Fadnavis, then Maharashtra’s chief minister, said.
India is currently struggling to contain misinformation and disinformation epidemics on social media platforms such as Facebook, Twitter, and WhatsApp. PN Vasanti, director of the Center for Media Studies, a Delhi-based not-for-profit think tank, stated that fake news and photos pose additional challenges for fact-checkers due to the different formats of these contents. These impact the credibility and believability of fake news in different ways.
This is what Indian startup MetaFact wants to solve. MetaFact recently introduced its fact-checking tool, which uses natural language processing or NLP. This tool aims to help detect, monitor, and counter phony stories. Sagar Kaul, MetaFact’s founder, stated that this is meant to extend to newsrooms “the power to detect and monitor fake news in real-time, sifting through all the data cacophony that is generated online.”
According to Fast Company, the world's leading progressive business media brand, the fact-checking tool works by analyzing the context of sentences in news stories, blogs, and social media posts. It will then flag down misinformation and bias by identifying contentious sentences through their tone. The filtered content will be sent to debunk false and fabricated claims.
“Our main focus for training the tool is through making the tool understand the context of the sentence rather than the wording itself,” Kaul explained.