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Indian Techies Work to Detect Fake WhatsApp and Facebook Messages

Mozilla L10N Hackathon in Punjab, India. Photo by Subhashish Panigrahi via Wikimedia (CC BY-SA 4.0)

Two Indian coders are building a website that helps detect fake messages shared widely on WhatsApp and Facebook. Known as check4spam.com, the site relies on both the research and investigation by the check4spam team along with volunteer users.

The group hopes to expand the portal's capabilities to provide certain services through technical tools. They describe the project as follows:

We verify any known posts with the below actions:

1. Contact the person/organization mentioned in the post
2. Do an extensive search (online and offline) to find any further fine information about the news

- How We Work, check4spam.com

India is seeing a rapid increase in Internet use, even among the elderly. Many of the new users do not yet know how to differentiate between authentic sources and fake or malicious ones. And there are threats of click bait, hoaxes or Trojan horse-style software built to steal information from the user’s device.

Bal Krishn Birla and Shammas Oliyath who created the website are two seasoned techies based in the Indian city of Bengaluru. With a vision of “unconditional Service for humanity” and a mission to “make life easy for the common man and life trouble for the spammers,” they have embarked on to educate people in India that fall victim to fake messages on social media, and help circulate those messages.

A typical certification. Image via Check4Spam Website.

They have set up a WhatsApp number in August 2016 for people to send in the messages for fact-checks. According to Shammas, they get as many as 100 messages a day for verification. Shammas reads the messages during his hour-long lunch break and starts researching the leads.

Check4Spam is a self-funded project. It gets some revenue from ads on the site which goes into its operation costs, including promotional posts on Facebook.

The check4spam.com currently supports messages that are text-only, image-only, and contains both text and image. They are also crowdsourcing spam message detection by asking people to report the spam messages that the users find out themselves. Currently the detected messages are categorized under internet rumours, accidents, jobs, medical, missing, government initiatives, and promotions.

The site gets half a million page views a month.

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