Researchers Use Machine-Learning Method to Improve Bloom Filter for Fake News Detection on Social Media

With so much misinformation spreading in social media, Rice University researchers led by computer scientist Anshumali Shrivastava developed a method using machine learning (ML) to prevent the spread of misinformation online.

This new method developed by Shrivastava and his team is presented during the 2020 Conference on Neural Information Processing Systems (NeurIPS 2020), which was held online. They improved the 50-year old Bloom filter technology for scanning social media help social media network companies prevent the spread of fake news in their platforms.

Shrivastava and Dai explained their filtering approach using some Twitter data. According to Twitter, about 500 million more tweets are sent per day, which are typically published just one second after the users pressed send. However, during election Twitter was receiving about 10,000 tweets per second, which would equivalent to about six tweets per millisecond, considering the latency of one second.

"If you want to apply a filter that reads every tweet and flags the ones with information that's known to be fake, your flagging mechanism cannot be slower than six milliseconds or you will fall behind and never catch up," Shrivastava told Free Press Journal.

Researchers noted that it is also important to have a low false-positive rate when flagged tweets are sent for another manual review and generally minimize genuine tweets that are mistakenly flagged.

"If your false-positive rate is as low as 0.1%, even then you are mistakenly flagging 10 tweets per second, or more than 800,000 per day, for manual review," said Shrivastava adding that this is the reason most "AI-only approaches are prohibitive" for regulating fake news.

Although Twitter did not reveal how it filters tweets, researchers believe the social media giant uses Bloom filter, which was developed in 1970. A Bloom filter could find all codes that match the database, but it also generate some false positives results.

Shrivastava noted that researchers have been proposing various methods using machine learning to improve Bloom filters' efficiency since 2017.

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Written by CJ Robles