New York: As misinformation campaigns on social media continue to pose a challenge to governments and regulators across the world, Rice University researchers have discovered a more efficient way for social media companies to keep misinformation from spreading online using probabilistic filters trained with artificial intelligence.
The new approach to scanning social media was presented by Rice computer scientist Anshumali Shrivastava and statistics graduate student Zhenwei Dai at an online conference this week.
Their method applies machine learning in a smarter way to improve the performance of Bloom filters, a widely used technique to identify false information.
Using test databases of fake news stories and computer viruses, Shrivastava and Dai showed their Adaptive Learned Bloom Filter (Ada-BF) required 50% less memory to achieve the same level of performance as learned Bloom filters.
To explain their filtering approach, Shrivastava and Dai cited some data from Twitter. The social media giant recently revealed that its users added about 500 million tweets a day, and tweets typically appeared online one second after a user hit send.
“Around the time of the election they were getting about 10,000 tweets a second, and with a one-second latency that’s about six tweets per millisecond,” Shrivastava said.
“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.”
If flagged tweets are sent for an additional, manual review, it’s also vitally important to have a low false-positive rate. In other words, you need to minimize how many genuine tweets are flagged by mistake.
“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,” he said. “This is precisely why most of the traditional AI-only approaches are prohibitive for controlling the misinformation.”
Shrivastava said Twitter doesn’t disclose its methods for filtering tweets, but they are believed to employ a Bloom filter, a low-memory technique invented in 1970 for checking to see if a specific data element, like a piece of computer code, is part of a known set of elements, like a database of known computer viruses. A Bloom filter is guaranteed to find all code that matches the database, but it records some false positives too.
“Let’s say you’ve identified a piece of misinformation, and you want make sure it is not spread in tweets.
“A Bloom filter allows to you check tweets very quickly, in a millionth of a second or less. If it says a tweet is clean, that it does not match anything in your database of misinformation, that’s 100% guaranteed. So, there is no chance of okaying a tweet with known misinformation. But the Bloom filter will flag harmless tweets a fraction of the time,” Shrivastava said.
Within the past three years, researchers have offered various schemes for using machine learning to augment Bloom filters and improve their efficiency.
Language recognition software can be trained to recognize and approve most tweets, reducing the volume that need to be processed with the Bloom filter. Use of machine learning classifiers can lower how much computational overhead is needed to filter data, allowing companies to process more information in less time with the same resources.
“When people use machine learning models today, they waste a lot of useful information that’s coming from the machine learning model,” Dai said.
The typical approach is to set a tolerance threshold and send everything that falls below that threshold to the Bloom filter. If the confidence threshold is 85%, that means information that the classifier deems safe with an 80% confidence level is receiving the same level of scrutiny as information it is only 10% sure about.
“Even though we cannot completely rely on the machine-learning classifier, it is still giving us valuable information that can reduce the amount of Bloom filter resources,” Dai said.
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