Machine learning, blockchain technology could help counter spread of fake news

The research led by Thi Tran, assistant professor of management information systems at Binghamton University's School of Management, expands on existing studies by offering tools for recognizing patterns in misinformation and helping content creators zero in the worst offenders.

"I hope this research helps us educate more people about being aware of the patterns," Tran said, "so they know when to verify something before sharing it and are more alert to mismatches between the headline and the content itself, which would keep the misinformation from spreading unintentionally."

Tran's research proposed machine learning systems -- a branch of artificial intelligence (AI) and computer science that uses data and algorithms to imitate the way humans learn while gradually improving its accuracy -- to help determine the scale to which content could cause the most harm to its audience.

Examples could include stories that circulated during the height of the COVID-19 pandemic touting false alternate treatments to the vaccine.

The framework would use data and algorithms to spot indicators of misinformation and use those examples to inform and improve the detection process. It would also consider user characteristics from people with prior experience or knowledge about fake news to help piece together a harm index. The index would reflect the severity of possible harm to a person in certain contexts if they were exposed and victimized by the misinformation.

"We're most likely to care about fake news if it causes a harm that impacts readers or audiences. If people perceive there's no harm, they're more likely to share the misinformation," Tran said. "The harms come from whether audiences act according to claims from the misinformation, or if they refuse the proper action because of it. If we have a systematic way of identifying where misinformation will do the most harm, that will help us know where to focus on mitigation."

Based on the information gathered, Tran said, the machine learning system could help fake news mitigators discern which messages are likely to be the most damaging if allowed to spread unchallenged.

"Your educational level or political beliefs, among other things, can play a role in whether you are likely to trust one misinformation message or not and those factors can be learned by the machine learning system," Tran said. "For example, the system can suggest, according to the features of a message and your personality and background and so on, that it's 70% likely that you'll become a victim to that specific misinformation message."

While other studies have been conducted about using blockchain -- a type of shared database technology -- as a tool to fight fake news, Tran's research also expands on previous findings by exploring user acceptability of such systems more closely.

Tran proposed surveying 1,000 people from among two groups: fake news mitigators (government organizations, news outlets and social network administrators) and content users who could be exposed to fake news messages. The survey would lay out three existing blockchain systems and gauge the participants' willingness to use those systems in different scenarios.

Traceability is one of the nice features of blockchain, Tran said, because it can identify and classify sources of misinformation to help with recognizing the patterns.

"The research model I've built out allows us to test different theories and then prove which is the best way for us to convince people to use something from blockchain to combat misinformation," Tran said.

Tran recently presented his research at a conference hosted by SPIE, the international non-profit dedicated to advancing light-based research and technologies. One paper focused on the machine learning-based framework and another paper dealt with the use of blockchain.