Fighting Fake News Using Machine Learning & Blockchains

Uploaded on 2023-08-10 in FREE TO VIEW, TECHNOLOGY-Key Areas-Artificial Intelligence, TECHNOLOGY-Key Areas-Blockchain

The proliferation of misinformation and fake news in the digital age has become a critical societal concern. The rapid spread of false or misleading information can have far-reaching consequences, including social unrest, political polarisation, and public health crises.

This misleading content like fake news and false media is spreading across social media platforms and has become a threat to society. It certainly has negative effects people and it is being misused in political propaganda, cyber crimes and other areas.

And for a decade or more electronic and social media false and fake news has become a global problem, but now with the rise of widely available AI technology this is a greater danger than ever.

Fake truths can lead to actual harmful consequences and so social media, and government organisations are using new strategies for dealing with the phenomenon.

This includes more fact-checking and flagging misleading information giving more important context to what the audiences needs, but this is still missing a lot of fake information publishing.

Now research from Binghamton University’s School of Management (SOM,) proposes a machine learning framework with expanded use of blockchain technology to combat this massive problem.

This research is led by Thi Tran the assistant professor of management information systems, who led the research and who has explained the thought behind it:
“We’re most likely to care about fake news if it causes harm that impacts readers or audiences. If people perceive there’s no harm, they’re more likely to share the misinformation… 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.”

According to Techxplore, Tran’s research proposed machine learning systems that will help determine how much harm content will cause to its audience and focus on the worst offenders.

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 victimised by the misinformation.

The system would also consider user characteristics of people with prior experience or knowledge about fake news to help build a “harm index”, which would reflect the severity of possible harm to a person in certain contexts if they were exposed and victimised by the fake news.

Tran further explains that based on the information gathered, the machine learning system could help fake news mitigators differentiate which messages are likely to be most damaging if allowed to spread unchallenged.

“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.

He has also suggested that there should be a survey of around a 1,000 people, both fake news mitigators and content consumers, lay out three existing blockchain systems and see the participants’ willingness to use those systems in different scenarios.

“We are more likely to be interested in fake news if it causes harm to readers or the public. If people perceive that there is no harm, they are more likely to share misinformation,” said Thi Tran.

“Harms come from whether audiences act on the disinformation claims or refuse appropriate 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.”

“I hope this research helps us educate more people about being aware of the patterns, 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 concluded.

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.

iHLS: Science Daily: IEEE Xplore: Unbiased: Tech Explorist: Compsmag: News8Plus

___________________________________________________________________________________________

If you like this website and use the comprehensive 6,500-plus service supplier Directory, you can get unrestricted access, including the exclusive in-depth Directors Report series, by signing up for a Premium Subscription.