AI News, Using machine learning for cross-lingual and cross-platform rumor verification

Using machine learning for cross-lingual and cross-platform rumor verification

Fake rumors can spread very quickly online, causing havoc and confusion among readers, so the development of tools to verify the authenticity of online information is of pressing importance.

'Our research is inspired by the increasing popularity of fake news attached by multimedia content in social networks,' Weiming Wen, one of the graduate researchers who carried out the study, told Tech Xplore.

The basic idea is to solve the problem through machine learning—extracting specific features from this type of rumor and building a model to classify rumors as fake or real.'

Past rumor verification research used multimedia content as input features, leveraging forensic features of images or videos to determine whether they have been tampered with.

possible reason for this is that often, multimedia content attached to fake news is merely borrowed from authentic events and is somewhat semantically aligned with the text that accompanies it.

'When we say multimedia rumors, we mean tweets or other social media content that are not verified and have images or videos along with the text,' Zhou Yu, assistant professor at UC Davis, who carried out the study, told Tech Xplore.

'I think the most meaningful part of our study is that we developed a rumor verification framework that works specifically for multimedia rumors, which is extremely common, but has not been studied thoroughly,' Wen said.

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