In addition, the new AI-based solution was also able to tell the difference between headlines that machines—or bots—generated and ones people wrote, they says.
In a study, the researchers asked people to write their own clickbait—an interesting, but misleading, news headline designed to attract readers to click on links to other online stories. The researchers also programmed machines to generate artificial clickbait. Then, researchers used the headlines from people and machines as data to train a clickbait-detection algorithm.
The resulting algorithm’s ability to predict clickbait headlines was about 14.5% better than other systems, according to the researchers, who released their findings at the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis.
Feeding the Algorithm
Beyond its use in clickbait detection, the team’s approach may help improve machine learning performance in general, says Dongwon Lee, the principal investigator of the project and an associate professor in the College of Information Sciences and Technology and an affiliate of Institute for CyberScience at Penn State.
“This result is quite interesting as we successfully demonstrated that machine-generated clickbait training data can be fed back into the training pipeline to train a wide variety of machine learning models to have improved performance,” says Lee.
“This is the step toward addressing the fundamental bottleneck of supervised machine learning that requires a large amount of high-quality training data.”
According to Thai Le, a doctoral student in the College of Information Sciences and Technology, one of the challenges confronting the development of clickbait detection is the lack of labeled data. Just like people need teachers and study guid ..
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