Abstract:

The widespread dissemination of fake news could have serious negative consequences for individuals and society. First, fake news could upset the balance of authenticity in the news ecosystem. For example, the most popular fake news was more prevalent on Facebook or Instagram media. Second, fake news intentionally persuaded consumers to accept biased or false beliefs. Third, fake news was changing the way people interpret and react to real news. For example, some fake news was created to mistrust and confuse people, so it was impossible, to tell the truth from what was not. To mitigate the negative impact of fake news, it was very important to develop methods to automatically detect fake news in social networks, namely problem-based learning, in order to differentiate between real and fake visual content. Qualitative and quantitative approaches were performed using experimental and control groups to determine whether problem learning could induce students to engage more actively with the topic and develop critical thinking skills to avoid the implicit bias of fake news. This study was conducted at Universitas Katolik Santo Thomas Medan, Indonesia. This research showed that problem-based learning could promote the development of learning communities where learners could freely exchange ideas and ask questions related to the material being studied. Therefore, problem-based learning was an effective way to improve the analytical ability to distinguish between real news and fake news based on the credibility of the news.

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