Abstract
Facing the issues of fake news, numerous researchers have been trying to devote efforts with different approaches, ranging from technical to social or behavioral views. This paper proposes a machine learning based framework that considers characteristics or features of various stakeholders or components of the fake news contexts in all technical, social and behavioral views, ranging from the fake news messages, users, contexts, fake news creators or senders, and fake news mitigators. With the end products as the classified real or fake news and the suggested action plans based on all of those features, the system is promising to be flexible in adapting to contextual changes through the time, which is the struggles of most solely technical systems. Such a framework not only contribute to the literature but also provide decision support tools to fake news mitigators, which can help predict, prevent, eliminate or minimize impacts from fake news issues.
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