The paper "Fake News Detection on Social Media: A Data Mining Perspective" by Kai Shu, Amy Sliva, Suhang Wang, Jiliang Tang, and Huan Liu explores the challenges and methodologies for detecting fake news on social media. The authors highlight the dual nature of social media as a source of news, which offers both easy access and rapid dissemination but also facilitates the spread of misinformation. They emphasize the need for auxiliary information, such as user social engagements, to enhance detection accuracy. The paper reviews existing definitions of fake news, discusses psychological and social theories related to its impact, and presents a comprehensive framework for fake news detection, including feature extraction and model construction. The authors also review datasets and evaluation metrics used in the field and discuss open issues and future research directions. The paper aims to guide future research and improve the detection of fake news on social media.The paper "Fake News Detection on Social Media: A Data Mining Perspective" by Kai Shu, Amy Sliva, Suhang Wang, Jiliang Tang, and Huan Liu explores the challenges and methodologies for detecting fake news on social media. The authors highlight the dual nature of social media as a source of news, which offers both easy access and rapid dissemination but also facilitates the spread of misinformation. They emphasize the need for auxiliary information, such as user social engagements, to enhance detection accuracy. The paper reviews existing definitions of fake news, discusses psychological and social theories related to its impact, and presents a comprehensive framework for fake news detection, including feature extraction and model construction. The authors also review datasets and evaluation metrics used in the field and discuss open issues and future research directions. The paper aims to guide future research and improve the detection of fake news on social media.