Machine Learning Approach for WhatsApp Application Threat Detection
Social media like WhatsApp and Facebook for news consumption, is a double edged sword. On the one hand, its low cost, easy access, and rapid dissemination of information lead people to search and consume social media news. On the other hand, it allows the wide dissemination of \fake news, i.e. low quality news with intentionally false information. The widespread dissemination of false news has the potential for extremely negative impacts on individuals and society. Therefore, the detection of false news in Social media has recently become an emerging research that is attracting tremendous attention. False news of Detection in social media presents unique features and challenges that make existing detection algorithms of traditional Media ineffective or not applicable. First, 's false news is intentionally written to deceive readers into believing false information, which makes it dissect and not trivial to detect based on news content; therefore, we need to include information Auxiliary, as the social commitments of users in social media, to help make a determination. Second, exploiting this auxiliary information is challenging in and of itself as the social commitments of the users with false news produce data which are large, incomplete, unstructured and noisy. Because the issue of detecting false news in social media is both challenging and relevant, we have conducted this survey to continue facilitate research on the problem. In this survey, we present a comprehensive review of detecting fake news on social media, including fake news characterizations on psychology and social theories, existing algorithms from a data mining perspective, evaluation metrics and representative datasets. We also discuss related research areas, open problems, and future research directions for fake news detection on social media. KEYWORDSFake News, WhatsApp, User Profile, Trust Analysis, machine learning, Social Media.