A Multi-Model Intelligent Approach for Rumor Detection in Social Networks

The impact of social media on public opinion has far-reaching repercussions across society. Even though social media give outlets to share news and opinions, the sheer number of posts on Twitter and Facebook makes it challenging to maintain quality control. These platforms have many users and offer various services, such as content creation and distribution. Not all information disseminated via the internet is accurate and reliable. Many people try to spread false and misleading information to influence public opinion. This paper reviews different algorithms for rumor identification, particularly fake news detection. It also proposes detection and classifications of fake news and its corresponding classification. Misinformation, commonly known as rumors, can cause serious harm due to unverified information. Despite their widespread use, the uncontrollable nature of social media platforms frequently results in the spread of rumors. One of the most sought after study areas in social media analytics is automatically recognizing rumors from tweets and posts.