Social media and Web sources have made information available, accessible, and shareable any time and anywhere nearly without friction. This information can be truthful, falsified, or can only be the opinion of the writer as users in such platforms are both information creators and consumers. In any case, it has the power to affect the decision of an individual, the beliefs of the society, activities, and the economy of the whole country. Thus, it is imperative to identify false information and mitigate the effects of false information that are ubiquitous across the Web and social media. Therefore, the main goal of this dissertation is to proactively combat false information by defining three objectives. First, analyze the reason behind the success of its motive, second, recognize and quantify the impacts made on information systems, and third, develop novel ways of identifying false information and the actors responsible for creating and spreading them. The achievement of these three objectives enhanced our understanding of false information and helped in building strategies to mitigate this phenomenon. Overall, this dissertation presents our research on in-depth analysis of malicious entities, their impact in the information ecosystem, and the models we build to accurately detect different malicious entities like fraudulent reviewers, fake news, fake news spreaders in real-world scenarios. We show that each of our methods outperforms the existing state-of-the-art methods in the detection of false information and malicious actors in real-world opinion-based systems and social media.