Best Free Disinformation Detection Tools of 2025

Maltego can be used by many users, including security professionals, forensic investigators and investigative journalists as well as researchers. You can easily gather information from disparate data sources. All information can be automatically linked and combined into one graph. Automately combine disparate data sources using point-and-click logic. Our intuitive graphical user interface allows you to enrich your data. You can detect patterns even in the largest graphs using entity weights. You can annotate your graph and then export it for further use. Maltego defaults to using our public Transform server. We have learned over the years that flexibility is important in choosing the right infrastructure for enterprise users.

Fakespot serves as a safeguard against online scams during your shopping experiences. Embrace the secure shopping movement and uncover the reality behind products, reviews, and sellers prior to making any purchases. Whether you are shopping from your couch or on your phone, Fakespot is there to support you. With its innovative browser application, you can navigate the internet, and whenever you visit a supported site, Fakespot’s features will activate to assist you in making well-informed buying choices. The functionality that allows sharing to analyze remains available for those transitioning from our earlier app. Since its launch, Fakespot has meticulously reviewed over 9 billion product and seller evaluations. This comprehensive analysis empowers you to choose wisely so that you genuinely enjoy each item you buy and avoid returns. Furthermore, Fakespot continuously enhances its algorithms to stay ahead of new threats, ensuring your shopping experience is safe and secure. In the end, Fakespot not only simplifies your shopping but also conserves your time and money while eliminating stress. You can shop with confidence, knowing that Fakespot is dedicated to protecting your interests.

The rise of online disinformation and counterfeit media has become a significant danger to democracy, the economy, and society as a whole. Verifying content on a large scale and in near real-time presents considerable challenges, even for seasoned journalists, fact-checkers, human rights advocates, and scholars specializing in media literacy. To address this issue, the WeVerify initiative seeks to create advanced methods and tools for intelligent human-in-the-loop content verification and disinformation analysis. By analyzing social media and web content within the context of the broader online landscape, the project aims to unveil false information through techniques such as cross-modal content verification, social network analysis, targeted debunking strategies, and a blockchain-based public repository for documenting known fabrications. This multifaceted approach not only enhances the accuracy of information but also empowers individuals and organizations to combat misinformation effectively.

ActiveFence is a leading Trust and Safety provider of online platforms. It protects over three billion users daily from malicious content and behavior. ActiveFence is trusted by Trust and Safety teams across the board to protect their users from online harms such as child abuse, disinformation and hate speech, fraud and terror. Our platform is equipped with a full range of capabilities, including AI-driven harmful content detection. ActiveFence protects online platforms in over 100 different languages. This allows people to interact and thrive online safely.

Bot Sentinel was created with user-friendliness in mind while aiming to provide extensive information. To enhance user understanding, we openly showcase comprehensive data regarding the Twitter accounts our platform monitors, illustrating how malicious accounts disseminate misinformation and target others. Our commitment to transparency ensures that users receive ample data insights. As an impartial platform, we monitor all accounts without bias. Utilizing advanced machine learning and artificial intelligence, we categorize Twitter accounts and compile them into an openly accessible database for public viewing. Our model was developed by analyzing thousands of accounts and millions of tweets, achieving a remarkable accuracy rate of 95% in classification. Unlike many other machine learning tools that focus solely on identifying “bots,” our approach is centered on recognizing specific behaviors and activities that violate Twitter's guidelines, allowing us to address the issue more effectively. By prioritizing these behaviors, we aim to contribute to a healthier online discourse.