Fake news is becoming a recognised problem in society, creating large volumes of misinformation and raising questions about media integrity. The spread of misinformation has serious consequences, affecting public opinion, undermining trust in institutions, and sometimes leading to real-world harm. The sheer volume of information produced daily making manual verification of news impractical and time-consuming, leading in the need for automated tools that can assist with the issue. In this work, we present a proof of concept of a tool designed to detect disinformation in English-language media sources. At the core of our solution is the RoBERTa model, fine-tuned on a diverse set of articles from American (mostly for non-disinformation) and Russian (mostly for disinformation) English-language sources. This approach allows us to save time by avoiding the need to train the model from scratch. Additionally, RoBERTa's advanced capabilities, such as its ability to grasp the context and meaning of complex sentences and its bidirectional nature (analysing sentences from both the beginning to the end and in the opposite way), enable it to capture long-range dependencies between words. These features are valuable in identifying complex linguistic structures, such as hyperbole, unverified claims, biased language, and sarcasm, commonly found in news sources. Besides, we are using this approach to collect and analyse texts from Lithuanian media sources, focusing on various domains such as politics, economy, society, and business, to identify the prevalence of disinformation within these outlets. This allows us to gain insights into how misinformation is distributed across different sectors. To enhance the usability of our solution, we developed a Dash library-based web application that displays the model's evaluation results and delivers an intuitive interface for users to interact with the system. This application allows users to upload news articles or plain text, analyse them for potential misinformation, and view detailed, real-time feedback on the model's predictions. Additionally, the system allows for batch processing of multiple texts at once, providing scalability for larger datasets. The result of our work is a developed and tested version of a deep learning based disinformation detection system, capable of analysing disinformation in the selected national and international news and media sources and presenting the analysis results with an informative dashboard. [ABSTRACT FROM AUTHOR]