# Romanian Fake News Detection Using Machine Learning and Transformer-Based Approaches
## 1 Introduction  
In today's world, rapid access to vast amounts of information has become essential, but it also brings challenges like the spread of fake news. Fake news consists of inaccurate or misleading information presented as facts, with serious societal consequences, such as influencing public opinion and undermining democratic processes. The rise of fake news, especially on social media, has sparked interest in finding effective methods for detection. Artificial intelligence (AI), particularly machine learning and deep learning, plays a crucial role by developing models that can analyze and classify text based on its truthfulness. In Romania, the phenomenon of fake news has grown significantly in recent years, impacting various sectors such as politics, health, economy, and culture. Therefore, there is a strong need to develop local solutions tailored to the specific linguistic and cultural context for the effective detection of fake news. Fake news refers to articles, images, videos, or other forms of media that contain false, misleading, or out-of-context information, created to deceive the public [1]. Fake news can be classified as satire/parody, which include false content created for humor not deception; disinformation, which is intentionally false content to manipulate opinion; propaganda, that refers to misleading information used to promote ideologies; clickbait, which refers to sensationalized headlines to attract clicks; distorted news, that is true information presented in a misleading way; fabricated content, that refers to completely false information with no basis in reality; and conspiracy theories, that represent unfounded claims of there being secret plots behind events [1]. Ref. [2] addresses ambiguities around \"fake news\" and re-frames it as \"false messages\" within a digital context. It critiques current terminology and uses the ontology of digital objects to define false messages by attributes like editability and interactivity. The study highlights overlooked research areas by placing these messages within broader networks of digital actors. Fake news significantly affects society, influencing politics, the economy, public health, and trust in institutions. Key consequences include the following:  
* Influencing public opinion and elections: Fake news can distort public perception, affecting election outcomes and political decisions through disinformation campaigns [3]. * Eroding trust in media and institutions: The spread of fake news reduces public trust in traditional media and government, complicating informed participation in democratic processes. * Creating panic during crises: In crises like pandemics, fake news exacerbates confusion by spreading false or alarming information, leading to dangerous public reactions. * Impact on public health: Health-related disinformation can lead to vaccine refusal, inappropriate treatments, and disease spread. * Affecting social cohesion: Fake news can divide communities, inciting radical ideas and interpersonal conflicts. * Economic impact: Disinformation can destabilize markets by spreading false information about companies or economic events, causing financial losses [3]. Understanding the profound impact of fake news on society highlights the importance of developing effective solutions for its detection and mitigation. In the following sections, we explore the methods and technologies employed to address this complex issue, with a particular focus on the use of artificial intelligence in fake news detection. This research aims to compare AI models used for detecting fake news, focusing on the following:  
* Data collection and preprocessing: Building a new dataset of fake and real news for the Romanian language and applying necessary preprocessing for model training. * Model training: Implementing and training several AI models to assess their ability to detect fake news. The classical models used in our research for fake news detection include Logistic Regression, Support Vector Machine (SVM), and Naive Bayes.