# Detecting Disinformation in Croatian Social Media Comments
## 1 Introduction  
Social networks and online news platforms have become integral parts of modern digital ecosystems and serve as primary channels for the dissemination of information. The Internet itself, and one of its first popular services--the World Wide Web [1]--were originally conceived as platforms for the exchange of information, but the widespread creation and publication of user-generated content became prominent only later, with the advent of social media and Web 2.0 technologies. In the wealth of data available to us every day in the online world, so-called fake news has emerged in recent years. Fake news or misinformation is defined as intentionally false posts that are presented as true [2]. In this paper, the terms \"misinformation\" and \"disinformation\" are used synonymously for false or misleading information, regardless of intent, as the focus is on recognizing and classifying such content and not on the motivation for its dissemination. The result of successfully marketed misinformation is a change in the belief or behavior of the consumer of that misinformation [3]. Research has shown that the average consumer is more inclined to believe dubious facts that are generally consistent with their beliefs (political, religious, etc.) than those that run counter to their beliefs, regardless of the strength of the arguments used to support them [4]. Social networks have proven to be an ideal place for the unhindered spread of misinformation. A very large number of social network users and easy access to and sharing of content, which are at the core of the architecture of any social network, provide the ideal setting for a phenomenon that has proven to be one of the greatest threats to democracyand freedom of expression. The issue becomes especially critical during sensitive periods, such as political elections, public health crises, or other events that amplify the impact of false information. The emergence of disinformation during the 2016 US presidential election and disinformation surrounding the 2017 referendum on the United Kingdom of Great Britain and Northern Ireland's exit from the European Union have led to a decline in public trust in the governance structures of these countries. As an example, we can use the analysis of posts on the social network Facebook during the 2016 presidential election campaign in the US [5]. In the last three months of the campaign, the 20 most widely shared fake campaign posts were shared more than 8,700,000 times. At the same time, the 20 most widely shared news items from verified sources (19 leading national news portals) were shared around 7,350,000 times by users of this social network. The fake news content that was most successful with users (so that they shared it with their friends on the social network) consisted of announcements that [PERSON] was supporting a candidate in the elections and that another candidate was involved in the sale of American weapons to ISIS. Furthermore, research shows that the publication of such misinformation may have even influenced the outcomes of these processes [6; 7], clearly pointing to the need to recognize, flag, and remove such misinformation from the public sphere. Despite the increasing presence of the Croatian language on digital platforms, the Croatian language remains underrepresented in natural language processing (NLP) research, leaving a gap in tools and frameworks tailored to the linguistic and cultural characteristics of the Croatian language. This study takes an initial step toward exploring the intersection of disinformation and hate speech in the Croatian language by proposing a novel framework for detecting potentially misinformative content in social media comments. It addresses the existing research gap and provides a foundation for future studies to expand the scope with additional datasets and broader thematic contexts. The framework focuses on real public comments from Croatian Facebook pages and uses text analysis techniques to classify and identify disinformation.