# Detecting Disinformation in Croatian Social Media Comments
## 3 Framework Design and Methodology  
The problem of the use of social media to spread disinformation has been recognized by many governments, including the European Commission, which declared disinformation to be one of the most threatening security challenges in 2015. Based on a broad public debate and expert recommendations, the Commission has published a document entitled \"Tackling online disinformation: A European approach\" [43], which provides guidance and a political commitment to the long-term suppression of disinformation. The document highlights that the spread of misinformation is also taking place in the context of major changes in the media, which are undergoing a deep transformation to adapt their business models. The major social media outlets (including Facebook and Twitter/X) have signed the document and have begun self-regulatory efforts to combat disinformation. Based on the principles of the European approach, various tools need to be implemented to make the fight against disinformation more efficient. In this paper, we propose an algorithm for detecting potentially harmful content (essentially disinformation) posted by social media users in comments on social media. The novelty of this research lies in its focus on analyzing and detecting disinformation within user-generated comments written in the Croatian language, addressing a significant gap in existing natural language processing studies for under-resourced languages. Croatian, a South Slavic language spoken primarily in Croatia, is known for its rich linguistic heritage and complex grammatical structure [44]. One of the most daunting aspects of Croatian grammar is its case system. Croatian uses seven grammatical cases: nominative, genitive, dative, accusative, vocative, locative, and instrumental. Each case serves a specific syntactic function and alters the endings of nouns, pronouns, and adjectives. Croatian verbs are conjugated based on person, number, tense, mood, and aspect. Due to these intricate features, developing an automated tool for disinformation detection proves to be a worthy research challenge. To the best of our knowledge, there are currently no available tools that can classify disinformation published in the Croatian language. While the research of [PERSON] and [PERSON] [41] identifies key local actors and disinformation narratives, offering valuable insights into the human-driven dissemination of false information, it does not propose a solution that can be used to utilize machine learning techniques to analyze text data, detect patterns, and flag potential disinformation with high accuracy, as our proposed model does. The selected methodology balances practical limitations with the necessity for a detailed examination of disinformation dynamics, ensuring that the research remains relevant and applicable to the specific issues at hand. In this study, we started by using the traditional ML techniques but have also started to explore the use of transformer-based models, such as BERT, due to several key considerations. Although transformer-based models have demonstrated superior performance in various natural language processing (NLP) tasks, they do require substantial computational resources and extensive datasets for training, which can be a limiting factor, especially in domain-specific studies or languages with scarce labeled data. In contexts where data may be limited or where computational efficiency is paramount, traditional machine learning models can provide a more accessible and practical solution [45]. Furthermore, traditional models offer greater interpretability and practical applicability, particularly in detecting disinformation in Croatian social media, where traditional approaches have proven effective and are easier to analyze. Given the dataset size and the scope of our research, employing transformers would not have been computationally efficient or practical.