# Detecting Disinformation in Croatian Social Media Comments
## 2 Related Work
### NLP Research in Croatian Language  
The Croatian language is classified as an under-resourced language [31], with limited availability of annotated datasets, linguistic resources, and tools for natural language processing (NLP). Despite these challenges, recent advancements in NLP research have increasingly focused on tasks such as sentiment analysis [32; 33] and text processing [34; 35] in Croatian. These studies have leveraged domain-specific datasets and adapted existing methodologies to address the unique morphological and syntactic complexities of the language. Recently, notable research has also been carried out relating to the specific area of disinformation published in the Croatian language. One significant initiative is the Early Disinformation Detection (EDD) system developed by the European Digital Media Observatory [36], which uses NLP algorithms to detect potential disinformation in Croatian news and on social media platforms such as Facebook and Twitter/X. This system continuously scans relevant text data, classifies topics, and extracts named entries, all tailored to the Croatian language. [PERSON] et al. [37] investigated hate speech detection in Croatian online comments and emphasized the challenges posed by the highly inflectional nature of the language, particularly when combined with informal and non-standard user-generated content. Although the study did not focus specifically on disinformation, it highlighted the importance of effective text normalization strategies--including lemmatization--for improving detection performance. Their findings suggest that lemmatization can play a crucial role in reducing lexical sparsity and enhancing the model's ability to generalize across morphologically diverse expressions of harmful content. This insight is directly relevant to disinformation detection, where subtle variations in language can obscure misleading or manipulative narratives. Additionally, studies have explored various methodologies for detecting fake news in social media, emphasizing the effectiveness of larger models and the challenges posed by content generated by large language models (LLMs) [38]. While specific research papers focusing solely on Croatian language detection systems were not detailed in the provided contexts, the general trends in automatic detection methods can be applied to this area [39; 40]. [PERSON] and [PERSON] [41] highlight the organization's efforts to identify and debunk false narratives during the pandemic. The study highlights the challenges of fact-checking in the digital age, including the amplification of misinformation by Facebook's algorithms and the limited reach of fact-checking measures. The research underscores the need for systemic changes in platform governance to effectively combat the spread of disinformation. This study contributes to a broader understanding of the limitations of fact-checking as a tool to curb misinformation in online environments. It also investigates the effectiveness of fact-checking initiatives in combating COVID-19 misinformation on social media platforms in Croatia. The study uncovered several critical limitations of current fact-checking practices, including challenges in the distribution and communication process. The findings suggest that traditional fact-checking methods may need to be complemented by more innovative approaches to increase their impact and reach. The current landscape of automatic disinformation detection tools demonstrates a clear lack of language-specific models tailored for Croatian. However, recently, a novel LLM, named the croBERT, model was developed by [PERSON] et al. [42]. It is a transformer-based language model specifically developed for the Croatian language. The research behind croBERT focuses on addressing the unique challenges of processing and understanding Croatian text, which is considered a moderately resourced language.