# Detecting Disinformation in Croatian Social Media Comments
## 5 Conclusions and Future Work  
This paper provides an overview of the challenges that the spread of misinformation, especially in social networks, poses to the modern information society. As can be seen from their definitions, misinformation, fake news, and similar synonyms are often created and spread to make a profit for their creators. As this profit often contradicts the goals of a democratic society, science and state authorities have made great efforts in recent years to find mechanisms to prevent the influence of misinformation on society. This paper presents initial research and an early version of the novel framework for classifying disinformation found in content published on social networks (i.e., X) in the Croatian language. The model attempts to overcome problems with orthography and grammar (e.g., spelling and grammar errors that are common in social networks). The proposed framework for classifying disinformation in social networks published in the Croatian language addresses several critical challenges in automatic disinformation detection. Our approach addresses the widespread problem of non-standard language usage and grammatical deviations that are common in social media communication. Figure 11: Training and evaluation loss with learning rate over epochs. Figure 10: AUC-ROC curve for CroBERT model. The framework shows promising results in detecting disinformation despite these linguistic challenges and offers several important insights. The framework's ability to deal with common misspellings and grammatical irregularities represents a significant advance over traditional text classification methods. This adaptability is crucial considering that disinformation is often spread via informal, user-generated content where standard language rules are often disregarded. Our model accounts for character constraints and platform-specific conventions (e.g., hashtags, mentions, and shortened URLs) that influence how disinformation is structured and disseminated. The model shows that it can capture contextual differences despite linguistic irregularities, although there is still room for improvement in understanding more complex forms of deception such as sarcasm and implicit messages. This study presents a comprehensive approach to disinformation detection by exploring both traditional machine learning techniques--Support Vector Machines (SVMs), Random Forests (RFs), and neural networks (NNs)--and transformer-based models, specifically croBERT. The inclusion of both methods serves a dual purpose: first, to establish a performance baseline using well-understood, computationally efficient models, and second, to assess the advantages offered by modern transformer architectures in handling complex linguistic patterns. While traditional ML models provide interpretability and faster training times, the croBERT model demonstrated superior performance, achieving an F1 score of 0.893, underscoring the effectiveness of pre-trained language models in capturing the semantics of disinformation. This comparison not only highlights the evolution of disinformation detection techniques but also emphasizes the trade-offs between computational complexity and model accuracy, offering a balanced perspective for future research. Several promising directions for future research emerge from this initial framework. Future research will focus on developing more sophisticated methods for dealing with emerging forms of multimedia disinformation, developing more robust validation methods for evaluating the performance of the framework, establishing standardized metrics for evaluating disinformation detection systems, and investigating the role of network effects in the spread of disinformation and how to incorporate them into the classification framework. To address the limitation of the dataset used for training the model, future work will focus on constructing an extended dataset that encompasses a wider range of disinformation topics, beyond migrants and LGBTQ+ issues.