# Detecting Disinformation in Croatian Social Media Comments
## 4 Model Evaluation on Croatian Social Media Dataset
### The Density of Vector Space  
It is to be expected that the comments that have offensive content will be positioned very closely in the vector space, and the same applies to the acceptable comments. Consequently, there will be space between these two groups. First, we decided to select the first 100 comments and determine their vector representation using TF-IDF. We opted for the vector space with the two axes.  
The output of vectorization resulted in the two-dimensional vector space. The axes were two words, one ordinary and the other offensive: \"imati\" (eng. \"to have\") and \"peder\" (eng. \"faggot\").  
It is apparent that there is a clear distinction between classes. All acceptable comments are at the origin, but there is also a group of the offensive comments around the origin. Figure 1 illustrates the distribution of the first 100 comments projected into a two-dimensional space for visualization.  
Due to the complexity of accurately representing high-dimensional data in only two dimensions, this plot primarily serves to demonstrate the general distinction between categories rather than providing a precise representation. Notably, comments positioned at the origin (0,0) are not meaningful and are excluded from the analysis. This plot is a simplified example to highlight the distinction between acceptable and offensive comments but cannot fully capture the complex relationships present in the original high-dimensional data space.  
To automate the process of determining the number of groupings, the Density-based Spatial Clustering of Applications with Noise (DBSCAN) algorithm was used, and the results are shown in Figure 2. The scikit-learn [48] implementation of the DBSCAN library was used. DBSCAN is an unsupervised classification algorithm that calculates the groupings by computing the distances between the points [53].  
The DBSCAN algorithm receives the minimal distance \(\epsilon\) and the minimum number of points (MinPts) as the input parameters. The parameter \(\epsilon\) defines the radius of the neighborhood around each data point, determining which points are considered neighbors. Points within this radius are classified in the same cluster as the anchor point. MinPts specifies the minimum number of points needed to form a dense region, or cluster. The selection of the DBSCAN clustering parameters, specifically the \(\epsilon\) and the MinPts, is crucial for the algorithm's effectiveness. In our study, we selected \(\epsilon\) and MinPts based on a combination of domain knowledge and empirical testing. We initially set MinPts to twice the number of dimensions in our dataset, following a common heuristic. Subsequently, we conducted a grid search to fine-tune \(\epsilon\), evaluating the clustering results using silhouette scores and visual inspection of the clusters. This approach ensured that the chosen parameters effectively  
Figure 1: The first 100 comments projected in 2D space.  
captured the underlying structure of the data, balancing the identification of meaningful clusters with the exclusion of noise points.