## 3 Results and Discussion
Both outperform traditional models like DT, NB, and even SVM in multi-class classification tasks. Feature importance plots were generated for the DL, DT, and RF models, as these algorithms provide interpretable importance scores based on internal mechanisms. For DT and RF, feature importance is derived from the frequency and quality of splits involving each feature, while in DL models, importance is computed using methods such as variable permutation or network weights analysis. In contrast, models like NB, SVM, and k-NN do not inherently offer meaningful feature importance scores. NB assumes feature independence and relies on conditional probabilities, SVM focuses on support vectors without ranking features globally, and k-NN is instance based and lacks an internal model structure to evaluate feature contribution. Therefore, direct feature importance visualisation is not applicable for these models without additional model-agnostic techniques. The feature importance plot in Figure 4 illustrates the relative significance of the top 20 predictors across three different models--DL, RF, and DT--with normalised importance scores enabling direct comparison. Several features, such as Class_distanceToEvent (SPC_EQ_Tweet-EQ_Location) and Cl_td_sd_toEvent1_0 (Pivot: TFC_trajPoint-EQ_Time and SPC_trajPoint-EQ_Location: Class: 1 and Class: 0), consistently exhibit high importance across all three models, indicating their strong and stable predictive power regardless of the underlying algorithm. In contrast, features like Cl_td_movBx_x (Pivot: TFC_trajPoint-EQ_Time and MBC_trajStepLength: Class: x and Class: x) demonstrate model-specific relevance, generally being highly influential in the DL and RF models, respectively, while receiving low importance scores in DT. This suggests that different models may capture distinct aspects of the data structure. Moreover, features such as EQ_magnitude_Class and Cl_tD_velocity2_0 and 0_0 (Pivot: TFC_trajPoint-EQ_Time and MBC_velocity: Class: 2,0 and Class: 0,0) show moderate to high importance in specific models, potentially reflecting algorithmic sensitivity to particular variable patterns or interactions. Overall, the variability in feature importance highlights both the consensus among models on core predictive features and the unique perspectives each model brings to the learning task. This study demonstrates that trajectory-based and context-specific features, when combined with machine learning (ML) techniques applied to social media data (SMD), can effectively assess the relevance and event-related credibility of earthquake-related content. Six ML techniques were employed to assess the relevance of individual earthquake-related tweets, achieving an overall accuracy of 89%. In general, class prediction accuracy tends to increase from the most relevant class to the least. Confusion matrices reveal a high level of misclassification between Class 1 and Class 2, likely due to the inherent semantic overlap between them. Specifically, Class 1 includes content directly related to the earthquake, while Class 2 represents indirectly relevant information. From a trajectory  
Figure 4: Top 20 normalised feature importances across DT, RF, and DL. The bar lengths represent the relative contribution of each feature to the model’s predictive performance, scaled between 0 and 1 for comparability. Variations in importance across models highlight both commonly influential features and model-specific sensitivities. perspective, it is reasonable that individuals might mention an earthquake both directly and indirectly in nearby locations. This overlap introduces ambiguity and represents a limitation of the study. Nevertheless, the classification of data at this level of detail constitutes a valuable contribution.