## 2 Materials and Methods
### Feature Extraction
For example, magnitude classes were defined based on standard earthquake severity groupings used in seismology (e.g., magnitudes \(\geq\)5 indicating felt events and \(\geq\)7 indicating major quakes) [38,41]. Temporal and spatial proximity thresholds (e.g., \(\leq\)1 h, \(\leq\)100 km, etc.) were selected to reflect meaningful operational response windows and user behaviour patterns observed in disaster contexts, aligning with the approaches in other studies [42, 43, 44]. Figure 2 illustrates the complete feature design for our model dataset. We describe the feature design process in two main steps:  
Figure 2: Trajectory-based feature design for the prediction model. Step 1: Basic feature extraction from the EQ tweet dataset
* EQ_magnitude_Class: Categorised into three classes (5, 6, and 7), corresponding to EQs with magnitudes less than 6, 7, and 8, respectively. This feature is named EQ_magnitude_Class in the model dataset. * Temporal Features Class (TFC): Categorised into four classes (0, 1, 2, and 3), representing the time difference between EQ occurrence and the corresponding tweet as follows: \(\leq\)1 h, \(\leq\)1 day, \(\leq\)2 days, and \(\leq\)3 days, respectively. This feature is named TFC_EQ_tweet-EQ_Location in the model dataset. * Spatial Proximity Class (SPC): Categorised into six classes (0, 1, 2, 3, 4, and 5), representing the spatial distance between the EQ location and the location of the tweet as follows: \(\leq\)100 km, \(\leq\)200 km, \(\leq\)300 km, \(\leq\)400 km, \(\leq\)500 km, and \(>\)500 km, respectively. This feature is named SPC_EQ_Tweet-EQ_Location in the model dataset. Step 2: Extraction of additional pivot features from trajectory data
* Temporal Features Class (TFC): Categorised into five classes (0, 1, 2, 3, and 4), representing the time difference between the EQ occurrence and the trajectory tweet as follows: within 3 days after the event, 3 days before, within 1 month before, within 2 months before, within 3 months before, respectively. This feature is named TFC_trajPoint-EQ_Time and is pivoted along with four other features--SPC_trajPoint-EQ_Location, SPC_trajPoint-EQ_Tweet, MBC_trajStepLength, and MBC_velocity--in the model dataset. * Spatial Proximity Class (SPC): Two SPC features are extracted under this title. o SPC_trajPoint-EQ_Location is categorised into six classes (0, 1, 2, 3, 4, and 5), representing the spatial distance between the EQ location and the location of the trajectory tweet as follows: \(\leq\)100 km, \(\leq\)200 km, \(\leq\)300 km, \(\leq\)400 km, \(\leq\)500 km, and \(>\)500 km, respectively. o SPC_trajPoint-EQ_Tweet is categorised into six classes (0, 1, 2, 3, 4, and 5), representing the spatial distance between the EQ tweet location and the location of the trajectory tweet as follows: \(\leq\)100 km, \(\leq\)200 km, \(\leq\)300 km, \(\leq\)400 km, \(\leq\)500 km, and \(>\)500 km, respectively. * Movement Behaviour Class (MBC): Two MBC features are extracted under this title. o MBC_trajStepLength is categorised into six classes (0, 1, 2, 3, 4, and 5), representing the step length between two trajectory tweets as follows: \(\leq\)0.5 km, \(\leq\)50 km, \(\leq\)100 km, \(\leq\)500 km, and \(>\)500 km, respectively. o MBC_velocity is categorised into six classes (0, 1, 2, 3, 4, and 5), representing the velocity between two trajectory tweets as follows: \(\leq\)5 km/h, \(\leq\)10 km/h, \(\leq\)50 km/h, \(\leq\)50 km/h, \(\leq\)500 km/h, and \(>\)500 km/h, respectively.