## 2 Materials and Methods
### Data Cleaning and Tidying  
The retrieved earthquake tweet data are biased due to bot accounts that systematically report every earthquake worldwide, as well as tweets from newsfeed accounts. To mitigate this, we applied text-based filtering to tweets and accounts to exclude tweets from bot accounts in the dataset. We designed the data extraction process in three steps:  
1. A word list of technical terms (e.g., \"mww\", \"mwr\", \"mw\", and \"ml\") was applied to tweet content to identify tweets from sensor-based bot accounts.
2. A word list of earthquake-related terms (e.g., \"quake\", \"afad\", \"dask\", \"deprem\", and \"kandilli_info\") was applied to account names to filter out tweets from earthquake institutions in Turkiye and worldwide.
3. A word list of news-related terms (e.g., \"news\", \"haber\", and \"trendinalia\") was applied to account names to exclude tweets from news-feeding bot accounts.  
Following the data extraction process, we summarised the tweet count by earthquakes and listed it in Table 1. The total tweet count for the 28 earthquakes is 4670, from 3467 data producers. While we test the hypothesis that a higher magnitude may result in a higher tweet count, the correlation test returns low significance for this hypothesis.  
The allocation of the number of data producers in terms of the number of distinct earthquakes they tweet about and the number of tweets is summarised in Table 2. According to this summary, a great number of data producers (1011) tweeted one time related to a single earthquake. On the other hand, 1318 data producers tweeted only related to a single earthquake, while 138 and 607 data producers tweeted related to two and more than two earthquakes, respectively. The maximum number of tweets by a data producer and the maximum number of related distinct earthquakes are 18 and 8, respectively. The second largest group of data producers after the single-tweet accounts tweeted more than one and less than five times. Only five data producers tweeted 10 times or more. The maximum number of earthquakes a single data producer tweeted about, including repeated tweets due to overlapping time intervals of multiple earthquakes, is 8 out of the 28 earthquakes listed in Table 1.  
Figure 1: Earthquake tweet data retrieval flow.  
The second data collection stage of the study is to create a trajectory-based dataset by retrieving the previous tweets of each of the 2063 users. Before collecting the dataset, we first disambiguated tweets that referred to more than one earthquake event. We removed tweets based on their relative distance to the earthquake locations; specifically, we excluded those located farther from the disaster event and retained only the tweet that was closest to the corresponding event. After this process, we retained 3237 earthquake-related tweets.  
To obtain trajectory data, we queried each user's tweets from up to three months prior to the timestamp of each earthquake-related tweet. These collected data were transformed into trajectory records for each earthquake event tweet, treating the earthquake tweet as the endpoint. In total, we compiled 3237 trajectories, each containing a varying number of trajectory points, as shown in Table 3. While nearly 10% of the trajectories consist of only the endpoint (i.e., the earthquake-related tweet itself), approximately 25% include between 77 and 1438 trajectory points.  
In the subsequent steps, aimed at determining earthquake relevancy based on user trajectories, the trajectory data were organised and feature engineered, as outlined in Section 2.4.