## 2 Materials and Methods
### Feature Extraction  
To assess the relevance of each retrieved tweet (2.1), cleaned and processed tweet (2.2), and labelled tweet (2.3), we designed and extracted 126 features based on basic characteristics of earthquake-related (EQ) tweets and trajectory data. Previous tweets from each user were retrieved from the database, covering a period of up to 3 months before and 72 h after the event. Using this dataset, each earthquake-related tweet was incorporated into a trajectory. We leveraged trajectory data to extract features that help determine the relevance of tweets to the earthquake. Mainly, two calculations--spatial proximity and temporal distance--were performed for feature extraction. In the preprocessing of the trajectory data, the spatial proximity was calculated to measure the distance between the tweet location and other relevant locations (e.g., the earthquake event location or another tweet location) using the Haversine formula. Specifically, we computed the great-circle distance between each user's geotagged location (lon.x, lat.x) and the reference location (lon.y, lat.y). The distHaversine () function from the geosphere package [40] was used for this purpose, and the results were converted from metres to kilometres (1). In the preprocessing of the trajectory data, the temporal distance was calculated to measure the time difference between each user's tweet and another relevant timestamp (e.g., the earthquake event time or another tweet time). Specifically, we first ensured that the tweet timestamps (timestamp.x) were standardised to the UTC time zone using the force_tz () function. Then, we computed the time difference between each tweet and the reference timestamp (timestamp.y) using the difftime () function in R. The result, stored as the temporal distance, represents the time interval in hours (2). _Pseudocode for Calculating Spatial Proximity to Earthquake Epicentre_  
_for each tweet in dataset:_  
\[\text{distance\_meters}=\text{distHaversine(lon.x, lat.x, lon.eq, lat.eq)} \tag{1}\]  
\[\text{distance\_km}=\text{distance\_meters}/1000\]  
_append distance_km to dataset_ \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Relevancy** & **Explanation** & **Examples (Translated from Turkish)** & **Tweet Count** & **Distinct Tweet Count** \\ \hline \multirow{4}{*}{1} & Directly relevant: tweets directly from the field based on the experiences of tweet producers & Felt the earthquake. & \multirow{4}{*}{170} & \multirow{4}{*}{124} \\  & Directly relevant: tweets directly from & I am ok but we are not \#earthquake & & \\  & Directly relevant: tweets highly from & Oh my God, don’t let those horror moments & & \\  & Directly relevant: tweets highly from & We wish [PERSON]’s mercy on those who lost their & & \\  & Directly relevant: tweets highly from & It is in the earthquake that took place in Izmir, & & \\  & & recovery or & and a speedy recovery to the injured. & \\  & spreading general & May [PERSON] help those who are under the rubble. & & \\  & information & Images of the building shaken like a cradle by & & \\  & about earthquakes & the 5.8 magnitude earthquake. & & \\ \hline \multirow{4}{*}{3} & \multirow{4}{*}{Irrelevant: mixed contents not directly meaning something} & Earthquake scientist chicken feed & & \\  & & & & \\  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The frame of three categories of relevancy labels. _Pseudocode for Calculating Temporal Distance to Earthquake Event_  
_for each tweet in dataset:_  
_convert timestamp.x and timestamp.y to POSIXct format_  
_assign UTC timezone to timestamp.x and timestamp.y if not already set_  
_time_difference_hours = difftime(timestamp.x, timestamp.y, units = \"hours\")_  
_append time_difference_hours to dataset_  
The classification intervals were designed with two main considerations in mind: (1) the empirical characteristics of the dataset and (2) common practices in prior disaster-related spatiotemporal analyses.