# GeoLocator: A Location-Integrated Large Multimodal Model (LMM) for Inferring Geo-Privacy
## 4 Experimental Design to Test GeoLocator
Step 4: Examine the impact of languages based on inference results  
Through further experiments, it was found that GeoLocator's performance varies with different input languages. Therefore, this experiment was designed to evaluate the impact of different languages on the model's inference results. During the experiment, images containing text prompts in different languages were provided to GeoLocator, and its predictions for the geographic location of the images were observed. Finally, we checked whether the model's predictions varied with the change of input language. Step 5: Evaluate the performance of GeoLocator based on social media posts  
The final experiment was focused on a more realistic and complex scenario: social media posts. Unlike structured datasets, social media content often includes a mix of meaningful and seemingly unrelated information, such as emotional expressions, user activities, and various multimedia elements. This complexity makes location inference particularly challenging and provides the best case for simulating attacks on geographic privacy. To test GeoLocator's performance, a set of social media posts containing both text and images was curated. These posts were selected to represent a wide range of scenarios, from everyday updates to more specific location-centric content. The goal was to determine whether GeoLocator, with its finely tuned large multimodal models (LMMs), could accurately infer the locations described or depicted in these posts. Applying GeoLocator to these realistic and varied social media scenarios, we aimed to simulate \begin{table}
\begin{tabular}{l l l} \hline \hline
**Data Source** & **Description** & **Number of Images** \\ \hline \multirow{4}{*}{Google Maps} & \begin{tabular}{l} Iconic landmarks \\ \end{tabular} & 50 images \\ \cline{2-3}  & \begin{tabular}{l} Street view without obvious \\ landmarks \\ \end{tabular} & 50 images \\ \cline{2-3}  & \begin{tabular}{l} Images of 20 locations from \\ two different angels \\ \end{tabular} & 20 sets (40 images) \\ \hline \multirow{2}{*}{Taken by a research team} & \begin{tabular}{l} Images of 20 locations at two \\ time slots (i.e., day and \\ nighttime) \\ \end{tabular} & 20 sets (40 images) \\ \hline \multirow{2}{*}{Google Images} & \
\begin{tabular}{l} Images of 10 locations from \\ China to assess the impact of \\ language input \\ \end{tabular} & 10 images \\ \hline Posts from social media &
\begin{tabular}{l} Social media posts sent by \\ research team members \\ \end{tabular} & 3 posts \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Data source and description. real-world conditions where users might unintentionally share geographic information. The results of this experiment are crucial for understanding how effectively GeoLocator can manage the complexities of social media data while ensuring user privacy and providing accurate location inferences. This step highlights GeoLocator's advanced capabilities in navigating the intricate landscape of social media content, showcasing its potential for practical applications in protecting geographic privacy.