## 4 Conclusions  
In this study, we examined the spatial trajectories of social media (SM) users to infer the relevance of their content to earthquake events. Social media content is inherently uncertain, unstructured, and heterogeneous. While text-based filtering and classification techniques are widely used, only a limited number of studies have employed spatial trajectories to classify content relevance and assess user credibility. [PERSON] (2018, 2020) [52; 53], for example, highlights the influence of geodemographics on SM posts, showing how they shape representations of urban places in Greater London and Los Angeles. From a reverse-engineering perspective, spatial representations can also reveal underlying geodemographic patterns. Our findings demonstrate that trajectory data provide valuable insights for assessing the relevance of SM content to earthquake events. We applied machine learning (ML) models to trajectory-based features extracted from a relatively small dataset with imbalanced class distributions. In configuring model parameters, we accounted for dataset size and class imbalance, as emphasised by [PERSON] et al. (2009) [45], to reduce bias and variance. Despite these constraints, the models achieved high overall accuracy. However, studies of this nature require larger datasets to increase impact and improve generalisability. To support future research, our data and code are available upon reasonable request. A key limitation of this study is the reliance on labelled data, which may introduce bias or limit scalability. Future work could address this through semi-supervised learning or crowdsourced annotation to improve both the quality and quantity of training data. Additionally, while our models performed well on the current dataset, methodological robustness could be strengthened through spatial or temporal cross-validation to evaluate performance across different geographic or temporal conditions. The true merit of modern mapping technologies lies not only in the existence of crowdsourced platforms but in their purposeful application to generate actionable value. In this study, we focused on leveraging such platforms for life-saving efforts in earthquake crisis management. In earthquake-prone countries such as Turkiye, SM plays a vital role in incident mapping, particularly given the vulnerabilities and infrastructural damages that follow major disasters. On 6 February 2023, two devastating earthquakes with magnitudes of 7.7 and 7.6 struck 10 cities in Turkiye, resulting in over 50,000 deaths and millions of injuries. Telecommunications infrastructure failed in many areas, and Internet bandwidth was restricted by the government to suppress the spread of misinformation. Despite this, demand for emergency assistance was high, and the country was largely unprepared for a disaster of this scale. Social media remained operational and could have played a pivotal role in emergency mapping, yet it was underutilised. Although some online communities mobilised to support crisis response, SM was overwhelmed by a flood of posts, many of which included false information, rumours, or irrelevant content, obstructing effective incident mapping. Unlike prior studies that focus solely on textual or network-based features, our approach integrates spatiotemporal trajectory patterns with content-level analysis, offering a more grounded and language-independent method of inferring tweet relevance. By relying on users' geolocations and temporal behaviours instead of linguistic cues, the method can generalise across different languages and regions. The fine-grained classification of tweets into directly relevant, indirectly relevant, and unrelated categories further enhances the interpretability and usefulness of the results. To validate the scalability of our approach, future work may apply the methodology to other seismic events using localised SM datasets. Moreover, the approach is adaptable to other types of crises (e.g., floods and wildfires), where spatiotemporal behaviour similarly reflects situational relevance.