## 1 Introduction  
Earthquakes are severe disasters affecting wide areas that result in multidimensional hazards and losses in the short and long run. On-time management is the key to reducing the secondary impacts of these kinds of disasters on living beings, the environment, and the economy [1, 2, 3, 4]. Geospatial data science immensely contributes to disaster management with various data sources (earth observatory satellites, aerial vehicles, and geospatial crowd sourcing) and information extraction algorithms [5, 6]. SM became an actor of geospatial data sources since enabling location information features. This actor gives us the ability to see different dimensions of events apart from observed from the space, e.g., emergencies inside buildings, the status of living beings, and the sentiments [7, 8]. There is a growing amount of research using SMD locations to identify urban dynamics [9, 10, 11], sentimental reactions [12, 13], and changes in human mobility [14, 15, 16]. Most of these studies are largely concentrated on social media data intensity to answer coarse -grained questions, such as \"Where are the most visited locations?\", \"How does sentiment sprawl after a major disaster?\", and \"When does human mobility vary?\". What is not clear yet are the answers to the fine-grained questions on social media data, such as \"Where are the victims' locations?\", \"What is the consistency between the content and the attached location for each post after an incident?\", and \"When did the aforementioned event in the post happen?\". Current methods primarily focus on problems that can be addressed using density-based spatial algorithms or text frequency-based approaches [9; 10; 11; 12; 13; 14; 15; 16]. As a result, these studies inherently rely on the clustering of multiple reports, which provides a form of internal validation. However, such methodologies tend to overlook individual emergency notifications and may also incorporate clusters formed as a result of misinformation. Neglecting individual emergency messages can lead to a scenario where only widely shared alerts gain visibility, while genuine victims in urgent need, who may have been able to send out only a single message, are left without assistance. Furthermore, the spread of misinformation can result in a high concentration of false reports in certain locations, leading to the misallocation of resources to areas where no real need exists. Therefore, social media data should be analysed at the individual level and supported with user-based credibility assessments. In this approach, each user is treated as an independent source of information, and the reliability of this source should be thoroughly examined. There are also several studies carried out on SMD that contribute to disaster management in various cases [17]. [PERSON] and [PERSON] (2015) [18] investigated Hurricane Sandy Twitter data by categorising them into topics regarding disaster management phases. [PERSON] et al. (2018) [19] proposed a combined technique with semantic machine learning and spatiotemporal analysis for natural disaster monitoring and examined Napa Earthquake tweets. Most of these disaster domain studies are well documented and present significant results for coarse-grained problematics with a high intensity of data. However, these studies [17; 18] do not assess the overall credibility of tweets; instead, they focus on classifying the relevance of content based on natural language processing, which may include second-hand information or misinformation. Additionally, mapping results using spatial clustering techniques based on high data intensity fails to address the extraction of individual earthquake-related tweets and, consequently, overlooks issues related to their reliability or credibility. Yet, the credibility of SMD producers is a key issue in answering the fine-grained questions. So far, very little research has been carried out on social media data producers' credibility during events. [PERSON] et al.