## 1 Introduction
(2011) [20] was one of the first to examine spatial credibility and found that 34% of data producers on SM do not provide their correct location. To support this finding, the study compared user profiles with the location information mentioned in tweet content. Users' actual locations were determined based on their attendance at a concert event. Since the event was confined to a specific concert venue, the dataset was suitable for investigating the accuracy of user-reported locations. The study aimed to draw general conclusions about users' location-sharing behaviours and potential location manipulation. However, in large-scale disasters covering wide areas, it becomes challenging to verify the real-time accuracy of users' locations. [PERSON] and [PERSON] (2012) [21] reported that solely 17% of gathered tweets provide credible situational awareness information. An algorithm utilising RankSVM and a relevance feedback approach was evaluated to rank tweets from 14 high-impact events based on the perceived credibility of the information in the tweet text (categorised as definitely credible, seems credible, definitely not credible, or undecided). This ranking algorithm focuses solely on the textual content and does not incorporate any spatial dimension of the tweets. [PERSON] et al. (2017) [22] presented a testing witnessing status of SM users based on evidence they share on their profiles. They present a framework for identifyingthe potential social media users' witnessing to an event based on evidence (such as text and image content) in their SM accounts. [PERSON] et al. (2013) [PERSON] et al. (2013) proposed the \"credRank\" algorithm to identify coordinated users (accounts acting together to dominate the results of something) based on the users' profile information such as tweeting timestamps and voting behaviour. [PERSON] et al. (2019) [PERSON] et al. (2019) proposed a framework for SM data credibility during disasters and tested it with Hurricane Harvey Twitter data. The research is based on the calculation of event credibility with semantic and spatiotemporal aggregation. The main focus of the study is extracting location information from text and localising tweet content. The credibility rate of an event was calculated based on the supporting tweets and retweets counts. Neither of these studies are in the same domain, nor are the methods similar, yet all aim to reach credible first-hand information about an event from SM. Credibility is not only associated with text content but also with spatial credibility. Since textual content can be easily manipulated by social media users, the spatial dimension may, in the long run, offer a more reliable relevance signal for an event. Although spatial information can also be manipulated, it is less likely to be altered consistently and continuously. Therefore, our hypothesis in this study is that user trajectory data can provide meaningful relevance information for event verification. The motivation behind this study is to scrutinise second-hand or misleading information shared during earthquakes to distinguish between those who are physically present in the affected area, potential victims or eyewitnesses, and those who are not. To date, geo-social media (geo-SM) data producers' credibility determination has still not been extensively studied. This study set out to identify the predictors for geo-SM users' credibility based on spatial trajectory data. Trajectory data mining was extensively reviewed by [PERSON] and [PERSON] (2016) [PERSON] and [PERSON] (2016) [PERSON] and [PERSON] (2015) [PERSON] et al. (2015). The pre-defined data processing steps vary based on the trajectory data sources, which are broadly classified as GPS, GSM, geo-SM, Wi-Fi, and RFID [PERSON] and [PERSON] (2016); [PERSON] and [PERSON] (2016); [PERSON] and [PERSON] (2016). Trajectory data change with regard to the frequency of space and time based on these data sources' pre-defined or uncertain settings. There are also different types of moving objects (such as animals, vehicles, human, etc.)