Sosyal medya verilerinin zaman-mekansal temellere göre makine öğrenimi

In recent years, machine learning algorithms have greatly revolutionized the methods used to analyze vast quantities of social media data and extract valuable insights from it. Researchers can leverage algorithms to enhance their understanding of human behaviors, reactions, and emotions, particularly in the context of natural disasters such as earthquakes. Analyzing social media data in terms of both space and time is extremely important. Utilizing machine learning on social media data related to earthquakes in a spatio-temporal manner allows for prompt interventions and allocation of resources, enabling early identification and rapid reaction by evaluating geo-tagged postings and real-time information sharing. It improves communication among individuals or groups involved and supports the ability to endure and recover from natural disasters by offering valuable and significant data on analyzing emotions, long-term strategies for recovery, and geographical patterns of damage. This study exploits crowdsourcing via social media data to extract information about emergency situations and needs after the earthquake. Using the semi-supervised method, the data has been labeled as either rescue or nonrescue to reach a high level of accuracy in detection. After the earthquake, rescue situations are detected on a spatial and temporal basis, along with location and time information provided by tweets. Two destructive earthquakes of magnitudes of Mw 7.7 and Mw 7.6 occurred on February 6, 2023, in the southeast of Turkiye. 53.537 people died, 107.213 people were injured, and several buildings were damaged. A total of 2.5 million tweets related to these earthquakes were collected from February 6 to February 28, 2023, through the X platform. For labeling purposes, nine BERT language models that are based on attention and transformers were used. Supervised learning methods, including logistic regression, support vector machines, decision trees, multinomial Naïve Bayes, and XGBoost, were applied to assess the precision of the labels and perform classification. Furthermore, the data set was processed with deep learning methods: convolutional neural networks, deep neural networks, and long short-term memory. A timely and proper response to delivering efficient solutions is possible only when the requests for assistance, rescue, and emergency are promptly and accurately understood. In this thesis, we determined the key terms of each data set through an extensive study of its spatio-temporal dynamics, allowing us to identify urgent supplies and use protection at the appropriate time quickly and clearly. The accuracy of data toward the detection of rescue and non-rescue situations is compared, and keywords on a spatio-temporal basis are extracted to determine hazard situations and emergency needs for coordination purposes. Deep learning and BERT models for detection of rescue and non-rescue classes reach a level of 0.8912 and 0.9792 in recall, respectively. This study highlights the vital importance of machine learning and deep learning in extracting valuable and applicable insights from social media data, especially in urgent scenarios like natural disasters. It achieves this by providing a thorough comprehension of the changing patterns that occur after the earthquake in Turkiye, incorporating both spatial and temporal factors into the analysis. The results demonstrate the effectiveness of the models in classifying microblogs connected to disasters.