Twitter Data Analysis: Izmir Earthquake Case
This provides incremental analysis of social media data for disaster response in Turkey, addressing the need for real-time public opinion gathering during earthquakes.
The study analyzed Twitter posts about the October 2020 Izmir earthquake using data mining and NLP methods, including LDA for topic modeling and BERT for sentiment analysis, to gather public opinion insights for disaster management. It found that users shared goodwill wishes, contributed to aid activities, and sought to be heard by authorities, with the methods working effectively.
Türkiye is located on a fault line; earthquakes often occur on a large and small scale. There is a need for effective solutions for gathering current information during disasters. We can use social media to get insight into public opinion. This insight can be used in public relations and disaster management. In this study, Twitter posts on Izmir Earthquake that took place on October 2020 are analyzed. We question if this analysis can be used to make social inferences on time. Data mining and natural language processing (NLP) methods are used for this analysis. NLP is used for sentiment analysis and topic modelling. The latent Dirichlet Allocation (LDA) algorithm is used for topic modelling. We used the Bidirectional Encoder Representations from Transformers (BERT) model working with Transformers architecture for sentiment analysis. It is shown that the users shared their goodwill wishes and aimed to contribute to the initiated aid activities after the earthquake. The users desired to make their voices heard by competent institutions and organizations. The proposed methods work effectively. Future studies are also discussed.