# Twitter Data Analysis: Izmir Earthquake Case

arXiv: https://arxiv.org/abs/2212.01453 · HTML: https://mlsanity.com/paper/2212.01453/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 23 references that are in the map.
- First posted on arXiv 2 Dec 2022.

## Builds on (most cited first, 3 of 3)

- [Sentiment Analysis of Twitter Data: A Survey of Techniques](https://mlsanity.com/paper/1601.06971/) (2016)
- [Twitter Speaks: A Case of National Disaster Situational Awareness](https://mlsanity.com/paper/1903.02706/) (2019)
- [Validation of Twitter opinion trends with national polling aggregates: Hillary Clinton vs Donald Trump](https://mlsanity.com/paper/1610.01587/) (2016)

## Abstract (arXiv)

T"urkiye 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.