Recently, social media has become a key platform that allowed people to interact and share information. The use of social media is expanding significantly and can serve a variety of purposes. Over the last few years, users of social media have played an increasing role in the dissemination of emergency and disaster information. In this paper, we conduct a case study exploring how Thai people used social media such as Twitter in response to one of the country's worst disasters in recent history: the 2011 Thai Flood. We combine multiple analysis methods in this study, including content analysis of Twitter messages, trend analysis of different message categories, and influential Twitter users analysis. This study helps us understand the role of social media in time of natural disaster.

In this paper, we analyze and compare various approaches for Thai word segmentation. The word segmentation approaches could be classified into two distinct types, dictionary based (DCB) and machine learning based (MLB). The DCB approach relies on a set of terms for parsing and segmenting input texts. Whereas the MLB approach relies on a model trained from a corpus by using machine learning techniques. We compare between two algorithms from the DCB approach: longest-matching and maximal matching, and four algorithms from the MLB approach: Naive Bayes (NB), decision tree, Support Vector Machine (SVM), and Conditional Random Field (CRF). From the experimental results, the DCB approach yielded better performance than the NB, decision tree and SVM algorithms from the MLB approach. However, the best performance was obtained from the CRF algorithm with the precision and recall of 95.79% and 94.98%, respectively.

In this paper, a method of automatically classifying

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