This review is concerned with the article titled "Content Analysis of Multi-Annual Time Series of Flood-Related

Twitter (X) Data". It is divided into three categories, namely, general comments, specific comments and technical comments.

General comments: The title of the article "Content Analysis of Multi-Annual Time Series of Flood-Related Twitter (X) Data" clearly reflects the contents of the paper, and the abstract provides a concise, complete, and unambiguous summary of the work done and the results obtained. Both these sections are pertinent and easy to understand. The manuscript is well-written and well-structured, delivering the idea, methodology, and results clearly and concisely. The figures are descriptive and of high quality, and the tables are informative. It is well-referenced with proper credit attributed to previous and/or related works, and the authors indicate each of their contributions and competing interests. Crediting the use of AI tools such as ChatGPT is fantastic, we are conducting research in the age of the AI revolution. The paper presents a comprehensive and innovative approach to using social media data from Twitter (X) to understand human behaviour and perceptions during several types of flooding events in Germany. The study develops an approach using advanced natural language processing (NLP) techniques, leveraging pre-existing and accessible tools, including transformer-based models like SBERT and clustering algorithms such as HDBSCAN, to automatically extract flood-related topics from large social media datasets. Several steps to clean and filter the data have been presented. This allows for a nuanced analysis of public response to various flood events. The paper’s relevance is clear, given the increasing reliance on real-time social media data for disaster risk management and the potential to enhance flood preparedness and response strategies. Thus, this manuscript has good scientific significance, scientific quality, and presentation quality.



Specific Comments:

1. Clarification on Data Filtering: The process for removing irrelevant tweets is well-explained. However, more detail on the limitations of this filtering process could be helpful.

2. Interpretation of Topic Groups: The clustering approach is well-explained. However, further discussion on the specific implications of the topics identified (such as "disaster management" or "fatalities") could be more elaborated.

3. Comparisons with Traditional Data Sources: The paper highlights Twitter (X) data as an alternative to traditional flood impact assessments. What would be the difference between the results from social media and conventional data sources?

Technical Comments:

1. Grammar and Style: 

   • Line 98: "The The Second" should be "The second."

2. Figure Labels and Descriptions: The figures provide valuable visual insights, but some (esp. Fig 3 & 4) would benefit from clearer labels or captions, particularly where technical details like clustering results or topic distributions are involved.

3. In-text Citations Formatting: Ensure that citations within the text follow a consistent format. There are some minor inconsistencies in how sources are referenced throughout the manuscript.