# A Methodological Framework for AI-Driven Textual Data Analysis in Digital Media
## 5 Conclusions  
Digital media platforms, including news aggregators, social networks, newsletters, blogs, and news portals, have established themselves as essential channels for communication and information dissemination, continuously generating large volumes of textual data. These often unstructured data serve as a significant source of information for research across various fields, enabling the exploration of factors such as narrative analysis, informational flows, and communication strategies. However, the analytical potential of these data is challenged by issues related to collection, processing, and interpretation, particularly in complex scenarios. In response to these challenges, this study proposed a comprehensive and practical methodology integrating artificial intelligence, statistical methods, and NLP techniques. The methodology covered the entire analysis cycle, from data collection to result visualization, demonstrating a workflow for the application of various techniques and tools. Among its main advantages are the flexibility in data collection, facilitated by the use of web scraping solutions, and data enrichment through advanced natural language processing tools, such as NER and algorithms designed for knowledge discovery. These strategies enable the systematic exploration of large volumes of digital media data, transforming unstructured content into actionable information for specific analyses. The validation of the proposed methodology was conducted through a case study analyzing news data aggregated by Google News in two regional versions--Israel and Lebanon--during a period of heightened tensions in the Middle East. The results revealed how editorial characteristics, curation algorithms, and geopolitical contexts influence the composition and focus of aggregated news. Furthermore, the analysis highlighted specific communication dynamics, such as the use of clickbait strategies and differences in the representation of events and political actors between the versions analyzed. Among the limitations of the proposed methodology, it is important to note that data acquisition--whether through APIs or web scraping solutions--is not always guaranteed. Not all platforms offer accessible or sufficiently comprehensive APIs to meet the needs of complex investigations, and, in many cases, web scraping may be hindered by technical issues, such as frequent changes in webpage structure, or legal restrictions, such as terms of use explicitly prohibiting this practice. Additionally, the proposed methodological strategy relies on artificial intelligence models with sufficient reliability to perform robust analyses. Although the AI models discussed, such as BERT and NER, demonstrated high performance, it is essential to emphasize that they require validation within the specific context of the analyzed data. Thus, the mere application of a model to any given problem without considering contextual specificities is insufficient; it is necessary to verify its applicability, ensure its accuracy is adequate for the dataset, and adjust parameters as needed to guarantee the validity of the inferences made. As future research directions, the study could be expanded to include comparative analyses across a broader range of algorithmic solutions, contexts, and digital media platforms, enabling, for example, the exploration of cross-cultural dynamics in media representation and consumption. Additionally, the development of automated solutions for adapting to dynamic webpage structures during web scraping would enhance the robustness and scalability of data collection. A deeper exploration of multimodal data--such as combining textual, visual, and audiovisual content--could also provide a more comprehensive understanding of media dynamics in digital environments. Finally, it is important to emphasize that ethical considerations are essential in studies involving the collection and analysis of digital media data.