13^th International Conference on Software Engineering and Applications (SEAPP 2024)

Accepted Papers

Scoring Unstructured Data From Online Social Network for Homeland Security Applications

Samti Ahmed^{1, 2}, Semeh ben salem^{1, 2}, Sami Naouali^{1, 2}, ¹Sciences and Technologies for Defense (STD), Military Academy of Fandouk Jedid, Tunisia, ²Military Research Center (MRC), L’Aouina Military Base, Tunisia

ABSTRACT

The explosion of social network activity in recent years has led to massive volumes of user data, including status updates, posts, blog articles, forum entries, recommendations, login requests, and suggestions. This has given rise to new topics, including social media analytics and social network analysis. Analyzing online data to uncover terrorist trends is an essential task. It not only aids in comprehending terrorist events, including the actors, communities, methods, and operational tactics involved but also assists in predicting future attacks. However, this process remains challenging and error-prone, as terrorist events often deviate from conventional attack patterns. This paper introduces a scoring model, the Keyword Feature Score (KFS), for collecting data from social networks. The KFS model aims to assist investigators in conducting focused and specific analyses. Researchers can employ the KFS model to score and categorize suspicious comments related to homeland security within the Online Social Network (OSN) dataset, thereby further strengthening the model’s robustness.

KEYWORDS

Text scoring, natural language processing, Text classification, open source intelligence, social Network, homeland Security, text mining.