Samti Ahmed1, 2, Semeh ben salem1, 2, Sami Naouali1, 2, 1Sciences and Technologies for Defense (STD), Military Academy of Fandouk Jedid, Tunisia, 2Military Research Center (MRC), L’Aouina Military Base, Tunisia
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.
Text scoring, natural language processing, Text classification, open source intelligence, social Network, homeland Security, text mining.
Hanna Abi Akl, Data ScienceTech Institute, 4 Rue de la Collégiale, 75005, Paris, France
We introduce SHADOW, a fine-tuned language model trained on an intermediate task using associative deductive reasoning, and mea- sure its performance on a knowledge base construction task using Wiki- data triple completion. We evaluate SHADOW on the LM-KBC 2024 challenge and show that it outperforms the baseline solution by 20% with a F1 score of 68.72%.
Knowledge Graphs, Large Language Models, Ontologies.
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