In the latest years, the use of social media has increased dramatically. Content, as well as media, are shared in Big Data volumes and this poses a critical requirement for the behavior supervision and fraud protection. The detection of terrorist behavior in the social media is essential to every country, but has complexities in both the supervision of shared content and in the understanding of behavior. Therefore, in this project an artificial intelligence enabled Detection Terrorist behavior system (ALT-TERROS) as a key priority was developed. The key requirements for a terrorist behavior detection system operating in the Kingdom are: (i) Data integration, (ii) Advanced smart analysis capacity and (iii) Decision making capability. The unique value proposition is based on a sophisticated integrated approach to the management of distributed data available on social media, which uses advanced social mining methods for the detection of patterns of terrorist behavior, its visualization and use for decision making. In addition, several critical issues related to the availability of APIs to handle Arabic text as well as the need to provide an end-to-end workflow from the extraction of textual and visual data over social media to the deliverable of advanced analytics and visualizations for rating mechanisms were highlighted. The key contribution of our approach is a testbed for the application of novel scientific approaches and algorithms for the rating of harm associated to social media content. The complexity of the problem does not allow hyper-optimistic solutions, but the combination of heuristic rules and advanced decision-making capabilities is toward the right direction. We contribute to the body of the theory of Sentiment Analysis for Arabic content and we also summarize a heuristic algorithm developed for the future. In the future research directions, we emphasize on the need to develop trusted Arabic thesaurus and corpus for the use sentiment analysis.

近年来，社交媒体的使用急剧增加。内容以及媒体在大数据量中共享，这对行为监管和欺诈保护提出了关键要求。在社交媒体中检测恐怖行为对于每个国家都是必不可少的，但是在共享内容的监管和行为理解方面都具有复杂性。因此，在该项目中，开发了具有人工智能功能的“检测恐怖行为系统”（ALT-TERROS）作为主要优先事项。在沙特王国运行的恐怖行为检测系统的关键要求是：（i）数据集成，（ii）先进的智能分析能力和（iii）决策能力。独特的价值主张基于复杂的集成方法来管理社交媒体上可用的分布式数据，该方法使用高级社交挖掘方法来检测恐怖行为的模式，对其进行可视化并用于决策。此外，还有几个关键问题，涉及用于处理阿拉伯文本的API的可用性，以及提供端到端工作流的需求，从通过社交媒体提取文本和视觉数据到为实现高级分析和可视化交付评级机制得到了强调。我们的方法的主要贡献在于为新颖的科学方法和算法的应用提供了测试平台，以评估与社交媒体内容相关的伤害。问题的复杂性不允许采用超乐观的解决方案，但是启发式规则和先进的决策能力的结合才是正确的方向。我们为阿拉伯语内容的情感分析理论做出了贡献，并总结了为未来开发的启发式算法。在未来的研究方向上，我们强调需要开发可信任的阿拉伯词库和语料库以进行使用情绪分析。