A Comprehensive Review of NLP Techniques for Military Terminologies and Information Operations on Social Media

IF 3.6 3区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Tamara Zhukabayeva;Zulfiqar Ahmad;Aigerim Yerimbetova;Madina Sambetbayeva;Duman Telman;Abdygalym Bayangali;Elmira Daiyrbayeva
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Abstract

This paper presents a comprehensive study aimed at systematically analyzing and evaluating natural language processing (NLP) techniques for military information operations, with a special focus on social media intelligence. Among an ever-growing complicated information environment, NLP methods like sentiment analysis, named entity recognition, and topic modeling have been essential in tracking online propaganda efforts, discovering emerging issues and threats globally with dialogues on military operations. These techniques make an impact on available decision making via situational awareness and getting the added extraction from volumes of unstructured data outputs thus increasing the overall strategic benefits to military organizations. There are technical and operational challenges concerning the use of NLP in a military context such as requirements for real-time data processing; language diversity; and maintaining data privacy while preserving ethical standards. To address these challenges, the study conducts an exhaustive survey of NLP methods, reviewing their range of applications, and highlights the relevance of several approaches for military information operations, with special emphasis on social media intelligence. The work further provides discussion on the comprehensive adoption of artificial intelligence (AI), edge computing, and multilingual NLP models for enhancing adaptability, efficiency, and transparency of the systems. It also extols the need for explainable AI (XAI) to improve accountability and trust by making term or even whole early warning systems derived from NLP analyses, transparent and interpretable for these military research applications with significant financial consequences. The paper also emphasizes the strategic importance of multilingual and multimodal analysis and the integration of specialized military lexicons to improve the contextual understanding of military discourse in social media environments. We also elucidate the important capabilities of NLP in enabling military operations to be responsive, rapid and data-driven while also adapting to the evolving nature of warfare. Key conclusions suggest that applying advanced NLP tools enhances situational awareness, enables timely threat detection, and supports more agile, data-informed decision-making within modern military operations. The paper shows a perspective to optimize NLP and AI technologies, leveraging various perspectives to benefit the operational needs of military and defense sectors in more data-rich environments.
军事术语和社交媒体信息操作的NLP技术综述
本文提出了一项全面的研究,旨在系统地分析和评估军事信息作战中的自然语言处理(NLP)技术,特别关注社交媒体情报。在日益复杂的信息环境中,情感分析、命名实体识别和主题建模等NLP方法在跟踪在线宣传工作、通过军事行动对话发现全球新出现的问题和威胁方面至关重要。这些技术通过态势感知对可用决策产生影响,并从大量非结构化数据输出中获得额外的提取,从而增加军事组织的整体战略效益。在军事背景下使用自然语言处理存在技术和操作方面的挑战,例如对实时数据处理的要求;语言的多样性;在保持道德标准的同时维护数据隐私。为了应对这些挑战,该研究对NLP方法进行了详尽的调查,回顾了它们的应用范围,并强调了几种方法与军事信息作战的相关性,特别强调了社交媒体情报。这项工作进一步讨论了人工智能(AI)、边缘计算和多语言NLP模型的全面采用,以提高系统的适应性、效率和透明度。它还赞扬了可解释的人工智能(XAI)的必要性,通过使基于NLP分析的术语甚至整个预警系统透明和可解释,为这些具有重大财务后果的军事研究应用提高问责制和信任。本文还强调了多语言和多模态分析以及专业军事词汇的整合对于提高对社交媒体环境下军事话语的语境理解的战略重要性。我们还阐明了NLP在使军事行动具有响应性、快速性和数据驱动性方面的重要能力,同时也适应了不断变化的战争性质。关键结论表明,应用先进的NLP工具可以增强态势感知能力,实现及时的威胁检测,并在现代军事行动中支持更灵活、数据知情的决策。本文展示了优化NLP和人工智能技术的视角,利用各种视角在数据更丰富的环境中使军事和国防部门的运营需求受益。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
IEEE Access
IEEE Access COMPUTER SCIENCE, INFORMATION SYSTEMSENGIN-ENGINEERING, ELECTRICAL & ELECTRONIC
CiteScore
9.80
自引率
7.70%
发文量
6673
审稿时长
6 weeks
期刊介绍: IEEE Access® is a multidisciplinary, open access (OA), applications-oriented, all-electronic archival journal that continuously presents the results of original research or development across all of IEEE''s fields of interest. IEEE Access will publish articles that are of high interest to readers, original, technically correct, and clearly presented. Supported by author publication charges (APC), its hallmarks are a rapid peer review and publication process with open access to all readers. Unlike IEEE''s traditional Transactions or Journals, reviews are "binary", in that reviewers will either Accept or Reject an article in the form it is submitted in order to achieve rapid turnaround. Especially encouraged are submissions on: Multidisciplinary topics, or applications-oriented articles and negative results that do not fit within the scope of IEEE''s traditional journals. Practical articles discussing new experiments or measurement techniques, interesting solutions to engineering. Development of new or improved fabrication or manufacturing techniques. Reviews or survey articles of new or evolving fields oriented to assist others in understanding the new area.
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