Narrative Maps Visualization Tool (NMVT): An interactive narrative analytics system based on the narrative maps framework

IF 2.4 4区 计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Brian Keith Norambuena
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引用次数: 0

Abstract

The Narrative Maps Visualization Tool (NMVT) is an interactive visual analytics system designed to help analysts understand complex narratives from collections of text documents. NMVT leverages graph-based representations to extract and visualize coherent storylines, showing how events connect over time. The system integrates advanced features including document clustering, coherence-based optimization, storyline extraction, and explainable AI components that provide interpretable insights into narrative connections. NMVT supports both directed analysis (connecting specific events) and exploratory analysis (discovering emerging storylines). By enabling analysts to make sense of large document collections, NMVT addresses critical challenges in intelligence analysis, computational journalism, and misinformation research, allowing users to effectively connect the dots between seemingly unrelated events. The system has been successfully demonstrated on news data by extracting coherent narrative structures that capture both main storylines and alternative perspectives. Case studies show that NMVT’s semantic interaction capabilities enable analysts to refine narratives based on domain expertise, while the explainable AI components increase trust in the system’s outputs.
叙事地图可视化工具(NMVT):基于叙事地图框架的交互式叙事分析系统
叙事地图可视化工具(NMVT)是一个交互式可视化分析系统,旨在帮助分析人员从文本文档集合中理解复杂的叙事。NMVT利用基于图形的表示来提取和可视化连贯的故事情节,显示事件如何随时间连接。该系统集成了高级功能,包括文档聚类、基于连贯性的优化、故事情节提取和可解释的AI组件,这些组件可为叙事联系提供可解释的见解。NMVT既支持定向分析(连接特定事件),也支持探索性分析(发现新出现的故事情节)。通过使分析人员能够理解大型文档集合,NMVT解决了情报分析、计算新闻和错误信息研究中的关键挑战,允许用户有效地将看似不相关的事件之间的点连接起来。该系统已经成功地在新闻数据上进行了演示,通过提取连贯的叙事结构来捕捉主要故事情节和替代视角。案例研究表明,NMVT的语义交互能力使分析人员能够根据领域专业知识改进叙述,而可解释的人工智能组件增加了对系统输出的信任。
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来源期刊
SoftwareX
SoftwareX COMPUTER SCIENCE, SOFTWARE ENGINEERING-
CiteScore
5.50
自引率
2.90%
发文量
184
审稿时长
9 weeks
期刊介绍: SoftwareX aims to acknowledge the impact of software on today''s research practice, and on new scientific discoveries in almost all research domains. SoftwareX also aims to stress the importance of the software developers who are, in part, responsible for this impact. To this end, SoftwareX aims to support publication of research software in such a way that: The software is given a stamp of scientific relevance, and provided with a peer-reviewed recognition of scientific impact; The software developers are given the credits they deserve; The software is citable, allowing traditional metrics of scientific excellence to apply; The academic career paths of software developers are supported rather than hindered; The software is publicly available for inspection, validation, and re-use. Above all, SoftwareX aims to inform researchers about software applications, tools and libraries with a (proven) potential to impact the process of scientific discovery in various domains. The journal is multidisciplinary and accepts submissions from within and across subject domains such as those represented within the broad thematic areas below: Mathematical and Physical Sciences; Environmental Sciences; Medical and Biological Sciences; Humanities, Arts and Social Sciences. Originating from these broad thematic areas, the journal also welcomes submissions of software that works in cross cutting thematic areas, such as citizen science, cybersecurity, digital economy, energy, global resource stewardship, health and wellbeing, etcetera. SoftwareX specifically aims to accept submissions representing domain-independent software that may impact more than one research domain.
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