RIDIR: Collaborative Research: Integrated Communication Database and Computational Tools

RIDIR：协作研究：集成通信数据库和计算工具

• 批准号: 1831481
• 负责人: Jennifer Pan
• 金额: $ 20.95万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-15 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1831481&HistoricalAwards=false
• 关键词: RIDIR; Collaborative; Research; Integrated; Communication

This project will develop an integrated research framework for sociotechnical cybersecurity research and broader investigations of information provenance by behavioral, information, and computer scientists. Currently, researchers are mainly limited to natural language processing of large bodies of online text. This project will make it possible to analyze larger information worlds, including those from such countries as China, and the flow of information, including video and audio information, in newspapers, TV, and online sources. The project addresses a core goal of cybersecurity research, which is to understand the provenance, flow, and termination of information warfare, and censorship.The project is aimed at constructing an integrated and unified information database that combines mass communication data from TV and print sources from six locations, with data from two popular online communication platforms. The project will generate a variety of metadata and time series data on topics, actors, events, and sentiments presented in communications by automated multimodal content analysis using text, image, video, and audio. Variables will be linked to identify trajectories of information flow between communication channels through multiple platforms. It will develop a new class of computational models and algorithms that can automatically analyze both verbal and nonverbal communications data by machine learning, computer vision, deep learning, and natural language processing. This project will allow researchers across the computational and social sciences to access the metadata and time series data through a search interface for qualitative research, a statistical package for quantitative research, and various visualization tools. This project will therefore link previously untapped data sources using cutting-edge computational methods to enable scholars to conduct systematic research on large-scale patterns in the emerging information and communication ecosystem.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目将为社会技术网络安全研究和行为、信息和计算机科学家对信息来源的更广泛调查开发一个综合研究框架。目前，研究人员主要局限于对大量在线文本的自然语言处理。该项目将使分析更大的信息世界成为可能，包括来自中国等国家的信息，以及报纸，电视和网络来源的信息流，包括视频和音频信息。该项目以了解信息战的起源、流向、终结和审查制度为网络安全研究的核心目标，旨在构建一个综合统一的信息数据库，该数据库将来自六个地方的电视和印刷媒体的大众传播数据与来自两个流行的在线传播平台的数据相结合。该项目将通过使用文本、图像、视频和音频的自动多模式内容分析，生成关于通信中呈现的主题、演员、事件和情绪的各种元数据和时间序列数据。变量将被连接起来，以确定通过多个平台的通信渠道之间的信息流的轨迹。它将开发一类新的计算模型和算法，可以通过机器学习，计算机视觉，深度学习和自然语言处理自动分析语言和非语言通信数据。该项目将允许计算科学和社会科学的研究人员通过定性研究的搜索界面、定量研究的统计软件包和各种可视化工具访问元数据和时间序列数据。因此，该项目将利用最先进的计算方法将以前未开发的数据源连接起来，使学者能够对新兴信息和通信生态系统中的大规模模式进行系统研究。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

CASM: A DEEP-LEARNING APPROACH FOR IDENTIFYING COLLECTIVE ACTION EVENTS WITH TEXT AND IMAGE DATA FROM SOCIAL MEDIA

• DOI: 10.1177/0081175019860244
• 发表时间: 2019-01-01
• 期刊: SOCIOLOGICAL METHODOLOGY, VOL 49
• 作者: Zhang, Han;Pan, Jennifer
• 通讯作者: Pan, Jennifer

Selectively localized: Temporal and visual structure of smartphone screen activity across media environments

• DOI: 10.1177/20501579221080333
• 发表时间: 2022-02-21
• 期刊: MOBILE MEDIA & COMMUNICATION
• 影响因子: 4.9
• 作者: Muise, Daniel;Lu, Yingdan;Reeves, Byron
• 通讯作者: Reeves, Byron

How Information Flows from the World to China

• DOI: 10.1177/19401612221117470
• 发表时间: 2022-08
• 期刊: The International Journal of Press/Politics
• 作者: Yingda Lu;Jack Schaefer;Kunwoo Park;Jungseock Joo;Jennifer Pan

Capturing Clicks: How the Chinese Government Uses Clickbait to Compete for Visibility

• DOI: 10.1080/10584609.2020.1765914
• 发表时间: 2020-07
• 期刊: Political Communication
• 影响因子: 7.5
• 作者: Yingda Lu;Jennifer Pan

The Pervasive Presence of Chinese Government Content on Douyin Trending Videos

抖音热门视频中普遍存在中国政府内容

• 发表时间: 2022
• 期刊: Computational communication research
• 作者: Lu, Yingdan;Pan, Jennifer
• 通讯作者: Pan, Jennifer