Collaborative Research: Collective Sense Making Following a Terrorist Attack: The Immediate and Long-Term Impact on Public Resilience

合作研究：恐怖袭击后的集体意识：对公众抵御能力的直接和长期影响

• 批准号: 1634944
• 负责人: Yu-Ru Lin
• 金额: $ 18.1万
• 依托单位: University of Pittsburgh
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1634944&HistoricalAwards=false
• 关键词: Collaborative; Research; Collective; Sense; Making

Social media has become central to the public's response to terrorism. From the transmission of breaking news, to the offering of social support, to the dissemination of radical, hateful messages, people increasingly turn to social media to both share and gain understandings of terrorist events. This project utilizes the social media data to investigate the reactions of individuals located in Paris during the November 2015 attacks. It analyzes both their immediate social and emotional reactions to the attacks as well as longer-term changes in their communication behavior. The investigation focuses on three questions. First, in immediate response to the attacks, how, and how effectively, did improvised logistical communications, such as the use of the #PorteOuverte hashtag offering shelter to strangers, emerge in the chaotic and emotional context of the attack aftermath. Second, what effect did attention by individuals to different aspects of the attack, such as particular news stories, the role of different ethnic groups, or other salient aspects of social media discussion after the attacks influence their longer term attitudes toward the threat of terrorism. Third, how and to what extent did government authorities and professional news outlets sway this public attention both broadly and for specific social communities. This research outcome will both improve responses to specific terrorist attacks as well as enhance public understanding of the specific means through which terrorism wields social influence. The project advances the theoretical study of collective sensemaking, understood here as the emergent, communal attempt to cognitively understand, emotionally accept, and logistically respond to surprising, disruptive events. The project addresses collective sensemaking in a novel way by focusing on both temporal and subject based differences. Analysis will focus on both short-term and longer-term communication behavioral changes, such as changes in the expression of sentiment or of reference to specific URLs. Methodological issues such as selection bias and the measurement of different sensemaking behaviors will be addressed by leveraging text mining, network science methods and techniques, and communication theories of social media. In so doing, the project will greatly improved understanding of both the immediate and longer-term behavioral responses of citizens to a terrorist attack.

社交媒体已成为公众应对恐怖主义的核心。 从传播突发新闻，到提供社会支持，再到传播激进、仇恨的信息，人们越来越多地转向社交媒体，以分享和了解恐怖主义事件。该项目利用社交媒体数据调查2015年11月袭击事件期间位于巴黎的个人的反应。它分析了他们对袭击的即时社会和情感反应，以及他们沟通行为的长期变化。调查集中在三个问题上。 首先，在对袭击的即时反应中，如何以及如何有效地进行即兴后勤通信，例如使用#PorteOuverte标签为陌生人提供庇护，在袭击后的混乱和情绪化背景下出现。 其次，个人对袭击事件不同方面的关注，如特定的新闻报道，不同种族群体的作用，或袭击后社交媒体讨论的其他突出方面，会影响他们对恐怖主义威胁的长期态度。 第三，政府当局和专业新闻媒体如何以及在多大程度上影响了公众对广泛和特定社会群体的关注。这一研究成果将改善对具体恐怖袭击的反应，并提高公众对恐怖主义施加社会影响的具体手段的认识。该项目推进了集体意义建构的理论研究，在这里被理解为认知上理解，情感上接受和逻辑上应对令人惊讶的破坏性事件的紧急，共同的尝试。 该项目以一种新颖的方式通过关注时间和基于主题的差异来解决集体意义建构问题。 分析将侧重于短期和长期的通信行为变化，例如情感表达或引用特定URL的变化。 方法论问题，如选择偏见和不同的意义构建行为的测量将利用文本挖掘，网络科学的方法和技术，以及社会媒体的传播理论来解决。这样做，该项目将大大提高对公民对恐怖袭击的即时和长期行为反应的理解。

项目成果

Tracking Disaster Response and Relief Following the 2015 Nepal Earthquake

跟踪 2015 年尼泊尔地震后的救灾和救援工作

• DOI: 10.1109/cic.2016.075
• 发表时间: 2016
• 期刊: International Workshop on Collaborative Internet Computing for Disaster Management (CIC­DM 2016
• 作者: Su, Yue;Lan, Ziyi;Lin, Yu-Ru;Comfort, Louise K.;Joshi, James
• 通讯作者: Joshi, James

Event Analytics via Discriminant Tensor Factorization

• DOI: 10.1145/3184455
• 发表时间: 2018-10
• 期刊: ACM Transactions on Knowledge Discovery from Data (TKDD)
• 作者: Xidao Wen;Y. Lin;K. Pelechrinis

Quantifying Content Polarization on Twitter

• DOI: 10.1109/cic.2017.00047
• 发表时间: 2017-10
• 期刊: 2017 IEEE 3rd International Conference on Collaboration and Internet Computing (CIC)
• 作者: Muhe Yang;Xidao Wen;Y. Lin;Lingjia Deng

Anomaly Detection in Dynamic Networks using Multi-view Time-Series Hypersphere Learning

• DOI: 10.1145/3132847.3132964
• 发表时间: 2017-11
• 期刊: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management
• 作者: Xian Teng;Y. Lin;Xidao Wen

Visual topic discovering, tracking and summarization from social media streams

• DOI: 10.1007/s11042-016-3877-1
• 发表时间: 2017-04
• 期刊: Multimedia Tools and Applications
• 影响因子: 3.6
• 作者: Zhao Lu;Y. Lin;Xiaoxian Huang;N. Xiong;Zhijun Fang