PFI:AIR - TT: Market Driven Innovations and Scaling up of Twitris- A System for Collective Social Intelligence

这个PFI: AIR技术翻译项目的重点是将社交媒体监测和分析技术Twitris转化为商业应用。Twitris的集体社交媒体智能技术包括远远超出当前最先进的社交媒体监控和分析工具的功能。Twitris平台可以提供对实时社交媒体流和各种相关知识的集体利用，以显着改善决策并支持经济，人类和社会发展各个领域的及时行动。Twitris的独特功能包括从三个维度对社交媒体内容进行实时语义分析：时空-主题、人-内容-网络和情感-情感-意图。与该市场领域的领先竞争技术相比，这些功能可以提供更深入的、与环境相关的分析和可操作的见解。该项目将Twitris从研究原型转变为能够支持商业应用的规模化技术，解决了几个技术差距。因此，我们将在三个方面进行研究和技术改进：1)通过广泛的特定位置处理来增强Twitris的功能，这需要解决Twitter上空间元数据稀缺的挑战；2)语义增强过滤和改善推文自动和半自动过滤的用户体验，这需要解决内容模糊和信息过载等挑战；3)支持特定领域的可扩展架构，支持知识模块来处理大容量；数据的多样性和速度。此外，该项目还将为学生和应届毕业生提供一个独特的教育和培训平台，帮助他们为创业、商业和经济发展以及初创企业的职业生涯做好准备。具体而言，该项目(a)将基础研究与技术开发和知识产权开发联系起来，从而实现成功的商业化；(b)与成功的企业家、商业伙伴和客户密切合作。它还将进行有组织的教育活动，包括五门技术和商业课程，同时继续促进高科技领域和计算机科学急需的多样性。该项目与战略上重要的市场中的几个商业伙伴合作，开展涉及其客户的试验，以努力评估研究和技术改进的功效和效益。

This PFI: AIR Technology Translation project focuses on translating a technology for social media monitoring and analysis, known as Twitris, to commercial application. Twitris' collective social media intelligence technology includes capabilities well beyond current state-of-the-art social media monitoring and analysis tools. The Twitris platform can provide collective exploitation of real-time social media streams, and a variety of relevant knowledge, to significantly improve decision-making and support timely actions in various domains of economic, human, and social development. Twitris' unique features include real-time semantic analysis of social media content along three dimensions: spatio-temporal-thematic, people-content-network, and sentiment-emotion-intent. These features result in deeper, contextually-relevant analysis and actionable insights when compared to the leading competing technology in this market space. This project addresses several technology gaps as it transitions Twitris from a research prototype to a scaled-up technology capable of supporting commercial applications. Consequently, three areas of research and technology enhancement will be conducted: 1) enhancing the functionalities of Twitris with a broad range of location-specific processing that requires addressing the challenge of scarcity of spatial metadata on Twitter, 2) semantics-enhanced filtering and improved user experience for automatic and semi-automatic filtering of tweets, which requires addressing challenges such as content ambiguity and information overload, and 3) scalable architecture supporting domain-specific, knowledge-enabled modules to handle high volume, variety and velocity of data. In addition, the project will also provide a unique education and training platform for students and recent graduates to prepare them for careers involving entrepreneurship, business and economic development, and careers in startups. Specifically, the project (a) bridges basic research with technology development and intellectual property development that can lead to successful commercialization and (b) involves close collaboration with successful entrepreneurs, business partners, and customers. It will also undertake structured educational activities involving five technical and business courses, while continuing to foster much-needed diversity in high-tech fields and computer science. This project engages several business partners in strategically important markets to carry out trials involving their customers in an effort to evaluate the efficacy and benefits of research and technology enhancements involved in this scale-up.