I-Corps: Probabilistically Detecting Controversy

这个I-Corps项目的更广泛的影响/商业潜力是利用过去的研究来检测争议并将这项技术带入商业环境。该项目通过使用复杂的机器学习和信息检索技术来解决有争议的话题，为对证券交易感兴趣的组织提供可操作的见解。该项目最初将探索金融服务领域的市场，这些市场从获取的数据流中为客户带来部分价值。除了金融行业，该技术还应用于声誉和危机管理，以及通过对个人用户的争议进行开放分析和解释力来产生社会影响的范围。该项目有可能帮助用户更好地了解情况，更有能力批判性地评估往往压倒性的内容流。这个I-Corps项目技术依赖于复杂的机器学习和信息检索技术，以自动检测有争议的主题。NSF支持的研究通过将网页映射到算法识别的有争议的主题来识别争议。拟议的商业化努力是独一无二的，因为它解决了争议和危机情况。该项目的假设是，鉴于金融、股票交易行业的收入模式和成本结构，这项技术可以成功地商业化。该项目将使用精益创业方法进行客户访谈，并探索可扩展的商业模式的可行性。

The broader impact/commercial potential of this I-Corps project is in leveraging past research on detecting controversy and bringing this technology to a commercial setting. The project provides actionable insights to organizations interested in securities trading, through the usage of sophisticated machine learning and information retrieval techniques for controversial topics. The project will initially explore markets in the financial services sector that derive some portion of the value for their clients from acquired data streams. Besides the financial industry, there are applications of the technology in reputation and crisis management, as well as scope for social impact by opening analysis and explanatory power of controversies to individual users. The project has the potential to help users become better informed and more capable of critically evaluating the often-overwhelming stream of content.This I-Corps project technology relies on sophisticated machine learning and information retrieval techniques in order to automatically detect controversial topics. The enabling NSF-supported research recognized controversy by mapping a webpage to algorithmically-identified controversial topics. The proposed commercialization effort is unique because it addresses controversy and crisis situations. The project's hypothesis is that this technology can be successfully commercialized to the financial, equity-trading industry given their revenue model and cost structure. The project will use the lean startup method to perform customer interviews and explore the viability of a scalable business model.