CAREER: Towards a Data-driven Understanding of Online Sentiment

这个项目通过探索理解和模拟在线情感的新方法，促进了科学知识的进步和进步。随着网络已经成为社会的关键，影响着我们的个人生活和整个世界，我们更需要了解人们是如何互动的。特别是，关于如何衡量人们对各种主题或内容类型的看法的研究，未能解决不断发展的社交网络上使用的新方式和新类型的内容。这个项目通过开发新的工具来解决这种缺乏理解的问题，以帮助使用大规模的经验数据来量化在线情绪。除了这些新工具，该项目的成果，包括数据集和代码人工制品，将帮助其他研究人员加深我们对社交媒体的理解。在这个项目中，研究人员试图实现四个研究目标。首先是创建一个多平台的社交媒体数据集。特别是，调查员将利用以前在大规模数据收集方面的经验开发一系列工具，以持续识别和收集来自新兴社交媒体平台的多媒体数据。接下来，研究人员将开发数据驱动的技术，以理解社交媒体中使用的编码语言。这些技术将不仅关注文本内容，例如俚语单词，而且还关注编码视觉语言，例如迷因。第三个目标是开发一种新的、可解释的内容情感系统。这种方法不同于现有的方法，它将分类任务视为两个内容之间的博弈，并从国际象棋和视频游戏中使用的ELO评分系统中学习内容的总排序。最后，调查员将探索用户和社区层面的在线情绪建模。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This project promotes the progress and advancement of scientific knowledge by exploring newmethods for understanding and modeling online sentiment. As the Web has become critical tosociety, impacting our personal lives as well as the world at large, a greater need to understandhow people interact. In particular, research into how to measure people’s opinion on varioustopics or types of content has failed to address the new ways and types of content used on theevolving social Web. This project addresses this lack of understanding by developing new toolsto help quantify online sentiment using large-scale, empirical data. In addition to these newtools, output from the project, including datasets and code artifacts will help other researchersadvance our understanding of social media.In this project, the investigator seeks to achieve four research objectives. The first is thecreation of a multi-platform social media dataset. In particular, the investigator will develop aseries of tools leveraging prior experience in large-scale data collection to perform continuousidentification and collection of multimedia data from emerging social media platforms. Next, theinvestigator will develop data-driven techniques to understand coded language used in socialmedia. These techniques will focus not just on textual content, e.g., slang words, but also codedvisual language, e.g., memes. The third objective is developing a new, explainable system forrating the sentiment of content. This method departs from existing approaches by treating theclassification task as a game between two pieces of content and learning a total ordering of thecontent devised from the Elo rating system used in chess and video games. Finally, theinvestigator will explore user and community level modeling of online sentiment.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.