EAGER: Tagging and Browsing Videos According to the Preferences of Differing Affinity Groups

这个探索性的项目研究了不同的人群如何倾向于以不同的方式创建和描述(标记)同一共同事件的视频。由于这些亲和力群体中的每一个都有特定的背景、语言、兴趣和地点，他们也会以不同的方式标记和浏览这些视频。例如，中国的新闻媒体对国际卫生危机的描述与美国的截然不同。中国的报道往往包括历史片段、关于国家的讨论，对个别参与者的关注较少。相比之下，美国的报道往往更具时代性，主要使用国家来确定个人的背景，并倾向于突出全名人员的行动和反应。这个项目的目的是开发一种新的方法，使不同群体的视频更容易获得和理解，方法是开发一种浏览器，以图形化地说明这些视频之间的视觉差异，并将一组的首选标记转换为另一组的首选标记。由此产生的新浏览器将使各种各样的视频，特别是那些不同语言的视频，更容易查找、扫描和比较。这个探索性项目有三个主要组成部分：(1)开发一个新的、可共享的跨组显著差异目录。要做到这一点，它将首先将视觉和文本特征从关于单个主题的多个视频映射到一个联合潜在空间。然后，通过使用可靠的共享视觉线索，它将使用规范相关分析的变体来确定标签关系，使用G2等衡量标准对它们进行排序，并针对这两个组的成员验证它们。(2)探索将这些非线性方法扩展到多对具有更细微差异的亲缘群体的视频，例如比较美国和加拿大。特别是，它将使用PageRank算法的一个新变体来跟踪视觉模因在不同群体中的影响和持久性，并通过基本事实来验证这一点。(3)实现和评估一个原型浏览器，该浏览器在并行的时间线上静态和动态地显示不同群体的观点，随着他们随着时间的推移而演变。该项目将衡量常见的浏览器属性(时间、准确性、满意度、易用性)，但也将衡量一些不寻常的和探索性的属性(检索的适当性、标签翻译的准确性、用户参与度的增加、对记者的影响)。该奖项反映了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 exploratory project studies how different groups of people tend to create and describe (tag) videos of the same common event differently. Since each of these affinity groups share particular backgrounds, languages, interests, and locations, they also tag and browse these videos differently as well. For example, an international health crisis is portrayed rather differently by the news media of China compared to that of the U.S. Chinese coverage tends to include historical clips, talks about countries, and has less focus on individual participants. In contrast, U.S. coverage tends to be more contemporary, uses countries mainly to identify the background of individuals, and prefers to highlight the actions and reactions of people who are fully named. The aim of this project is to develop new ways of making the videos of other groups to be more accessible and more understandable to different groups by developing a browser that graphically illustrates the visual differences across such videos, and that translates the preferred tags of one group into the preferred tags of the other. The resulting new browser would make a broad range of videos, especially those in a different language, easier to find, scan, and compare.This exploratory project has three major components: (1) Development of a novel, shareable catalog of statistically significant cross-group differences. To do this, it will first map visual and textual features, from many videos about a single topic, into a joint latent space. Then, by using reliable shared visual cues, it will determine tag relationships using variants of canonical correlation analysis, sort them using measures such as G2, and validate them against users who are members of both groups. (2) Exploration of how well these non-linear methods can be extended to the videos of multiple pairs of affinity groups with more subtle differences, such as comparing the U.S. to Canada. In particular, it will use a novel variant of the PageRank algorithm to track the influence and persistence of visual memes across groups, validating this by ground truth. (3) Implementing and evaluating a prototype browser that visualizes on parallel timelines the perspectives of different groups, both statically and dynamically as they evolve over time. The project will measure the usual browser attributes (time, accuracy, satisfaction, ease of use), but also some unusual and exploratory ones (appropriateness of retrieval, accuracy of tag translations, increases in user engagement, impact on journalists).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.