@inproceedings{song2016watching,
author = {Song, Emily and Ellis, Joseph G and Li, Hongzhi and Chang, Shih-Fu},
title = {Watching What and How Politicians Discuss Various Topics: A Large-Scale Video Analytics UI},
booktitle = {Proceedings of the 2016 ACM on International Conference on Multimedia Retrieval},
year = {2016},
month = {June},
abstract = {Accurately gauging the political atmosphere is especially difficult in this day and age, as individuals have access to a constantly growing collection of written and audiovisual news sources. This is especially true with regards to the U.S. presidential election, as there are numerous candidates, countless stories, and opinion articles discussing the merits of each particular candidate. It is therefore challenging for people to make an accurate assessment of what each candidate represents and how they would act if they were elected into office. To address this problem, we present a large-scale dataset comprised of videos of politicians speaking organized by the topics they are speaking about, and a user interface for exploring this interesting dataset. Our interface links people and events to relevant pieces of audiovisual media, and presents the desired information in a meaningful and intuitive manner. Our approach is unique by direct linking to actual speaking by politicians about specific topics, rather than links to textual quotes only. We describe the larger underlying infrastructure, a novel automated system that crawls thousands of internet news sources and 100 television news channels daily, and automatically discovers entities and indexes the content into events and topics. We examine how our user interface provides helpful and unique insights to its users, and give an example of the type of large scale trend analysis that can be performed with our system.},
publisher = {ACM},
url = {http://approjects.co.za/?big=en-us/research/publication/watching-politicians-discuss-various-topics-large-scale-video-analytics-ui/},
pages = {401-404},
}