Foundations of statistical natural language processing
Foundations of statistical natural language processing
An Introduction to Variational Methods for Graphical Models
Machine Learning
Topic Detection and Tracking: Event-Based Information Organization
Topic Detection and Tracking: Event-Based Information Organization
Automatic generation of biased video sequences
Proceedings of the 1st ACM workshop on Story representation, mechanism and context
On the detection of semantic concepts at TRECVID
Proceedings of the 12th annual ACM international conference on Multimedia
Proceedings of the 12th annual ACM international conference on Multimedia
Tracking news stories across different sources
Proceedings of the 13th annual ACM international conference on Multimedia
Proceedings of the 13th annual ACM international conference on Multimedia
Large-Scale Concept Ontology for Multimedia
IEEE MultiMedia
How many high-level concepts will fill the semantic gap in news video retrieval?
Proceedings of the 6th ACM international conference on Image and video retrieval
Novelty detection for cross-lingual news stories with visual duplicates and speech transcripts
Proceedings of the 15th international conference on Multimedia
Introduction to Information Retrieval
Introduction to Information Retrieval
A generalized mean field algorithm for variational inference in exponential families
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
PeaceMaker: a video game to teach peace
INTETAIN'05 Proceedings of the First international conference on Intelligent Technologies for Interactive Entertainment
Identifying ideological perspectives in text and video
Identifying ideological perspectives in text and video
The third eye: mining the visual cognition across multi-language communities
Proceedings of the international conference on Multimedia
A cross-media method of stakeholder extraction for news contents analysis
WAIM'10 Proceedings of the 11th international conference on Web-age information management
Cross community news event summary generation based on collaborative ranking
Proceedings of the 4th International Conference on Internet Multimedia Computing and Service
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Television news has become the predominant way of understanding the world around us, but individual news broadcasters can frame or mislead an audience's understanding of political and social issues. We are developing a computer system that can automatically identify highly biased television news and encourage audiences to seek news stories from contrasting viewpoints. But can computers identify the ideological perspective from which a news video was produced? We propose a method based on an empathic pattern of visual concepts: news broadcasters holding contrasting ideological beliefs appear to emphasize different subsets of visual concepts. We formalize the emphatic patterns and propose a statistical model. We evaluate the proposed model on a large broadcast news video archive with promising experimental results.