Identifying news videos' ideological perspectives using emphatic patterns of visual concepts

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.

[1]  John R. Smith,et al.  On the detection of semantic concepts at TRECVID , 2004, MULTIMEDIA '04.

[2]  Monte J. Zerger.  Seeing is believing , 2004 .

[3]  Chong-Wah Ngo,et al.  Novelty detection for cross-lingual news stories with visual duplicates and speech transcripts , 2007, ACM Multimedia.

[4]  Hinrich Schütze,et al.  Book Reviews: Foundations of Statistical Natural Language Processing , 1999, CL.

[5]  Paul Over,et al.  TRECVID 2005 - An Overview , 2005, TRECVID.

[6]  John R. Smith,et al.  Large-scale concept ontology for multimedia , 2006, IEEE MultiMedia.

[7]  J. Davenport.  Editor , 1960 .

[8]  Michael I. Jordan,et al.  A generalized mean field algorithm for variational inference in exponential families , 2002, UAI.

[9]  E. Xing.  On Topic Evolution , 2005 .

[10]  Alexander G. Hauptmann,et al.  Towards a Large Scale Concept Ontology for Broadcast Video , 2004, CIVR.

[11]  Christopher D. Manning,et al.  Introduction to Information Retrieval , 2010, J. Assoc. Inf. Sci. Technol..

[12]  Theo van Leeuwen,et al.  Reading Images: The Grammar of Visual Design , 1996 .

[13]  Mauricio Arango,et al.  Vanishing point , 2005, MULTIMEDIA '05.

[14]  Jun Yang,et al.  CMU Informedia's TRECVID 2005 Skirmishes , 2005, TRECVID.

[15]  Rong Yan,et al.  How many high-level concepts will fill the semantic gap in news video retrieval? , 2007, CIVR '07.

[16]  Frank Nack,et al.  Automatic generation of biased video sequences , 2004, SRMC '04.

[17]  Tim Sweeney,et al.  PeaceMaker: A Video Game to Teach Peace , 2005, INTETAIN.

[18]  Steven Kull,et al.  Misperceptions, the Media, and the Iraq War , 2003 .

[19]  Shih-Fu Chang,et al.  Semantic video clustering across sources using bipartite spectral clustering , 2004, 2004 IEEE International Conference on Multimedia and Expo (ICME) (IEEE Cat. No.04TH8763).

[20]  S. Bocconi,et al.  VOX POPULI: automatic generation of biased video sequences , 2004 .

[21]  E. Efron,et al.  The news twisters , 1971 .

[22]  Alexander G. Hauptmann,et al.  LSCOM Lexicon Definitions and Annotations (Version 1.0) , 2006 .

[23]  Wei-Hao Lin,et al.  Identifying ideological perspectives in text and video , 2009 .

[24]  James Allan,et al.  Topic detection and tracking: event-based information organization , 2002 .

[25]  Mubarak Shah,et al.  Tracking news stories across different sources , 2005, MULTIMEDIA '05.

[26]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[27]  T. V. Dijk,et al.  Ideology: A Multidisciplinary Approach , 1998 .

[28]  Michael I. Jordan,et al.  An Introduction to Variational Methods for Graphical Models , 1999, Machine Learning.