 digital methodologies for global media research Randy Kluver Dept of Communication Texas A&M University

speech, language and digital media: History BBN developed one of the first continuous speech understanding systems. First software-only, real-time, large-vocabulary, speaker independent continuous speech recognition. First demonstration of 20,000-word real time speech recognizer. HARK™ Recognizer product introduced. Pioneered statistical language understanding and data extraction. First use of statistical modeling for speaker identification. Introduced Byblos recognition system with context-dependent phonetic units. Audio Mining system combines speech and language processing to index broadcast news. Development of multi-lingual optical character recognition (OCR). Real-time monitoring of foreign broadcast news with retrieval, alert, and playback capabilities.. Call center automation launched. First demonstration of speech transmission over ARPANET using 2400 bit/s LPC speech coding. DARPA GALE contract awarded in 2005 focusing on machine translation First Demonstration of end-to-end GALE Distillation, DARPA MADCAT, TSWG CALL TSWG GMT, DARPA BOLT, Operational deployment of Media Monitoring and TransTalk ©Raytheon BBN Technologies Corp., Proprietary Information. All Rights Reserved.

Multi-Media Monitoring System (M3S) at Texas A&M  Satellite Television: stream, automatically transcribe and translate foreign language broadcasts  24/7 video stream with machine-generated transcript and translation  Data then remains available within the system for later research  Web sites  Targeted websites in languages of interest  Broad-based identification of websites that provide solid regional perspective  Social media  Based on selected social media actors, and captures all tweets, retweets, and mentions  Currently support Twitter and Facebook (Arabic only)  Russian under development (mid-2016) 3

advantages to this system  Provides bigger data approach to global media studies, by harvesting large amounts of content  Allows English-language access to content that is inaccessible to users without target language skills.  Allows keyword searchable/Boolean searches on content in either english or target language over entirety of dataset  Archives material in an unaltered/unedited format for multiple types of analysis  Creates exportable clips and/or rich media content for incorporation into analysis, reports, or other media 4

broadcast translation system 5

story view

pivot view of stories 7

network analysis of twitter influence 8

developing research agendas  Globalize traditional research agendas  Multi-modal (broadcast/web/social media)  Multi-lingual/regional  Framing, content analysis, cross-platform agenda setting  Regional, comparative  Broadens the scope dramatically of available data  Allow studies that normally cannot be studied across cultural/linguistic boundaries  Potential for new methods  Sharable, marked-up databases  Big data approaches 9

where we are going:  Developing M3S as an academic/pedagogical resource  Developing collaborative research projects with key researchers/institutions  Developing research templates for the archive  Creating datasets of key global media content for use by multiple scholars  Eg, Arab Spring Archive  Developing a corpus of research to emerge from the systems to raise the visibility  Technical Refinement  Developing mechanism for incorporating digital object architecture into the systems  Developing meta-tagging capacity to current content  Improving usability of the system 10

partners  Partners  TSWG (Technical Support Working Group- Department of Defense)  BBN Raytheon Technologies  CNRI-Center for National Research Initiatives  For more info:  mms.tamu.edu, gnma.tamu.edu  11