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The subtitling demand of multimedia content has grown quickly over the last years, especially after the adoption of the new European audiovisual legislation, which forces to make multimedia content accessible to all. As a result, TV channels have been moved to produce subtitles for a high percentage of their broadcast content. Consequently, the market has been seeking subtitling alternatives more productive than the traditional manual process. The large effort dedicated by the research community to the development of Large Vocabulary Continuous Speech Recognition (LVCSR) over the last decade has resulted in significant improvements on multimedia transcription, becoming the most powerful technology for automatic intralingual subtitling. This article contains a detailed description of the live and batch automatic subtitling applications developed by the SAVAS consortium for several European languages based on proprietary LVCSR technology specifically tailored to the subtitling needs, together with results of their quality evaluation.

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Organised crime uses information technology systems to communicate, work or expand its influence. The EU FP7 Security Research Project CAPER (Collaborative information, Acquisition, Processing, Exploitation and Reporting for the prevention of organised crime), created in cooperation with European Law Enforcement Agencies (LEAs), aims to build a common collaborative and information sharing platform for the detection and prevention of organised crime, which exploits Open Source Intelligence (OSINT). LEAs are becoming more inclined to using OSINT tools, and particularly tools able to manage Online Social Networks (OSNs) data. This paper presents the CAPER Facebook crawling and analysis subsystem. Heuristic algorithms have been implemented in order to extract specific properties of Facebook's social graph, in particular user interactions. To support analysis tasks specifically, extensive effort has been spent on the analysis of textual user generated content and on the recognition of named-entities, in particular person names, locations and organisations. Relationships between users and entities mentioned in posts and in related comments are created and merged into the users networks extracted from the social graph. All entity relationships are finally visualised in userfriendly network graphs.