1. INFORMATION EXTRACTION BY SOCIAL MEDIA ANALYSIS Giuseppe Amato – ISTI-CNR (MIR-NeMIS)

3. What information can be extracted?  Very high value in multimedia content  Security Identify risks or provide information  Quasi real-time news Get information from people witnessing an event in real time  Marketing What are the new emerging trends?  Entertainment People spend a lot of time with on-line media. What are they looking for?  Human behaviour analysis What do people do in this moment.

4. Boston bombing Rome – Feyenoord FC supporter riots Security / quasi real-time news

5. Market trends

6. -Entertainment -Behaviour -Events

7. Challenges  Effectiveness  Extract information relevant pertinent useful  Scalability  Keep track and handle a continuous flow of media Billion images and videos

8. Our Skill  Multimedia information retrieval on very large scale  Prototypes using 100 millions images  Similarity search on very large scale  Searching by similarity on faces, images, objects, audios, etc.  Real time image matching on very limited resources  Prototypes recognizing in real-time around 1000 images on mobile phones  Image content classification/recognition

9. Research group  Permanent  Giuseppe Amato  Fausto Rabitti  Pasquale Savino  Claudio Gennaro  Fabrizio Falchi  Franca Debole  Temporary  Costantino Thanos (AS)  Fraco A. Cardillo (PD)  Claudio Vairo (PD)  Paolo Bolettieri (TS)

10. Fundings and Projects  National 210k€ (120k€ soBigData related) /year  Inter. 1380k€ (190€ soBigData related) /year  On going soBigData related projects:  FP7 STREP - RUBICON (2011-2014)  CIP-ICT-PSP - EAGLE (2013-2016)  PON – DiCET (2013-2014)  POR CREO - SECURE (2013-2014),

11. Research aim  Study and develop techniques to  Analyze  Aggregate  Annotate  Make searchable user generated multimedia content available on social networks