Slides Amato

INFORMATION EXTRACTION
BY SOCIAL MEDIA ANALYSIS
Giuseppe Amato – ISTI-CNR (MIR-NeMIS)
What information can be extracted?

Very high value in multimedia content

Security


Quasi real-time news


What are the new emerging trends?
Entertainment


Get information from people witnessing an event in real time
Marketing


Identify risks or provide information
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.
Boston bombing
Rome – Feyenoord FC
supporter riots
Security / quasi real-time news
Market trends
-Entertainment
-Behaviour
-Events
Challenges

Effectiveness
 Extract
information
 relevant
 pertinent
 useful

Scalability
 Keep
track and handle a continuous flow of media
 Billion
images and videos
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
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)
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),
Research aim

Study and develop techniques to
 Analyze
 Aggregate
 Annotate
 Make
searchable
user generated multimedia content available on
social networks