49,660 research outputs found
Multimedia big data computing for in-depth event analysis
While the most part of ”big data” systems target text-based analytics, multimedia data, which makes up about 2/3 of internet traffic, provide unprecedented opportunities for understanding and responding to real world situations and
challenges. Multimedia Big Data Computing is the new topic
that focus on all aspects of distributed computing systems that
enable massive scale image and video analytics. During the
course of this paper we describe BPEM (Big Picture Event
Monitor), a Multimedia Big Data Computing framework that
operates over streams of digital photos generated by online
communities, and enables monitoring the relationship between
real world events and social media user reaction in real-time.
As a case example, the paper examines publicly available social media data that relate to the Mobile World Congress 2014 that has been harvested and analyzed using the described system.Peer ReviewedPostprint (author's final draft
SUPER: Towards the Use of Social Sensors for Security Assessments and Proactive Management of Emergencies
Social media statistics during recent disasters (e.g. the 20 million tweets relating to 'Sandy' storm and the sharing of related photos in Instagram at a rate of 10/sec) suggest that the understanding and management of real-world events by civil protection and law enforcement agencies could benefit from the effective blending of social media information into their resilience processes. In this paper, we argue that despite the widespread use of social media in various domains (e.g. marketing/branding/finance), there is still no easy, standardized and effective way to leverage different social media streams -- also referred to as social sensors -- in security/emergency management applications. We also describe the EU FP7 project SUPER (Social sensors for secUrity assessments and Proactive EmeRgencies management), started in 2014, which aims to tackle this technology gap
Social Fingerprinting: detection of spambot groups through DNA-inspired behavioral modeling
Spambot detection in online social networks is a long-lasting challenge
involving the study and design of detection techniques capable of efficiently
identifying ever-evolving spammers. Recently, a new wave of social spambots has
emerged, with advanced human-like characteristics that allow them to go
undetected even by current state-of-the-art algorithms. In this paper, we show
that efficient spambots detection can be achieved via an in-depth analysis of
their collective behaviors exploiting the digital DNA technique for modeling
the behaviors of social network users. Inspired by its biological counterpart,
in the digital DNA representation the behavioral lifetime of a digital account
is encoded in a sequence of characters. Then, we define a similarity measure
for such digital DNA sequences. We build upon digital DNA and the similarity
between groups of users to characterize both genuine accounts and spambots.
Leveraging such characterization, we design the Social Fingerprinting
technique, which is able to discriminate among spambots and genuine accounts in
both a supervised and an unsupervised fashion. We finally evaluate the
effectiveness of Social Fingerprinting and we compare it with three
state-of-the-art detection algorithms. Among the peculiarities of our approach
is the possibility to apply off-the-shelf DNA analysis techniques to study
online users behaviors and to efficiently rely on a limited number of
lightweight account characteristics
Daily Stress Recognition from Mobile Phone Data, Weather Conditions and Individual Traits
Research has proven that stress reduces quality of life and causes many
diseases. For this reason, several researchers devised stress detection systems
based on physiological parameters. However, these systems require that
obtrusive sensors are continuously carried by the user. In our paper, we
propose an alternative approach providing evidence that daily stress can be
reliably recognized based on behavioral metrics, derived from the user's mobile
phone activity and from additional indicators, such as the weather conditions
(data pertaining to transitory properties of the environment) and the
personality traits (data concerning permanent dispositions of individuals). Our
multifactorial statistical model, which is person-independent, obtains the
accuracy score of 72.28% for a 2-class daily stress recognition problem. The
model is efficient to implement for most of multimedia applications due to
highly reduced low-dimensional feature space (32d). Moreover, we identify and
discuss the indicators which have strong predictive power.Comment: ACM Multimedia 2014, November 3-7, 2014, Orlando, Florida, US
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