19,219 research outputs found
DNA-inspired online behavioral modeling and its application to spambot detection
We propose a strikingly novel, simple, and effective approach to model online
user behavior: we extract and analyze digital DNA sequences from user online
actions and we use Twitter as a benchmark to test our proposal. We obtain an
incisive and compact DNA-inspired characterization of user actions. Then, we
apply standard DNA analysis techniques to discriminate between genuine and
spambot accounts on Twitter. An experimental campaign supports our proposal,
showing its effectiveness and viability. To the best of our knowledge, we are
the first ones to identify and adapt DNA-inspired techniques to online user
behavioral modeling. While Twitter spambot detection is a specific use case on
a specific social media, our proposed methodology is platform and technology
agnostic, hence paving the way for diverse behavioral characterization tasks
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
A Framework for Discovery and Diagnosis of Behavioral Transitions in Event-streams
Date stream mining techniques can be used in tracking user behaviors as they attempt to achieve their goals. Quality metrics over stream-mined models identify potential changes in user goal attainment. When the quality of some data mined models varies significantly from nearby models—as defined by quality metrics—then the user’s behavior is automatically flagged as a potentially significant behavioral change. Decision tree, sequence pattern and Hidden Markov modeling being used in this study. These three types of modeling can expose different aspect of user’s behavior. In case of decision tree modeling, the specific changes in user behavior can automatically characterized by differencing the data-mined decision-tree models. The sequence pattern modeling can shed light on how the user changes his sequence of actions and Hidden Markov modeling can identifies the learning transition points. This research describes how model-quality monitoring and these three types of modeling as a generic framework can aid recognition and diagnoses of behavioral changes in a case study of cognitive rehabilitation via emailing. The date stream mining techniques mentioned are used to monitor patient goals as part of a clinical plan to aid cognitive rehabilitation. In this context, real time data mining aids clinicians in tracking user behaviors as they attempt to achieve their goals. This generic framework can be widely applicable to other real-time data-intensive analysis problems. In order to illustrate this fact, the similar Hidden Markov modeling is being used for analyzing the transactional behavior of a telecommunication company for fraud detection. Fraud similarly can be considered as a potentially significant transaction behavioral change
Quantitative and functional post-translational modification proteomics reveals that TREPH1 plays a role in plant thigmomorphogenesis
Plants can sense both intracellular and extracellular mechanical forces and
can respond through morphological changes. The signaling components responsible
for mechanotransduction of the touch response are largely unknown. Here, we
performed a high-throughput SILIA (stable isotope labeling in
Arabidopsis)-based quantitative phosphoproteomics analysis to profile changes
in protein phosphorylation resulting from 40 seconds of force stimulation in
Arabidopsis thaliana. Of the 24 touch-responsive phosphopeptides identified,
many were derived from kinases, phosphatases, cytoskeleton proteins, membrane
proteins and ion transporters. TOUCH-REGULATED PHOSPHOPROTEIN1 (TREPH1) and MAP
KINASE KINASE 2 (MKK2) and/or MKK1 became rapidly phosphorylated in
touch-stimulated plants. Both TREPH1 and MKK2 are required for touch-induced
delayed flowering, a major component of thigmomorphogenesis. The treph1-1 and
mkk2 mutants also exhibited defects in touch-inducible gene expression. A
non-phosphorylatable site-specific isoform of TREPH1 (S625A) failed to restore
touch-induced flowering delay of treph1-1, indicating the necessity of S625 for
TREPH1 function and providing evidence consistent with the possible functional
relevance of the touch-regulated TREPH1 phosphorylation. Bioinformatic analysis
and biochemical subcellular fractionation of TREPH1 protein indicate that it is
a soluble protein. Altogether, these findings identify new protein players in
Arabidopsis thigmomorphogenesis regulation, suggesting that protein
phosphorylation may play a critical role in plant force responses
Highlights from the eleventh ISCB Student Council Symposium 2015
This report summarizes the scientific content and activities of the annual symposium organized by the Student Council of the International Society for Computational Biology (ISCB), held in conjunction with the Intelligent Systems for Molecular Biology (ISMB) / European Conference on Computational Biology (ECCB) conference in Dublin, Ireland on July 10, 2015
- …