TALK TITLE: "Data-driven Approaches for Detection of Antisocial
Behavior"DESCRIPTION: Fake news, fake reviews, hating or trolling
are the most prominent examples of online antisocial behavior which is
currently present across the whole web. Growing negative consequences
of such negative behavior have recently elicited many efforts, which
are aimed at its characterization, detection and mitigation.
At first in our talk, we will shortly describe how the
state-of-the-art data-driven techniques (especially AI, ML, data
mining, NLP) can be applied in fighting antisocial behavior. We will
highlight the most challenging open problems, such as a lack of
suitable rich datasets or useful end-user services as well as bad
interoperability of detection methods.
Secondly, we will introduce our solution how to overcome the stated
open problems. We are working on a novel, universal and easily
extensible platform named Monant. Our platform provides features for
web monitoring, interfaces for AI classification and detection methods
as well as for end-ser services (e.g. how to train users to detect
fake news).
In more details, we will discuss its architecture and current state of
implementations using many Python libraries for web crawling/parsing,
scheduling extractions, API communication between main platform
modules, etc.
SPEAKER/BIO: Ivan Srba, Róbert Móro, Veronika Žatková
Ivan and Róbert are researchers from FIIT STU in BRATISLAVA LEADING
TWO RESEARCH PROJECTS AIMED AT ONLINE ANTISOCIAL BEHAVIOR DETECTION.
Veronika is a member of student team which worked on development of
platform Monant.
LINKEDIN: https://sk.linkedin.com/in/ivansrba,
[https://sk.linkedin.com/in/ivansrba,]
https://sk.linkedin.com/in/robom, [https://sk.linkedin.com/in/robom,]
https://sk.linkedin.com/in/veronika-%C5%BEatkov%C3%A1
[https://sk.linkedin.com/in/veronika-%C5%BEatkov%C3%A1]
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Language of the event: Python, English
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23/05/2019 Last update