23,405 research outputs found
Solutions to Detect and Analyze Online Radicalization : A Survey
Online Radicalization (also called Cyber-Terrorism or Extremism or
Cyber-Racism or Cyber- Hate) is widespread and has become a major and growing
concern to the society, governments and law enforcement agencies around the
world. Research shows that various platforms on the Internet (low barrier to
publish content, allows anonymity, provides exposure to millions of users and a
potential of a very quick and widespread diffusion of message) such as YouTube
(a popular video sharing website), Twitter (an online micro-blogging service),
Facebook (a popular social networking website), online discussion forums and
blogosphere are being misused for malicious intent. Such platforms are being
used to form hate groups, racist communities, spread extremist agenda, incite
anger or violence, promote radicalization, recruit members and create virtual
organi- zations and communities. Automatic detection of online radicalization
is a technically challenging problem because of the vast amount of the data,
unstructured and noisy user-generated content, dynamically changing content and
adversary behavior. There are several solutions proposed in the literature
aiming to combat and counter cyber-hate and cyber-extremism. In this survey, we
review solutions to detect and analyze online radicalization. We review 40
papers published at 12 venues from June 2003 to November 2011. We present a
novel classification scheme to classify these papers. We analyze these
techniques, perform trend analysis, discuss limitations of existing techniques
and find out research gaps
Statistical Modelling of Information Sharing: Community, Membership and Content
File-sharing systems, like many online and traditional information sharing
communities (e.g. newsgroups, BBS, forums, interest clubs), are dynamical
systems in nature. As peers get in and out of the system, the information
content made available by the prevailing membership varies continually in
amount as well as composition, which in turn affects all peers' join/leave
decisions. As a result, the dynamics of membership and information content are
strongly coupled, suggesting interesting issues about growth, sustenance and
stability.
In this paper, we propose to study such communities with a simple statistical
model of an information sharing club. Carrying their private payloads of
information goods as potential supply to the club, peers join or leave on the
basis of whether the information they demand is currently available.
Information goods are chunked and typed, as in a file sharing system where
peers contribute different files, or a forum where messages are grouped by
topics or threads. Peers' demand and supply are then characterized by
statistical distributions over the type domain.
This model reveals interesting critical behaviour with multiple equilibria. A
sharp growth threshold is derived: the club may grow towards a sustainable
equilibrium only if the value of an order parameter is above the threshold, or
shrink to emptiness otherwise. The order parameter is composite and comprises
the peer population size, the level of their contributed supply, the club's
efficiency in information search, the spread of supply and demand over the type
domain, as well as the goodness of match between them.Comment: accepted in International Symposium on Computer Performance,
Modeling, Measurements and Evaluation, Juan-les-Pins, France, October-200
Triaging Content Severity in Online Mental Health Forums
Mental health forums are online communities where people express their issues
and seek help from moderators and other users. In such forums, there are often
posts with severe content indicating that the user is in acute distress and
there is a risk of attempted self-harm. Moderators need to respond to these
severe posts in a timely manner to prevent potential self-harm. However, the
large volume of daily posted content makes it difficult for the moderators to
locate and respond to these critical posts. We present a framework for triaging
user content into four severity categories which are defined based on
indications of self-harm ideation. Our models are based on a feature-rich
classification framework which includes lexical, psycholinguistic, contextual
and topic modeling features. Our approaches improve the state of the art in
triaging the content severity in mental health forums by large margins (up to
17% improvement over the F-1 scores). Using the proposed model, we analyze the
mental state of users and we show that overall, long-term users of the forum
demonstrate a decreased severity of risk over time. Our analysis on the
interaction of the moderators with the users further indicates that without an
automatic way to identify critical content, it is indeed challenging for the
moderators to provide timely response to the users in need.Comment: Accepted for publication in Journal of the Association for
Information Science and Technology (2017
Investigating the impact of networking capability on firm innovation performance:using the resource-action-performance framework
The author's final peer reviewed version can be found by following the URI link. The Publisher's final version can be found by following the DOI link.Purpose
The experience of successful firms has proven that one of the most important ways to promote co-learning and create successful networked innovations is the proper application of inter-organizational knowledge mechanisms. This study aims to use a resource-action-performance framework to open the black box on the relationship between networking capability and innovation performance. The research population embraces companies in the Iranian automotive industry.
Design/methodology/approach
Due to the latent nature of the variables studied, the required data are collected through a web-based cross-sectional survey. First, the content validity of the measurement tool is evaluated by experts. Then, a pre-test is conducted to assess the reliability of the measurement tool. All data are gathered by the Iranian Vehicle Manufacturers Association (IVMA) and Iranian Auto Parts Manufacturers Association (IAPMA) samples. The power analysis method and G*Power software are used to determine the sample size. Moreover, SmartPLS 3 and IBM SPSS 25 software are used for data analysis of the conceptual model and relating hypotheses.
Findings
The results of this study indicated that the relationships between networking capability, inter-organizational knowledge mechanisms and inter-organizational learning result in a self-reinforcing loop, with a marked impact on firm innovation performance.
Originality/value
Since there is little understanding of the interdependencies of networking capability, inter-organizational knowledge mechanisms, co-learning and their effect on firm innovation performance, most previous research studies have focused on only one or two of the above-mentioned variables. Thus, their cumulative effect has not examined yet. Looking at inter-organizational relationships from a network perspective and knowledge-based view (KBV), and to consider the simultaneous effect of knowledge mechanisms and learning as intermediary actions alongside, to consider the performance effect of the capability-building process, are the main advantages of this research
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