7,207 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
Using Learning Analytics to Devise Interactive Personalised Nudges for Active Video Watching
Videos can be a powerful medium for acquiring soft skills, where learning requires contextualisation in personal experience and ability to see different perspectives. However, to learn effectively while watching videos, students need to actively engage with video content. We implemented interactive notetaking during video watching in an active video watching system (AVW) as a means to encourage engagement. This paper proposes a systematic approach to utilise learning analytics for the introduction of adaptive intervention - a choice architecture for personalised nudges in the AVW to extend learning. A user study was conducted and used as an illustration. By characterising clusters derived from user profiles, we identify different styles of engagement, such as parochial learning, habitual video watching, and self-regulated learning (which is the target ideal behaviour). To find opportunities for interventions, interaction traces in the AVW were used to identify video intervals with high user interest and relevant behaviour patterns that indicate when nudges may be triggered. A prediction model was developed to identify comments that are likely to have high social value, and can be used as examples in nudges. A framework for interactive personalised nudges was then conceptualised for the case study
What, how and why Web 2.0?
This article focuses on introducing Web 2.0 technologies and possible uses for student and teacher learning and collaboration. Many of these tools are already used in social and business contexts. These new and emerging applications are also gaining popularity in classrooms across all education levels. Various applications are introduced to raise awareness and encourage educators to explore these new avenues for teaching and learning.<br /
A treatise on Web 2.0 with a case study from the financial markets
There has been much hype in vocational and academic circles surrounding the emergence of
web 2.0 or social media; however, relatively little work was dedicated to substantiating the
actual concept of web 2.0. Many have dismissed it as not deserving of this new title, since the
term web 2.0 assumes a certain interpretation of web history, including enough progress in
certain direction to trigger a succession [i.e. web 1.0 → web 2.0]. Others provided arguments in
support of this development, and there has been a considerable amount of enthusiasm in the
literature. Much research has been busy evaluating current use of web 2.0, and analysis of the
user generated content, but an objective and thorough assessment of what web 2.0 really stands
for has been to a large extent overlooked. More recently the idea of collective intelligence
facilitated via web 2.0, and its potential applications have raised interest with researchers, yet a
more unified approach and work in the area of collective intelligence is needed.
This thesis identifies and critically evaluates a wider context for the web 2.0 environment, and
what caused it to emerge; providing a rich literature review on the topic, a review of existing
taxonomies, a quantitative and qualitative evaluation of the concept itself, an investigation of
the collective intelligence potential that emerges from application usage. Finally, a framework
for harnessing collective intelligence in a more systematic manner is proposed.
In addition to the presented results, novel methodologies are also introduced throughout this
work. In order to provide interesting insight but also to illustrate analysis, a case study of the
recent financial crisis is considered. Some interesting results relating to the crisis are revealed
within user generated content data, and relevant issues are discussed where appropriate
Characterizing Popularity Dynamics of User-generated Videos: A Category-based Study of YouTube
Understanding the growth pattern of content popularity has become a subject of immense interest to
Internet service providers, content makers and on-line advertisers. This understanding is also important for
the sustainable development of content distribution systems. As an approach to comprehend the characteristics of this growth pattern, a significant amount of research has been done in analyzing the popularity
growth patterns of YouTube videos. Unfortunately, no work has been done that intensively investigates the
popularity patterns of YouTube videos based on video object category. In this thesis, an in-depth analysis
of the popularity pattern of YouTube videos is performed, considering the categories of videos.
Metadata and request patterns were collected by employing category-specific YouTube crawlers. The
request patterns were observed for a period of five months. Results confirm that the time varying popularity
of di fferent YouTube categories are conspicuously diff erent, in spite of having sets of categories with very
similar viewing patterns. In particular, News and Sports exhibit similar growth curves, as do Music and
Film.
While for some categories views at early ages can be used to predict future popularity, for some others
predicting future popularity is a challenging task and require more sophisticated techniques, e.g., time-series clustering. The outcomes of these analyses are instrumental towards designing a reliable workload generator, which can be further used to evaluate diff erent caching policies for YouTube and similar sites. In this
thesis, workload generators for four of the YouTube categories are developed. Performance of these workload generators suggest that a complete category-specific workload generator can be developed using time-series clustering. Patterns of users' interaction with YouTube videos are also analyzed from a dataset collected in a local network. This shows the possible ways of improving the performance of Peer-to-Peer video distribution
technique along with a new video recommendation method
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