Abstract: The dramatic rise in the use of Social Media (SM)
platforms such as Facebook and Twitter provide access to an
unprecedented amount of user data. Users may post reviews on
products and services they bought, write about their interests, share
ideas or give their opinions and views on political issues. There is a
growing interest in the analysis of SM data from organisations for
detecting new trends, obtaining user opinions on their products and
services or finding out about their online reputations. A recent
research trend in SM analysis is making predictions based on
sentiment analysis of SM. Often indicators of historic SM data are
represented as time series and correlated with a variety of real world
phenomena like the outcome of elections, the development of
financial indicators, box office revenue and disease outbreaks. This
paper examines the current state of research in the area of SM mining
and predictive analysis and gives an overview of the analysis
methods using opinion mining and machine learning techniques.
Abstract: This paper examines the issues, the dangers and the
saving graces of life in a transparent global community where there is
truly “no place to hide". In recent years, social networks and online
groups have transformed issues of privacy and the ways in which we
perceive and interact with others. The idea of reputation is critical to
this dynamic. The discussion begins with a brief etymological history
of the concept of reputation and moves to an exploration of how and
why online communication changes our basic nature, our various
selves and the Bakhtin idea of the polyphonic nature of truth. The
discussion considers the damaging effects of bullying and gossip,
both of which constitute an assault on reputation and the latter of
which is not limited to the lifetime of the person. It concludes with
guidelines and specific recommendations.