Profiling social, emotional and behavioural difficulties of children involved in direct and indirect bullying behaviours

Being involved in bullying places a child at risk of poor psychosocial and educational outcomes. This study aimed to examine the profile of behavioural, emotional and social functioning for two subtypes of bullying; direct and indirect (relational). Pupils aged between seven and eleven years old completed sociometric measures of social inclusion and bullying behaviour to identify 192 pupils considered to be involved in either direct, indirect, both or neither types of bullying. These pupils and their teachers completed a battery of assessments relating to behaviour, social competence and self-perception. All bully-groups experienced similar levels of significant social rejection. ‘Direct’ and ‘both’ groups showed the greatest number of behavioural, emotional and social difficulties, while the ‘indirect’ group showed weaknesses in self-perception, but no teacher-rated problems. Understanding the behavioural, emotional and social correlates of bullying is of particular importance for early identification of children at risk of becoming bullies and for developing targeted interventions

Machine Learning in Automated Text Categorization

The automated categorization (or classification) of texts into predefined categories has witnessed a booming interest in the last ten years, due to the increased availability of documents in digital form and the ensuing need to organize them. In the research community the dominant approach to this problem is based on machine learning techniques: a general inductive process automatically builds a classifier by learning, from a set of preclassified documents, the characteristics of the categories. The advantages of this approach over the knowledge engineering approach (consisting in the manual definition of a classifier by domain experts) are a very good effectiveness, considerable savings in terms of expert manpower, and straightforward portability to different domains. This survey discusses the main approaches to text categorization that fall within the machine learning paradigm. We will discuss in detail issues pertaining to three different problems, namely document representation, classifier construction, and classifier evaluation.Comment: Accepted for publication on ACM Computing Survey