Detection and fine-grained classification of cyberbullying events

In the current era of online interactions, both positive and negative experiences are abundant on the Web. As in real life, negative experiences can have a serious impact on youngsters. Recent studies have reported cybervictimization rates among teenagers that vary between 20% and 40%. In this paper, we focus on cyberbullying as a particular form of cybervictimization and explore its automatic detection and fine-grained classification. Data containing cyberbullying was collected from the social networking site Ask.fm. We developed and applied a new scheme for cyberbullying annotation, which describes the presence and severity of cyberbullying, a post author's role (harasser, victim or bystander) and a number of fine-grained categories related to cyberbullying, such as insults and threats. We present experimental results on the automatic detection of cyberbullying and explore the feasibility of detecting the more fine-grained cyberbullying categories in online posts. For the first task, an F-score of 55.39% is obtained. We observe that the detection of the fine-grained categories (e.g. threats) is more challenging, presumably due to data sparsity, and because they are often expressed in a subtle and implicit way

Automatic detection and prevention of cyberbullying

The recent development of social media poses new challenges to the research community in analyzing online interactions between people. Social networking sites offer great opportunities for connecting with others, but also increase the vulnerability of young people to undesirable phenomena, such as cybervictimization. Recent research reports that on average, 20% to 40% of all teenagers have been victimized online. In this paper, we focus on cyberbullying as a particular form of cybervictimization. Successful prevention depends on the adequate detection of potentially harmful messages. However, given the massive information overload on the Web, there is a need for intelligent systems to identify potential risks automatically. We present the construction and annotation of a corpus of Dutch social media posts annotated with fine-grained cyberbullying-related text categories, such as insults and threats. Also, the specific participants (harasser, victim or bystander) in a cyberbullying conversation are identified to enhance the analysis of human interactions involving cyberbullying. Apart from describing our dataset construction and annotation, we present proof-of-concept experiments on the automatic identification of cyberbullying events and fine-grained cyberbullying categories

Putting multidisciplinarity (back) on the map

The dominant theory of cross-disciplinarity represents multidisciplinarity as 'lower' or 'less interesting' than interdisciplinarity. In this paper, it is argued that this unfavorable representation of multidisciplinarity is ungrounded because it is an effect of the theory being incomplete. It is also explained that the unfavorable, ungrounded representation of multidisciplinarity is problematic: when someone adopts the dominant theory of cross-disciplinarity, the unfavorable representation supports the development of a preference for interdisciplinarity over multidisciplinarity. However, being ungrounded, the support the representation provides for a preference for interdisciplinarity, is invalid. The issue is even more pressing because research policy makers and funding bodies are among the adopters of the theory, which means that there is a risk of (funding) policies reflecting an unjustified preference for interdisciplinarity over multidisciplinarity. This paper presents an improved version of the dominant theory of cross-disciplinarity, obtained by completing the original version with the information it was missing. Because the improved version is more neutral regarding the value of different types of cross-disciplinarity, it is better suited for use by research policy makers and funding bodies

Mechanism discovery and design explanation : where role function meets biological advantage function

In the recent literature on explanation in biology, increasing attention is being paid to the connection between design explanation and mechanistic explanation, viz. the role of design principles and heuristics for mechanism discovery and mechanistic explanation. In this paper we extend the connection between design explanation and mechanism discovery by prizing apart two different types of design explanation and by elaborating novel heuristics that one specific type offers for mechanism discovery across species. We illustrate our claims in terms of two lines of biological research on the biological advantages of organismal traits, one on the eye-size of giant squid, the other on foraging habits of specific bat species. We argue that this research illustrates useful heuristics for mechanism discovery across species, viz. reasoning strategies to infer likely mechanisms for a certain biological role based on assessments of the environmental conditions in which the role is performed efficiently (i.e., offers a biological advantage) and less or in-efficiently. We bring out the novel features of our analysis in terms of a comparison with mechanistic approaches to mechanism discovery, amongst which graph-theoretical ones, and by comparing the different types of design explanation and the discovery heuristics they support