Stylometric text analysis for Dutch-speaking adolescents with autism spectrum disorder

One of the main characteristics of individuals with autism spectrum disorder (ASD) is a deficit in social communication. The effects of ASD on both verbal and non-verbal communication are widely researched in this respect. In this exploratory study, we investigate whether texts of Dutchspeaking adolescents with ASD (aged 12-18 years) are (automatically) distinguishable from texts written by typically developing peers. First, we want to reveal whether specific characteristics can be found in the writing style of adolescents with ASD, and secondly, we examine the possibility to use these features in an automated classification task. We look for surface features (word and character n-grams, and simple linguistic metrics), but also for deep linguistic features (namely syntactic, semantic and discourse features). The differences between the ASD group and control group are tested for statistical significance and we show that mainly syntactic features are different among the groups, possibly indicating a less dynamic writing style for adolescents with ASD. For the classification task, a Logistic Regression classifier is used. With a surface feature approach, we could reach an F-score of 72.15%, which is much higher than the random baseline of 50%. However, a pure n-gram-based approach very much relies on content and runs the risk of detecting topics instead of style, which argues the need of using deeper linguistic features. The best combination in the deep feature approach originally reached an F-score of just 62.14%, which could not be boosted by automatic feature selection. However, by taking into account the information from the statistical analysis and merely using the features that were significant or trending, we could equal the surface-feature performance and again reached an F-score of 72.15%. This suggests that a carefully composed set of deep features is as informative as surface-feature word and character n-grams. Moreover, combining surface and deep features resulted in a slight increase in F-score to 72.33%

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

Doggerland

In this project, Ben Branagan, Luke Pendrell and Eva Verhoeven have banded together to create this booklet that dissects a collection of damaged and questionable artefacts around this disappearing land and suggests the possibility of spaces both real and metaphoric. The clean layout and minimal text allows room for these examinations to really sink in and be considered by the viewer. This booklet is the first in a series of three

A corpus-based study of the human impersonal pronoun "('n) mens" in Afrikaans:Compared to "men" and "een mens" in Dutch

This article compares the grammaticalizing human impersonal pronoun ('n) mens in Afrikaans to fully grammaticalized men and non-grammaticalized een mens in Dutch. It is shown that 'n mens and een mens can still be used lexically, unlike mens and men, and that ('n) mens and een mens are restricted to non-referential indefinite, universal-internal uses while men exhibits the whole range of (non-) referential indefinite ones. Despite the latter’s presence in the earliest Afrikaans data, it is argued not to have influenced the development of ('n) mens. This pronoun and Dutch een mens are also found to have syntactic functions other than subjecthood, unlike men. The contrast is attributed to their different degrees of grammaticalization. Lastly, the Afrikaans ‘man’-pronoun is shown to differ from its Dutch counterparts in relying on the second person singular for suppletion, though forms of ('n) mens are found to occasionally occur instead

Low Altitude Thermal Survey by Means of an Automated Unmanned Aerial Vehicle for the Detection of Archaeological Buried Structures

International audienceAirborne thermal prospecting is based on the principle that there is a fundamental difference between the thermal characteristics of buried remains and the environment in which they are buried. The French 'Archéodrone' project aims to combine the flexibility and low cost of using an airborne drone with the accuracy of the registration of a thermal digital camera. This combination allows the use of thermal prospection for archaeological detection at low altitude with high-resolution information, from a microregional scale to the site scale. The first results have allowed us to assess the contribution of this technique for the detection of ancient roads, land plots boundaries, site plans and underground caves. Copyright © 2013 John Wiley & Sons, Ltd

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

Current Limitations in Cyberbullying Detection: on Evaluation Criteria, Reproducibility, and Data Scarcity

The detection of online cyberbullying has seen an increase in societal importance, popularity in research, and available open data. Nevertheless, while computational power and affordability of resources continue to increase, the access restrictions on high-quality data limit the applicability of state-of-the-art techniques. Consequently, much of the recent research uses small, heterogeneous datasets, without a thorough evaluation of applicability. In this paper, we further illustrate these issues, as we (i) evaluate many publicly available resources for this task and demonstrate difficulties with data collection. These predominantly yield small datasets that fail to capture the required complex social dynamics and impede direct comparison of progress. We (ii) conduct an extensive set of experiments that indicate a general lack of cross-domain generalization of classifiers trained on these sources, and openly provide this framework to replicate and extend our evaluation criteria. Finally, we (iii) present an effective crowdsourcing method: simulating real-life bullying scenarios in a lab setting generates plausible data that can be effectively used to enrich real data. This largely circumvents the restrictions on data that can be collected, and increases classifier performance. We believe these contributions can aid in improving the empirical practices of future research in the field

Pharmacological pain relief and fear of childbirth in low risk women; secondary analysis of the RAVEL study

Acknowledgements We would like to thank all of the participants in our study and the midwives and gynaecologists of the participating practices and hospitals respectively. Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on request.Peer reviewedPublisher PD