TA-COS 2016 : First workshop on text analytics for cybersecurity and online safety : Proceedings

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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

High mannose-specific lectin Msl mediates key interactions of the vaginal Lactobacillus plantarum isolate CMPG5300

To characterize the interaction potential of the human vaginal isolate Lactobacillus plantarum CMPG5300, its genome was mined for genes encoding lectin-like proteins. cmpg5300.05_29 was identified as the gene encoding a putative mannose-binding lectin. Phenotypic analysis of a gene knock-out mutant of cmpg5300.05_29 showed that expression of this gene is important for auto-aggregation, adhesion to the vaginal epithelial cells, biofilm formation and binding to mannosylated glycans. Purification of the predicted lectin domain of Cmpg5300.05_29 and characterization of its sugar binding capacity confirmed the specificity of the lectin for high-mannose glycans. Therefore, we renamed Cmpg5300.05_29 as a mannose-specific lectin (Msl). The purified lectin domain of Msl could efficiently bind to HIV-1 glycoprotein gp120 and Candida albicans, and showed an inhibitory activity against biofilm formation of uropathogenic Escherichia coli, Staphylococcus aureus and Salmonella Typhimurium. Thus, using a combination of molecular lectin characterization and functional assays, we could show that lectin-sugar interactions play a key role in host and pathogen interactions of a prototype isolate of the vaginal Lactobacillus microbiota

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

The chromodomain helicase Chd4 is required for Polycomb-mediated inhibition of astroglial differentiation

Polycomb group (PcG) proteins form transcriptional repressor complexes with well-established functions during cell-fate determination. Yet, the mechanisms underlying their regulation remain poorly understood. Here, we extend the role of Polycomb complexes in the temporal control of neural progenitor cell (NPC) commitment by demonstrating that the PcG protein Ezh2 is necessary to prevent the premature onset of gliogenesis. In addition, we identify the chromodomain helicase DNA-binding protein 4 (Chd4) as a critical interaction partner of Ezh2 required specifically for PcG-mediated suppression of the key astrogenic marker gene GFAP. Accordingly, in vivo depletion of Chd4 in the developing neocortex promotes astrogenesis. Collectively, these results demonstrate that PcG proteins operate in a highly dynamic, developmental stage-dependent fashion during neural differentiation and suggest that target gene-specific mechanisms regulate Polycomb function during sequential cell-fate decisions

B-cell targeting with anti-CD38 daratumumab:implications for differentiation and memory responses

B cell–targeted therapies, such as CD20-targeting mAbs, deplete B cells but do not target the autoantibody-producing plasma cells (PCs). PC-targeting therapies such as daratumumab (anti-CD38) form an attractive approach to treat PC-mediated diseases. CD38 possesses enzymatic and receptor capabilities, which may impact a range of cellular processes including proliferation and differentiation. However, very little is known whether and how CD38 targeting affects B-cell differentiation, in particular for humans beyond cancer settings. Using in-depth in vitro B-cell differentiation assays and signaling pathway analysis, we show that CD38 targeting with daratumumab demonstrated a significant decrease in proliferation, differentiation, and IgG production upon T cell–dependent B-cell stimulation. We found no effect on T-cell activation or proliferation. Furthermore, we demonstrate that daratumumab attenuated the activation of NF-κB in B cells and the transcription of NF-κB–targeted genes. When culturing sorted B-cell subsets with daratumumab, the switched memory B-cell subset was primarily affected. Overall, these in vitro data elucidate novel non-depleting mechanisms by which daratumumab can disturb humoral immune responses. Affecting memory B cells, daratumumab may be used as a therapeutic approach in B cell–mediated diseases other than the currently targeted malignancies.</p

B-cell targeting with anti-CD38 daratumumab:implications for differentiation and memory responses

B cell–targeted therapies, such as CD20-targeting mAbs, deplete B cells but do not target the autoantibody-producing plasma cells (PCs). PC-targeting therapies such as daratumumab (anti-CD38) form an attractive approach to treat PC-mediated diseases. CD38 possesses enzymatic and receptor capabilities, which may impact a range of cellular processes including proliferation and differentiation. However, very little is known whether and how CD38 targeting affects B-cell differentiation, in particular for humans beyond cancer settings. Using in-depth in vitro B-cell differentiation assays and signaling pathway analysis, we show that CD38 targeting with daratumumab demonstrated a significant decrease in proliferation, differentiation, and IgG production upon T cell–dependent B-cell stimulation. We found no effect on T-cell activation or proliferation. Furthermore, we demonstrate that daratumumab attenuated the activation of NF-?B in B cells and the transcription of NF-?B–targeted genes. When culturing sorted B-cell subsets with daratumumab, the switched memory B-cell subset was primarily affected. Overall, these in vitro data elucidate novel non-depleting mechanisms by which daratumumab can disturb humoral immune responses. Affecting memory B cells, daratumumab may be used as a therapeutic approach in B cell–mediated diseases other than the currently targeted malignancies

Automatic Detection of Cyberbullying in Social Media Text

While social media offer great communication opportunities, they also increase the vulnerability of young people to threatening situations online. Recent studies report that cyberbullying constitutes a growing problem among youngsters. Successful prevention depends on the adequate detection of potentially harmful messages and the information overload on the Web requires intelligent systems to identify potential risks automatically. The focus of this paper is on automatic cyberbullying detection in social media text by modelling posts written by bullies, victims, and bystanders of online bullying. We describe the collection and fine-grained annotation of a training corpus for English and Dutch and perform a series of binary classification experiments to determine the feasibility of automatic cyberbullying detection. We make use of linear support vector machines exploiting a rich feature set and investigate which information sources contribute the most for this particular task. Experiments on a holdout test set reveal promising results for the detection of cyberbullying-related posts. After optimisation of the hyperparameters, the classifier yields an F1-score of 64% and 61% for English and Dutch respectively, and considerably outperforms baseline systems based on keywords and word unigrams.Comment: 21 pages, 9 tables, under revie

The major secreted protein Msp1/p75 is O-glycosylated in Lactobacillus rhamnosus GG

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