The increasing popularity of social media platforms such as Twitter and Facebook has led to a rise in the presence of hate and aggressive speech on these platforms. Despite the number of approaches recently proposed in the Natural Language Processing research area for detecting these forms of abusive language, the issue of identifying hate speech at scale is still an unsolved problem. In this article, we propose a robust neural architecture that is shown to perform in a satisfactory way across different languages; namely, English, Italian, and German. We address an extensive analysis of the obtained experimental results over the three languages to gain a better understanding of the contribution of the different components employed in the system, both from the architecture point of view (i.e., Long Short Term Memory, Gated Recurrent Unit, and bidirectional Long Short Term Memory) and from the feature selection point of view (i.e., ngrams, social network–specific features, emotion lexica, emojis, word embeddings). To address such in-depth analysis, we use three freely available datasets for hate speech detection on social media in English, Italian, and German.
BackgroundOsteoarthritis of the knee is a major clinical problem affecting a greater proportion of women than men. Women generally report higher pain intensity at rest and greater perceived functional deficits than men. Women also perform worse than men on function measures such as the 6-minute walk and timed up and go tests. Differences in pain sensitivity, pain during function, psychosocial variables, and physical activity levels are unclear. Further the ability of various biopsychosocial variables to explain physical activity, function and pain is unknown.MethodsThis study examined differences in pain, pain sensitivity, function, psychosocial variables, and physical activity between women and men with knee osteoarthritis (N = 208) immediately prior to total knee arthroplasty. We assessed: (1) pain using self-report measures and a numerical rating scale at rest and during functional tasks, (2) pain sensitivity using quantitative sensory measures, (3) function with self-report measures and specific function tasks (timed walk, maximal active flexion and extension), (4) psychosocial measures (depression, anxiety, catastrophizing, and social support), and (5) physical activity using accelerometry. The ability of these mixed variables to explain physical activity, function and pain was assessed using regression analysis.ResultsOur findings showed significant differences on pain intensity, pain sensitivity, and function tasks, but not on psychosocial measures or physical activity. Women had significantly worse pain and more impaired function than men. Their levels of depression, anxiety, pain catastrophizing, social support, and physical activity, however, did not differ significantly. Factors explaining differences in (1) pain during movement (during gait speed test) were pain at rest, knee extension, state anxiety, and pressure pain threshold; (2) function (gait speed test) were sex, age, knee extension, knee flexion opioid medications, pain duration, pain catastrophizing, body mass index (BMI), and heat pain threshold; and (3) physical activity (average metabolic equivalent tasks (METS)/day) were BMI, age, Short-Form 36 (SF-36) Physical Function, Kellgren-Lawrence osteoarthritis grade, depression, and Knee Injury and Osteoarthritis Outcome Score (KOOS) pain subscale.ConclusionsWomen continue to be as physically active as men prior to total knee replacement even though they have significantly more pain, greater pain sensitivity, poorer perceived function, and more impairment on specific functional tasks.
Although WhatsApp is used by teenagers as one major channel of cyberbullying, such interactions remain invisible due to the app privacy policies that do not allow ex-post data collection. Indeed, most of the information on these phenomena rely on surveys regarding self-reported data. In order to overcome this limitation, we describe in this paper the activities that led to the creation of a WhatsApp dataset to study cyberbullying among Italian students aged 12-13. We present not only the collected chats with annotations about user role and type of offense, but also the living lab created in a collaboration between researchers and schools to monitor and analyse cyberbullying. Finally, we discuss some open issues, dealing with ethical, operational and epistemic aspects.
While there is a wide consensus in the NLP community over the modeling of temporal relations between events, mainly based on Allen's temporal logic, the question on how to annotate other types of event relations, in particular causal ones, is still open. In this work, we present some annotation guidelines to capture causality between event pairs, partly inspired by TimeML. We then implement a rule-based algorithm to automatically identify explicit causal relations in the TempEval-3 corpus. Based on this annotation, we report some statistics on the behavior of causal cues in text and perform a preliminary investigation on the interaction between causal and temporal relations.
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