Titlebook: Quantifying Approaches to Discourse for Social Scientists; Ronny Scholz Book 2019 The Editor(s) (if applicable) and The Author(s) 2019 tec

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also add new models. We use well-established classification methods, such as Logistic regression, Random forest, and Support Vector Machine (SVM). The users’ data from which the system should detect if they are depressed, are represented as vectors composed of (a) various task-oriented features inc

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Julian Hamann,Jens Maesse,Ronny Scholz,Johannes Angermullers the key information for the detection of users suffering from self-harm. We also present some extensions to BoSE aimed to better model and capture the emotional changes expressed by social media users through their posts. The proposed approach showed competitive performance in comparison to other

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Johannes Angermuller,Thed van Leeuwenand anorexia, where our classifiers obtained the best performance for precision, F1, latency-weighted F1, and ERDE at 5 and 50. This work suggests that BERT-based classifiers, when trained appropriately, can accurately infer which social media users are at risk of self-harming, with precision up to

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Ronny Scholz in the latter case, the eggs are usually unfertilized or the embryos do not survive. Mature eggs are elongated or cylindrical in shape and white or cream in color. Eggs are laid by a female in an uninterrupted sequence or almost simultaneously so they adhere quite firmly to one another and form dif

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audiologist