The shift of human communication to online platforms brings many benefits to society due to the ease of publication of opinions, sharing experience, getting immediate feedback and the opportunity to discuss the hottest topics. Besides that, it builds up a space for antisocial behavior such as harassment, insult and hate speech. This research is dedicated to detection of antisocial online behavior detection (AOB) -an umbrella term for cyberbullying, hate speech, cyberaggression and use of any hateful textual content. First, we provide a benchmark of deep learning models found in the literature on AOB detection. Deep learning has already proved to be efficient in different types of decision support: decision support from financial disclosures, predicting process behavior, text-based emoticon recognition. We compare methods of traditional machine learning with deep learning, while applying important advancements of natural language processing: we examine bidirectional encoding, compare attention mechanisms with simpler reduction techniques, and investigate whether the hierarchical representation of the data and application of attention on differ-Financial support from the Deutsche Forschungsgemeinschaft via the IRTG 1792 "High Dimensional Non Stationary Time Series", Humboldt-Universität zu Berlin, is gratefully acknowledged.
The shift of human communication to online platforms brings many benefits to society due to the ease of publication of opinions, sharing experience, getting immediate feedback and the opportunity to discuss the hottest topics. Besides that, it builds up a space for antisocial behavior such as harassment, insult and hate speech. This research is dedicated to detection of antisocial online behavior detection (AOB) -an umbrella term for cyberbullying, hate speech, cyberaggression and use of any hateful textual content. First, we provide a benchmark of deep learning models found in the literature on AOB detection. Deep learning has already proved to be efficient in different types of decision support: decision support from financial disclosures, predicting process behavior, text-based emoticon recognition. We compare methods of traditional machine learning with deep learning, while applying important advancements of natural language processing: we examine bidirectional encoding, compare attention mechanisms with simpler reduction techniques, and investigate whether the hierarchical representation of the data and application of attention on differ-Financial support from the Deutsche Forschungsgemeinschaft via the IRTG 1792 "High Dimensional Non Stationary Time Series", Humboldt-Universität zu Berlin, is gratefully acknowledged.
Smart Contracts are commonly considered to be an important component or even a key to many business solutions in an immense variety of sectors and promises to securely increase their individual efficiency in an ever more digitized environment.Introduced in the early 1990's, the technology has gained a lot of attention with its application to blockchain technology to an extent, that can be considered a veritable hype. Reflecting the growing institutional interest, this intertwined exploratory study between statistics, information technology, and law contrasts these idealistic stories with the data reality and provides a mandatory step of understanding the matter, before any further relevant applications are discussed as being "factually" able to replace traditional constructions. Besides fundamental flaws and application difficulties of currently employed Smart Contracts, the technological drive and enthusiasm backing it may however serve as a jump-off board for future developments thrusting well in the presently unshakeable traditional structures.
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