Threat perception in online anti-migrant speech: a Slovene case study

The aim of this article is to describe the perception of refugees as a threat in Slovene online discourse, based on a critical analysis of commenters’ re­sponses to popular media posts at the height of the European migrant crisis. The proposition of the study is that the perception of migration as a threat is at the core of socially unacceptable discourse (SUD), portraying refugees and migrants as an undesirable and potentially dangerous presence. Within the framework of a comprehensive project examining public responses to media coverage of the arrival of migrants to Slovenia, online comments classified as SUD targeting refugees were extracted and annotated to reveal the recur­ring themes of threat perception. The analysis focused on describing the main categories of threat, as well as the various discursive features and strategies employed. Although the approach to observing this subject is essentially qual­itative, a general case-specific overview of the frequency and distribution of identifiable categories is also given

Threat perception in online anti-migrant speech: a Slovene case study

The aim of this article is to describe the perception of refugees as a threat in Slovene online discourse, based on a critical analysis of commenters’ re­sponses to popular media posts at the height of the European migrant crisis. The proposition of the study is that the perception of migration as a threat is at the core of socially unacceptable discourse (SUD), portraying refugees and migrants as an undesirable and potentially dangerous presence. Within the framework of a comprehensive project examining public responses to media coverage of the arrival of migrants to Slovenia, online comments classified as SUD targeting refugees were extracted and annotated to reveal the recur­ring themes of threat perception. The analysis focused on describing the main categories of threat, as well as the various discursive features and strategies employed. Although the approach to observing this subject is essentially qual­itative, a general case-specific overview of the frequency and distribution of identifiable categories is also given

Challenges in discriminating profanity from hate speech

In this study, we approach the problem of distinguishing general profanity from hate speech in social media, something which has not been widely considered. Using a new dataset annotated specifically for this task, we employ supervised classification along with a set of features that includes -grams, skip-grams and clustering-based word representations. We apply approaches based on single classifiers as well as more advanced ensemble classifiers and stacked generalisation, achieving the best result of accuracy for this 3-class classification task. Analysis of the results reveals that discriminating hate speech and profanity is not a simple task, which may require features that capture a deeper understanding of the text not always possible with surface -grams. The variability of gold labels in the annotated data, due to differences in the subjective adjudications of the annotators, is also an issue. Other directions for future work are discussed

Fully Connected Neural Network with Advance Preprocessor to Identify Aggression over Facebook and Twitter

Aggression Identification and Hate Speech detection had become an essential part of cyberharassment and cyberbullying and an automatic aggression identification can lead to the interception of such trolling. Following the same idealization, vista.ue team participated in the workshop which included a shared task on ’Aggression Identification’. A dataset of 15,000 aggression-annotated Facebook Posts and Comments written in Hindi (in both Roman and Devanagari script) and English languages were made available and different classification models were designed. This paper presents a model that outperforms Facebook FastText (Joulin et al., 2016a) and deep learning models over this dataset. Especially, the English developed system, when used to classify Twitter text, outperforms all the shared task submitted systems