# A Web Interface for Analyzing Hate Speech
### Related Work: Hate Speech in Social Media and Proposed Algorithmic Solutions  
Hate speech has been a pivotal concept both in public debate and in academia for a long time. However, the proliferation of online journalism along with the diffusion of user-generated content and the possibility of anonymity that it allows [20; 21] has led to the increasing presence of hate speech in mainstream media and social networks [22; 23]. During the recent decades, media production has often been analyzed through the lens of citizen participation. The idea of users' active engagement in the context of main stream media was initially accompanied by promises of enhancing democratization and strengthening bonds with the community [24; 25]. However, the empirical reality of user participation was different from the expectations, as there is lots of dark participation, with examples ranging from misinformation and hate campaigns to individual trolling and cyberbullying; a large variety of participation behaviors are evil, malevolent, and destructive [22]. Journalists identify hate speech as a very frequently occurring problem in participatory spaces [8]. Especially comments, which are considered an integral part of almost every news item [26], have become an important section for hate speech spreading [27]. Furthermore, an overwhelming majority of journalists argue that they frequently come upon hate speech towards journalists in general, while most of them report a strong increase in hate speech personally directed at them [28]. When directed at professionals, hate speech can cause negative effects both on journalists themselves and journalistic work: it might impede their ability to fulfill their duties as it can put them under stark emotional pressure, trigger conflict into newsrooms when opinions diverge on how to deal with hateful attacks or even negatively affect journalists' perception of their audience [28]. Hence, not rarely, professionals see users' contributions as a necessary evil [27] and are compelled to handle a vast amount of amateur content in tandem with their other daily tasks [29]. To avoid problems, such as hate speech, and protect the quality of their online outlets, media organizations adopt policies that establish standards of conduct and restrict certain behaviors and expressions by users [30]. Community managers are thus in charge of moderating users' contributions [31], by employing various strategies for supervising, controlling, and enabling content submission [32]. When pre-moderation is followed, every submission is checked before publication and high security is achieved. However, this method requires considerable human, financial, and time resources [27]. On the other hand, post-moderation policies lead to a simpler and more open approach but can lower the quality [33], exposing the platform to ethical and legal risks. Apart from manual moderation, some websites utilize artificial intelligence techniques to tackle this massive work automatically [34], while others implement semi-automatic approaches that assist humans through the integration of machine learning into the manual process [35]. The automation of the process of hate speech detection relies on the training and evaluation of models, using annotated corpora. The main approaches include lexicon-based term detection and supervised machine learning. Lexicons contain a list of terms, along with their evaluation concerning the relation to hate speech. The terms are carefully selected and evaluated by experts on the field, and they need to be combined with rule-based algorithms [36; 37; 38]. Such algorithms are based on language-specific syntax and rules. Computational models such as unsupervised topic modeling can lead to insight regarding the most frequent terms that allow further categorization of the hate-related topics [39; 40]. In supervised machine learning approaches, models are trained using annotated corpora. Baseline approaches rely on bag-of-words representations combined with machine learning algorithms [36; 37; 38; 40; 41].