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

Detecting Hate Speech in Social Media

In this paper we examine methods to detect hate speech in social media, while distinguishing this from general profanity. We aim to establish lexical baselines for this task by applying supervised classification methods using a recently released dataset annotated for this purpose. As features, our system uses character n-grams, word n-grams and word skip-grams. We obtain results of 78% accuracy in identifying posts across three classes. Results demonstrate that the main challenge lies in discriminating profanity and hate speech from each other. A number of directions for future work are discussed.Comment: Proceedings of Recent Advances in Natural Language Processing (RANLP). pp. 467-472. Varna, Bulgari

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

Trademarks, Hate Speech, and Solving a Puzzle of Viewpoint Bias

In this article, I argue that in the seemingly straightforward ruling in Iancu v Brunetti, striking down a provision of the law governing trademarks, the Court revealed a significant clarification of the limits of the doctrine of viewpoint discrimination. In free speech doctrine, the Court is unanimous in condemning viewpoint discrimination, but its contours remain “slippery” because viewpoint bias is rarely a game changer in a given case. One enduring puzzle is whether a limit on the mode or manner of communication – a ban on racial epithets, for example – embodies viewpoint discrimination. This question has been unresolved for almost thirty years, ever since the Court’s murky opinion in R.A.V. v St. Paul struck down, as viewpoint based, an ordinance aimed at fighting words that “arouse[d] anger, alarm, or resentment in others on the basis of race, color, creed, religion, or gender.” The Court was not clear whether the ordinance was viewpoint biased because it regulated one side of a public debate or limited a mode or manner of debate for both sides. The difference between these two possible readings matters: if limits on modes or manners of speech are deemed to be viewpoint discrimination, then it may be virtually impossible to enact, for example, bans on racial epithets at a public university. But Brunetti clarified matters considerably. The Court struck down the provisions of the Lanham Act prohibiting the registration of “immoral” and “scandalous” marks as viewpoint biased. The conflict between Justice Elena Kagan’s opinion for the Court and the lead opposition opinion written by Justice Sonia Sotomayor illuminated an important area of agreement that appears to control a majority of the Court. That agreement is this: that worries about viewpoint bias do not ordinary come into play when the government regulates the mode and manner of communication as opposed to the ideas conveyed. Such a principle has a number of implications. Perhaps the most important is that R.A.V. is less significant in First Amendment doctrine than it has seemed for thirty years. Also, some kinds of speech codes could survive First Amendment challenge, as long as they apply in certain fora and are aimed at the mode and manner of communication rather than the ideas expressed. Another implication would be that it would be possible for Congress to rewrite the now-defunct provisions of the Lanham Act to survive First Amendment challenge and also satisfy much of Congress’s original goals

Detecting Abusive Language on Online Platforms: A Critical Analysis

Abusive language on online platforms is a major societal problem, often leading to important societal problems such as the marginalisation of underrepresented minorities. There are many different forms of abusive language such as hate speech, profanity, and cyber-bullying, and online platforms seek to moderate it in order to limit societal harm, to comply with legislation, and to create a more inclusive environment for their users. Within the field of Natural Language Processing, researchers have developed different methods for automatically detecting abusive language, often focusing on specific subproblems or on narrow communities, as what is considered abusive language very much differs by context. We argue that there is currently a dichotomy between what types of abusive language online platforms seek to curb, and what research efforts there are to automatically detect abusive language. We thus survey existing methods as well as content moderation policies by online platforms in this light, and we suggest directions for future work

Separating Hate Speech and Offensive Language Classes via Adversarial Debiasing

Research to tackle hate speech plaguing online media has made strides in providing solutions, analyzing bias and curating data. A challenging problem is ambiguity between hate speech and offensive language, causing low performance both overall and specifically for the hate speech class. It can be argued that misclassifying actual hate speech content as merely offensive can lead to further harm against targeted groups. In our work, we mitigate this potentially harmful phenomenon by proposing an adversarial debiasing method to separate the two classes. We show that our method works for English, Arabic German and Hindi, plus in a multilingual setting, improving performance over baselines