Query: Hate speech detection: Challenges and solutions

title: Hate speech detection: Challenges and solutions
abstract: As online content continues to grow, so does the spread of hate speech. We identify and examine challenges faced by online automatic approaches for hate speech detection in text. Among these difficulties are subtleties in language, differing definitions on what constitutes hate speech, and limitations of data availability for training and testing of these systems. Furthermore, many recent approaches suffer from an interpretability problem that is, it can be difficult to understand why the systems make the decisions that they do. We propose a multi view SVM approach that achieves near state of the art performance, while being simpler and producing more easily interpretable decisions than neural methods. We also discuss both technical and practical challenges that remain for this task.

title: Hate and Toxic Speech Detection in the Context of Covid 19 Pandemic using XAI: Ongoing Applied Research
abstract: As social distancing, self quarantines, and travel restrictions have shifted a lot of pandemic conversations to social media so does the spread of hate speech. While recent machine learning solutions for automated hate and offensive speech identification are available on Twitter, there are issues with their interpretability. We propose a novel use of learned feature importance which improves upon the performance of prior state of the art text classification techniques, while producing more easily interpretable decisions. We also discuss both technical and practical challenges that remain for this task.

title: Exploring Hate Speech Detection in Multimodal Publications
abstract: In this work we target the problem of hate speech detection in multimodal publications formed by a text and an image. We gather and annotate a large scale dataset from Twitter, MMHS150K, and propose different models that jointly analyze textual and visual information for hate speech detection, comparing them with unimodal detection. We provide quantitative and qualitative results and analyze the challenges of the proposed task. We find that, even though images are useful for the hate speech detection task, current multimodal models cannot outperform models analyzing only text. We discuss why and open the field and the dataset for further research.

title: Towards generalisable hate speech detection: a review on obstacles and solutions
abstract: Hate speech is one type of harmful online content which directly attacks or promotes hate towards a group or an individual member based on their actual or perceived aspects of identity, such as ethnicity, religion, and sexual orientation. With online hate speech on the rise, its automatic detection as a natural language processing task is gaining increasing interest. However, it is only recently that it has been shown that existing models generalise poorly to unseen data. This survey paper attempts to summarise how generalisable existing hate speech detection models are and the reasons why hate speech models struggle to generalise, sums up existing attempts at addressing the main obstacles, and then proposes directions of future research to improve generalisation in hate speech detection.

title: Challenges of Hate Speech Detection in Social Media
abstract: The detection of hate speech in social media is a crucial task. The uncontrolled spread of hate has the potential to gravely damage our society, and severely harm marginalized people or groups. A major arena for spreading hate speech online is social media. This significantly contributes to the difficulty of automatic detection, as social media posts include paralinguistic signals (e.g. emoticons, and hashtags) and their linguistic content contains plenty of poorly written text. Another difficulty is presented by the context dependent nature of the task, and the lack of consensus on what constitutes as hate speech, which makes the task difficult even for humans. This makes the task of creating large labeled corpora difficult, and resource consuming. The problem posed by ungrammatical text has been largely mitigated by the recent emergence of deep neural network (DNN) architectures that have the capacity to efficiently learn various features. For this reason, we proposed a deep natural language processing (NLP) model combining convolutional and recurrent layers for the automatic detection of hate speech in social media data. We have applied our model on the HASOC2019 corpus, and attained a macro F1 score of 0.63 in hate speech detection on the test set of HASOC. The capacity of DNNs for efficient learning, however, also means an increased risk of overfitting. Particularly, with limited training data available (as was the case for HASOC) For this reason, we investigated different methods for expanding resources used. We have explored various opportunities, such as leveraging unlabeled data, similarly labeled corpora, as well as the use of novel models. Our results showed that by doing so, it was possible to significantly increase the classification score attained.

title: A GA Based Approach to Fine Tuning BERT for Hate Speech Detection
abstract: There exists a high level of variance in the finetuning of contextual word embedding models like Bi directional Encoder Representation for Transformers (BERT) Such variance can be introduced by various factors, such as the BERT encoder layers, the fine tuning architecture or the hyperparameter settings. Each architectural design choice leads to different results for a specific task. Hence, we are interested in reducing this variance given some settings. This study illustrates the use of a Genetic Algorithm (GA) to search, select and design a (near optimal fine tuned BERT architecture for a hate speech detection task. We propose an appropriate encoding scheme for this task which represents the possible solutions in a way that supports a less time consuming search for the global optima. Each encoding for a single solution represents both the BERT architecture and the fine tuning architecture for a robust search. The automatic search provided by the GA, helps to reduce the time and cost involved in manual trial and error design methods. The experiments show that the resulting architectural design and hyperparameter settings are good choices for the hate speech detection task. Our method, although validated only on hate speech detection tasks, can easily be extended and generalized to other text classification tasks.

title: Hate Speech Detection in Saudi Twittersphere: A Deep Learning Approach
abstract: With the rise of hate speech phenomena in Twittersphere, significant research efforts have been undertaken to provide automatic solutions for detecting hate speech, varying from simple machine learning models to more complex deep neural network models. Despite that, research works investigating hate speech problem in Arabic are still limited. This paper, therefore, aims to investigate several neural network models based on Convolutional Neural Network (CNN) and Recurrent Neural Networks (RNN) to detect hate speech in Arabic tweets. It also evaluates the recent language representation model BERT on the task of Arabic hate speech detection. To conduct our experiments, we firstly built a new hate speech dataset that contains 9,316 annotated tweets. Then, we conducted a set of experiments on two datasets to evaluate four models: CNN, GRU, CNN+GRU and BERT. Our experimental results on our dataset and an out domain dataset show that CNN model gives the best performance with an F1 score of 0.79 and AUROC

title: HATE SPEECH DETECTION
abstract: As online content continues to grow, so does the spread of hate speech. We identify and examine challenges faced by online automatic approaches for hate speech detection in text. The increasing propagation on social media and the urgent need for effective countermeasures have drawn significant investment from governments, companies, and researchers. Machine Learning and predictive analytics now help companies to focus on important areas, anticipating problems before they happen, reducing costs, and increasing revenue.

title: Hate Speech Detection Using Genetic Programming
abstract: There has been a steep increase in the use of social media in our everyday lives in recent years. Along with this, there has been an increase in hate speech disseminated on these platforms, due to the anonymity of the users as well as the ease of use. Social media platforms need to filter and prevent the spread of hate speech to protect their users and society. Due to the high traffic, automatic detection of hate speech is necessary. Hate speech detection is one of the most difficult classification challenges in text mining. Research in this domain focuses on the use of supervised machine learning approaches, such as support vector machine, logistic regression, convolutional neural network, and random forest. Ensemble techniques have also been employed. However, the performance of these approaches has not yet reached an acceptable level. In this paper, we propose the use of the Genetic Programming (GP) approach for binary classification of hate speech on social media platforms. Each individual in the GP framework represents a classifier that is evolved to optimize Fl score. Experimental results show the effectiveness of our GP approach; the proposed approach outperforms the state of the art using the same dataset HatEval.

title: A Systematic Literature Review of Different Machine Learning Methods on Hate Speech Detection
abstract: Hate speech is one of the most challenging problem internet is facing today. This systematic literature review examine hate speech detection problem and will be used to do an experimental approach on detecting hate speech and abusive language. This work also provide an overview of previous research, including methods, algorithms, and main features used. We use two research questions in this literature review which will be the foundation of the next experimental research. Correctly classifying a piece of text as an actual hate speech requires a lot of correctly labelled data. Most common challenges are different languages, out of vocabulary words, long range dependencies and many more.