Automatic Hate Speech Detection using Machine Learning: A Comparative Study (original) (raw)
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An Improve Framework for hate speech detection using Machine Learning Approach
IJARCCE, 2021
Hate Speech is any correspondence that decries an individual or a gathering based on some trademark, for example, race, identity, sex, sexual direction, ethnicity, religion, or other trademark. Harmful language (e.g., scorn discourse, damaging discourse, or other hostile discourse) principally targets individuals from minority gatherings and can catalyze genuine savagery towards them. The paper proposes an improve framework for hate speech detection using machine learning approach. This system uses a twitter dataset that contains tweeted messages of both hate speech, offensive language, and also messages that is neither hate speech nor offensive language. The dataset was downloaded from kaggle.com, the dataset contains a total of 24,784 twitted messages. The dataset is made up of 8 columns which we later reduced it to two columns by means of feature_extraction. The reduced columns are the tweet columns which contain the twitted messages and the class columns which contains 0,1 and 2, where 0 is classified as hate speech, 1 is classified as offensive language and 2 is classified as neither hate speech or offensive language. we trained our model using support vector machine and random forest classifier and had an accuracy of 95% and 99%. We then deployed our model to web using python flask for easy evaluation and testing. Our experimental results show that our proposed system had better performance in terms of classifying text as hate speech.
Social Media based Hate Speech Detection using Machine Learning
International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2022
Hate speech is a crime that has been increasing in recent years, not only in person but also online. There are several causes for this. There is tremendous growth in social media that promotes full freedom of expression through anonymity features. Freedom of expression is a human right, but hate speech directed at individuals or groups on the basis of race, caste, religion, ethnicity or nationality, gender, disability, gender identity, etc. is a violation of that sovereignty. Freedom of expression is a human right, but hate speech directed at individuals or groups on the basis of race, caste, religion, ethnicity or nationality, gender, disability, gender identity, etc. is a violation of that sovereignty. It promotes violence and hate crimes, creates social imbalances, and undermines peace, trust and human rights. Revealing hate speech in social media discourse is a very important but complex task. On the one hand, the anonymity provided by the Internet, especially social networks, makes people more likely to engage in hostile behavior. On the other hand, the desire to express one's thoughts on the Internet has increased, leading to the spread of hate speech. Governments and social media platforms can benefit from detection and prevention technologies, as this kind of bigoted language can wreak havoc on society. We help resolve this dilemma by providing a systematic overview of research on this topic in this survey. This project aims to accurately predict various forms by addressing different categories of hate individually and examining a set of text mining functions. Hate speech detection
Detecting Hate Speech and Offensive Language using Machine Learning in Published Online Content
ZAMBIA INFORMATION COMMUNICATION TECHNOLOGY (ICT) JOURNAL, 2023
Businesses are more concerned than ever about hate speech content as most brand communication and advertising move online. Different organisations may be incharge of their products and services but they do not have complete control over their content posted online via their website and social media channels, they have no control over what online users post or comment about their brand. As a result, it became imperative in our study to develop a model that will identify hate speechand, offensive language and detect cyber offence in online published content using machine learning. This study employed an experimental design to develop a detection model for determining which agile methodologies were preferred as a suitable development methodology. Deep learning and HateSonar was used to detect hate speech and offensive language in posted content. This study used data from Twitter and Facebook to detect hate speech. The text was classified as either hate speech, offensive language, or both. During the reconnaissance phase, the combined data (structured and unstructured) was obtained from kaggle.com. The combined data was stored in the database as raw data. This revealed that hate speech and offensive language exist everywhere in the world, and the trend of the vices is on the rise. Using machine learning, the researchers successfully developed a model for detecting offensive language and hate speech on online social media platforms. The labelling in the model makes it simple to categorise data in a meaningful and readable manner. The study establishes that in fore model to detect hate speech and offensive language on online social media platforms, the data set must be categorised and presented in statistical form after running the model; the count indicates the total number of data sets imported. The mean for each category, as well as the standard deviation and the minimum and maximum number of tweets in each category, are also displayed. The study established that preventing online platform abuse in Zambia requires a comprehensive approach that involves government law, responsible platform policies and practices, as well as individual responsibility and accountability. In accordance with this goal, the research was effective in developing the detection model. To guarantee that the model was completely functional, it was trained on the English dataset before being applied to the local language dataset. This was because of the fact that training deep learning models with local datasets can present a number of challenges, such as limited, biased data, data privacy, resource requirements, and model maintenance. However, the efficacy of these systems varies, and there have been concerns raised about the inherent biases and limitations of automatic moderation techniques. The study recommends that future studies consider other sources of information such as Facebook, WhatsApp, Instagram, and other social media platforms, as well as consider harvesting local data sets for training machines rather than relying on foreign data sets; the local data set can then be used to detect offences targeting Zambian citizens on local platforms.
Hate Speech Detection on Twitter: Feature Engineering v.s. Feature Selection
Lecture Notes in Computer Science, 2018
The increasing presence of hate speech on social media has drawn significant investment from governments, companies, and empirical research. Existing methods typically use a supervised text classification approach that depends on carefully engineered features. However, it is unclear if these features contribute equally to the performance of such methods. We conduct a feature selection analysis in such a task using Twitter as a case study, and show findings that challenge conventional perception of the importance of manual feature engineering: automatic feature selection can drastically reduce the carefully engineered features by over 90% and selects predominantly generic features often used by many other language related tasks; nevertheless, the resulting models perform better using automatically selected features than carefully crafted task-specific features.
Hate Speech Detection Using Machine Learning
International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2023
Twitter's central goal is to enable everybody to make and share thoughts and data, and to communicate their suppositions and convictions without boundaries. Twitter's job is to serve the public discussion, which requires portrayal of a different scope of points of view. Yet, it does not advance viciousness against or straightforwardly assault or undermine others based on race, nationality, public cause, rank, sexual direction, age, inability, or genuine illness. Hate Speech can hurt a person or a community. So, it is not appropriate to use hate speech. Now, due to increase in social media usage, hate speech is very commonly used on these platforms. So, it is not possible to identify hate speeches manually. So, it is essnetial to develop an automated hate speech detection model and this resaech work shows different approaches of Natural Language Processing for classification of Hate Speech through Machine Learning Algorithms.
2020
This paper describes the system submitted by our team, KBCNMUJAL, for Task 2 of the shared task Hate Speech and Offensive Content Identification in Indo-European Languages (HASOC), at Forum for Information Retrieval Evaluation, December 16-20, 2020, Hyderabad, India. The datasets of two Dravidian languages Viz. Malayalam and Tamil of size 4000 observations, each were shared by the HASOC organizers. These datasets are used to train the machine using different machine learning algorithms, based on classification and regression models. The datasets consist of tweets or YouTube comments with two class labels offensive and not offensive. The machine is trained to classify such social media messages in these two categories. Appropriate n-gram feature sets are extracted to learn the specific characteristics of the Hate Speech text messages. These feature models are based on TFIDF weights of n-gram. The referred work and respective experiments show that the features such as word, character ...
Hate Speech Analysis Using Machine Learning
Hate speech is usually outlined as any form of communication that disparages a person or a group on the premises of some characteristic such as race, colour, ethnicity, gender, sexual orientation, nationality, religion, or other characteristic . Opinionated text has created a new area of research in text analysis. Traditionally, facts and information-centric view of text was expanded to enable sentiment-aware applications. Nowadays, the increased use of the internet and online activities like ticket booking, online transactions, e-commerce, social media communications, blogging, forums etc. has led to the need for extraction, transformation and analysis of huge amount of information. Hence, new methods are needed to analyze and summarize this information. (Kumar et al 2015) Previous research works face the problem of users being able to obfuscate tweets to beat the current state of the art hate speech detection by using new slang words or through inventive clever spellings of words that are not available in the popular pre-trained word embeddings such as Word2Vec or GloVe, but is highly common with hateful comments.
IJERT-Detection of Hate Speech using Text Mining and Natural Language Processing
International Journal of Engineering Research and Technology (IJERT), 2020
https://www.ijert.org/detection-of-hate-speech-using-text-mining-and-natural-language-processing https://www.ijert.org/research/detection-of-hate-speech-using-text-mining-and-natural-language-processing-IJERTV9IS110257.pdf In today's modern world, technology connected with humanity is doing wonderful things. On the other hand, people inclined to social networks where they have anonymity are bringing out the very nastiest of people in the form of hate speech. Social media hate speech is a serious societal problem which can contribute to magnify the violence ranging from lynching to ethical cleansing. One of the critical tasks of automatic detection of hate speech is differentiating it from the other context of offensive languages. The existing works to distinguish the two categories using the lexical methods showed very low performance metrics values which led to major misclassification. The works with supervised machine learning approaches indeed gave significant results in distinguishing hate and offensive but the presence or absence of certain words of both the classes can serve as both merit and demerit to achieve accurate classification. In this paper, a ternary classification of tweets into hate speech, offensive and neither is performed using multi class classifiers. Among the four classifiers: Logistic Regression, Random forests, Support Vector Machines (SVM) and Naïve Bayes. It can be seen that Random Forest classifier performs significantly well with almost all feature combinations giving maximum accuracy of 0.90 for TFIDF feature technique.
Computer Science Review, 2020
Twitter is a microblogging tool that allow the creation of big data through short digital contents. This study provides a survey of machine learning techniques for hate speech classification from Twitter data streams. Hate speech classification in Twitter data streams has remain a vibrant research focus, but little research efforts have been devoted to the design of a generic metadata architecture, threshold settings and fragmentation issues. Hate speech classification techniques presented in literature address some of the challenges inherent in Twitter data streams but limited in the aforementioned issues. This study presented collection of hate speech benchmarks datasets suitable for testing the efficiency of classification models. This study also presented the pros and cons for single and hybrid machine learning methods in hate speech classification. The summary of performance evaluation for the surveyed machine learning methods was also presented. The study also presented a generic metadata architecture for hate speech classification in Twitter to tackle issues with Twitter data streams. The developed generic metadata architecture was observed to performed better across all evaluation metrics for hate speech detection having 0.95, 0.93, 0.92 and 0.93 for accuracy, precision, recall and F1-score respectively, when compared to similar methods. Similarly, the developed generic metadata architecture for hate speech sentiment classification performed better with F1-score of 91.5% compared to related methods. The developed generic metadata architecture also indicates a more perfect test having an AUC of 0.97, when compared to similar methods. The statistical validation of results points out the efficiency of the developed system. Finally, the results also showed that the developed system is very good for automatic topic detection and categorization.
Detection and Classification of Hate Speech
International Journal of Engineering Applied Sciences and Technology, 2021
The challenges that are to be faced while handling with hate speech is not a new thing. From the past few years due to the boosted usage of internet, hateful activities across social media is increasing rapidly. Improved technology has made it possible to create a platform where people can feel free to share their opinions and experiences.it wouldn't be a problem if this is just the case. but we can also see hateful comments running throughout the social media targeting a person or a community. Hate speech is the statement that targets a person or community of people discriminating based on caste, creed, nationality etc. Our project aims at resolving the above problem by using Machine Learning techniques to automatically detect hate speech and classify them into various classes such as extremely positive, positive neutral etc. We have used classifier that works based on the lexicons and finally compare it with other classifiers that doesn't use lexicons. Aimed beneficiaries of this model are the people who are being targeted on social media. Based on the results they can calculate intensity of the comments.