Textual-based Turkish Offensive Language Detection Model (original) (raw)
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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.
A Turkish Hate Speech Dataset and Detection System
Language Resources and Evaluation (LREC), 2022
Social media posts containing hate speech are reproduced and redistributed at an accelerated pace, reaching greater audiences at a higher speed. We present a machine learning system for automatic detection of hate speech in Turkish, along with a hate speech dataset consisting of tweets collected in two separate domains. We first adopted a definition for hate speech that is in line with our goals and amenable to easy annotation; then designed the annotation schema for annotating the collected tweets. The Istanbul Convention dataset consists of tweets posted following the withdrawal of Turkey from the Istanbul Convention. The Refugees dataset was created by collecting tweets about immigrants by filtering based on commonly used keywords related to immigrants. Finally, we have developed a hate speech detection system using the transformer architecture (BERTurk), to be used as a baseline for the collected dataset. The binary classification accuracy is 77% when the system is evaluated using 5-fold cross validation on the Istanbul Convention dataset and 71% for the Refugee dataset. We also tested a regression model with 0.66 and 0.83 RMSE on a scale of [0-4], for the Istanbul Convention and Refugees datasets.
DEEP at HASOC2019: A Machine Learning Framework for Hate Speech and Offensive Language Detection
2019
In this paper, we describe the system submitted by our team for Hate Speech and Offensive Content Identification in Indo-European Languages (HASOC) shared task held at FIRE 2019. Hate speech and offensive language detection have become an important task due to the overwhelming usage of social media platforms in our daily life. This task has been applied for three languages namely, English, Germany and Hindi. The proposed model uses classical machine learning approaches to create classifiers that are used to classify the given post according to different subtasks.
Association for Computational Linguistics, 2019
This paper describes the submissions of our team, HAD-Tübingen, for the SemEval 2019-Task 6: "OffensEval: Identifying and Categorizing Offensive Language in Social Me-dia". We participated in all the three sub-tasks: Sub-task A-"Offensive language identifica-tion", sub-task B-"Automatic categorization of offense types" and sub-task C-"Offense target identification". As a baseline model we used a Long short-term memory recurrent neu-ral network (LSTM) to identify and categorize offensive tweets. For all the tasks we experimented with external databases in a postpro-cessing step to enhance the results made by our model. The best macro-average F 1 scores obtained for the sub-tasks A, B and C are 0.73, 0.52, and 0.37, respectively.
A Multilingual Evaluation for Online Hate Speech Detection
ACM Transactions on Internet Technology, 2020
The increasing popularity of social media platforms such as Twitter and Facebook has led to a rise in the presence of hate and aggressive speech on these platforms. Despite the number of approaches recently proposed in the Natural Language Processing research area for detecting these forms of abusive language, the issue of identifying hate speech at scale is still an unsolved problem. In this article, we propose a robust neural architecture that is shown to perform in a satisfactory way across different languages; namely, English, Italian, and German. We address an extensive analysis of the obtained experimental results over the three languages to gain a better understanding of the contribution of the different components employed in the system, both from the architecture point of view (i.e., Long Short Term Memory, Gated Recurrent Unit, and bidirectional Long Short Term Memory) and from the feature selection point of view (i.e., ngrams, social network–specific features, emotion lex...
Performance of Text Classification Methods in Detection of Hate Speech in Media
International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022
With the increased popularity of social media sites like Twitter and Instagram over the years, it has become easier for users of the sites to remain anonymous while taking part in hate speech against various peoples and communities. As a result, in an effort to curb such hate speech online, detection of the same has gained a lot more attention of late. Since curbing the growing amount of hate speech online by manual methods is not feasible, detection and control via Natural Language Processing and Deep Learning methods has gained popularity. In this paper, we evaluate the performance of a sequential model with the Universal Sentence Encoder against the RoBERTa method on different datasets for hate speech detection. The result of this study has shown a greater performance overall from using a Sequential model with a multilingual USE layer.
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 ...
2019
In this paper an approach for the automatic detection of offensive language in German twitter posts, so called tweets, based on a data set provided by the organizers from the GermEval2019 contest is presented. Two different approaches were used. The first one is based on a document-term-matrix and the second one uses fastText to represent tweets as numerical vectors. Additionally, some text based features, e.g. sentiment analysis of the text and emojis were added. Further, some statistic features were calculated, e.g. the number of special characters, hashtags and mentions. As a classifier a support vector machine with radial kernel function was utilized. The best f1-macro values for subtask 1 of 0.7978, subtask 2 of 0.5957 and for subtask 3 of 0.7055, validated by a ten-fold cross validation, were achieved by using a self-trained unsupervised fastText model to vectorize the tweets.