Deep Context-Aware Embedding for Abusive and Hate Speech detection on Twitter (original) (raw)
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Hate Speech Detection in Indonesian Twitter using Contextual Embedding Approach
Indonesian Journal of Computing and Cybernetics Systems, 2021
Hate speech develops along with the rapid development of social media. Hate speech is often issued due to a lack of public awareness of the difference between criticism and statements that might contribute to this crime. Therefore, it is very important to do early detection of sentences that will be written before causing a criminal act due to public ignorance. In this paper, we use the advancement of deep neural networks to predict whether a sentence contains a hate speech and abusive tone. We demonstrate the robustness of different word and contextual embedding to represent the semantic of hate speech words. In addition, we use a document embedding representation via a recurrent neural networks with gated recurrent unit as the main architecture to provide richer representation. Compared to syntactic representation of the previous approach, the contextual embedding in our model proved to give a significant boost on the performance by a significant margin.
PDHS: Pattern-Based Deep Hate Speech Detection With Improved Tweet Representation
IEEE Access
Automatic hate speech identification in unstructured Twitter is significantly more difficult to analyze, posing a significant challenge. Existing models heavily depend on feature engineering, which increases the time complexity of detecting hate speech. This work aims to classify and detect hate speech using a linguistic pattern-based approach as pre-trained transformer language models. As a result, a novel Pattern-based Deep Hate Speech (PDHS) detection model was proposed to detect the presence of hate speech using a cross-attention encoder with a dual-level attention mechanism. Instead of concatenating the features, our model computes dot product attention for better representation by reducing the irrelevant features. The first level of Attention is extracting aspect terms using predefined parts-of-speech tagging. The second level of Attention is extracting the sentiment polarity to form a pattern. Our proposed model trains the extracted patterns with term frequency, parts-of-speech tag, and Sentiment Scores. The experimental results on Twitter Dataset can learn effective features to enhance the performance with minimum training time and attained 88%F1Score.
AI ML NIT Patna at HASOC 2019: Deep Learning Approach for Identification of Abusive Content
2019
Social media is a globally open place for online users to express their thoughts and opinions. There are numerous advantages of social media but some severe challenges are also associated with it. Antisocial and abusive conduct has become more common due to the emergence of social media. Identification of Hate Speech, Cyber-aggression, and Offensive language is a very challenging task. The nature of structures of the natural language makes this task even more tedious. Being a challenging task, we are fascinated to propose a deep learning system based on Convolutional Neural Networks to identify Hate Speech, Offensive language, and Profanity. We have done experiments with three different embeddings. These experiments have been associated with comments of code-mixed Hindi-English and multi-domain social media text. We have found that One-hot embedding performed better than pre-trained fastText embedding for the code-mixed Hindi dataset.
Proceedings of the 13th International Workshop on Semantic Evaluation
This paper describes a bidirectional Long-Short Term Memory network for identifying offensive language in Twitter. Our system has been developed in the context of the Se-mEval 2019 Task 6 which comprises three different sub-tasks, namely A: Offensive Language Detection, B: Categorization of Offensive Language, C: Offensive Language Target Identification. We used a pre-trained Word Embeddings in tweet data, including information about emojis and hashtags. Our approach achieves good performance in the three subtasks.
Investigating Deep Learning Approaches for Hate Speech Detection in Social Media
ArXiv, 2020
The phenomenal growth on the internet has helped in empowering individual's expressions, but the misuse of freedom of expression has also led to the increase of various cyber crimes and anti-social activities. Hate speech is one such issue that needs to be addressed very seriously as otherwise, this could pose threats to the integrity of the social fabrics. In this paper, we proposed deep learning approaches utilizing various embeddings for detecting various types of hate speeches in social media. Detecting hate speech from a large volume of text, especially tweets which contains limited contextual information also poses several practical challenges. Moreover, the varieties in user-generated data and the presence of various forms of hate speech makes it very challenging to identify the degree and intention of the message. Our experiments on three publicly available datasets of different domains shows a significant improvement in accuracy and F1-score.
Proceedings of the 13th International Workshop on Semantic Evaluation, 2019
This paper describes our system submissions as part of our participation (team name: JU ETCE 17 21) in the SemEval 2019 shared task 6: "OffensEval: Identifying and Categorizing Offensive Language in Social Media". We participated in all the three sub-tasks: i) Sub-task A: offensive language identification, ii) Sub-task B: automatic categorization of offense types, and iii) Sub-task C: offense target identification. We employed machine learning as well as deep learning approaches for the sub-tasks. We employed Convolutional Neural Network (CNN) and Recursive Neural Network (RNN) Long Short-Term Memory (LSTM) with pre-trained word embeddings. We used both word2vec and Glove pre-trained word embeddings. We obtained the best F1score using CNN based model for sub-task A, LSTM based model for sub-task B and Logistic Regression based model for sub-task C. Our best submissions achieved 0.7844, 0.5459 and 0.48 F1-scores for sub-task A, sub-task B and sub-task C respectively.
Detection of Abusive Language from Tweets in Social Networks
Detection of abusive language in user generated online content has become an issue of increasing importance in recent years. Most current commercial methods make use of blacklists and regular expressions, however these measures fall short when contending with more subtle, less ham-fisted ex-samples of hate speech. In this work, we develop a machine learning based method to detect hate speech on online user comments from two domains which outperforms a state-of-the-art deep learning approach. We also develop a corpus of user comments annotated for abusive language, the first of its kind. Finally, we use our detection tool to analyze abusive language over time and in different settings to further enhance our knowledge of this behavior.
BIMA JOURNAL OF SCIENCE AND TECHNOLOGY (2536-6041)
Hate speech on online social networks is a general problem across social media platforms that has the potential of causing physical harm to the society. The growing number of hateful comments on the Internet and the rate at which tweets and posts are published per second on social media make it a challenging task to manually identify and remove the hateful commentsfrom such posts. Although numerous publications have proposed machine learning approaches to detect hate speech and other antisocial online behaviours without concentrating on blocking the hate speech from being published on social media. Similarly, prior publications on deep learning and multi-platform approaches did not work on the topic of detecting hate speech in Englishlanguage comments on Twitter and Facebook. This paper proposed a deep learning approach based on a hybrid of convolutional neural network (CNN) and long short-term memory (LSTM) with pre-trained GloVe words embedding to automatically detect and block ha...
Detection of Hate Tweets using Machine Learning and Deep Learning
2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), 2020
Cyberbullying has become a highly problematic occurrence due to its potential of anonymity and its ease for others to join in the harassment of victims. The distancing effect that technological devices have, has led to cyberbullies say and do harsher things compared to what is typical in a traditional face-toface bullying situation. Given the great importance of the problem, detection is becoming a key area of cyberbullying research. Therefore, it is highly necessary for a framework to accurately detect new cyberbullying instances automatically. To review the machine learning and deep learning approaches, two datasets were used. The first dataset was provided by the University of Maryland consisting of over 30,000 tweets, whereas the second dataset was based on the article 'Automated Hate Speech Detection and the Problem of Offensive Language' by Davidson et al., containing roughly 25,000 tweets. The paper explores machine learning approaches using word embeddings such as DBOW (Distributed Bag of Words) and DMM (Distributed Memory Mean) and the performance of Word2vec Convolutional Neural Networks (CNNs) to classify online hate.
Detecting Hate Speech on Twitter Using a Convolution-GRU Based Deep Neural Network
In recent years, the increasing propagation of hate speech on social media and the urgent need for effective countermeasures have drawn significant investment from governments, companies, and empirical research. Despite a large number of emerging scientific studies to address the problem, a major limitation of existing work is the lack of comparative evaluations, which makes it difficult to assess the contribution of individual works. This paper introduces a new method based on a deep neural network combining convolutional and gated recurrent networks. We conduct an extensive evaluation of the method against several baselines and state of the art on the largest collection of publicly available Twitter datasets to date, and show that compared to previously reported results on these datasets, our proposed method is able to capture both word sequence and order information in short texts, and it sets new benchmark by outperforming on 6 out of 7 datasets by between 1 and 13 percents in F1. We also extend the existing dataset collection on this task by creating a new dataset covering different topics.