Multi-channel Convolutional Neural Network for Hate Speech Detection in Social Media

2020 ◽

pp. 188-204

Author(s):

Brian Tuan Khieu ◽

Melody Moh

Keyword(s):

Neural Network ◽

Social Media ◽

Hate Speech ◽

Speech Detection ◽

Network Applications ◽

Current State ◽

New Directions ◽

Key Techniques ◽

Neural Network Applications ◽

Positive Results

This chapter presents a literature survey of the current state of hate speech detection models with a focus on neural network applications in the area. The growth and freedom of social media has facilitated the dissemination of positive and negative ideas. Proponents of hate speech are one of the key abusers of the privileges allotted by social media, and the companies behind these networks have a vested interest in identifying such speech. Manual moderation is too cumbersome and slow to deal with the torrent of content generation on these social media sites, which is why many have turned to machine learning. Neural network applications in this area have been very promising and yielded positive results. However, there are newly discovered and unaddressed problems with the current state of hate speech detection. Authors' survey identifies the key techniques and methods used in identifying hate speech, and they discuss promising new directions for the field as well as newly identified issues.

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Hate Speech Classification in Indonesian Language Tweets by Using Convolutional Neural Network

Journal of ICT Research and Applications ◽

10.5614/itbj.ict.res.appl.2021.14.3.2 ◽

2021 ◽

Vol 14 (3) ◽

pp. 225-239

Author(s):

Dewa Ayu Nadia Taradhita ◽

I Ketut Gede Darma Putra

Keyword(s):

Neural Network ◽

Social Media ◽

Convolutional Neural Network ◽

Hate Speech ◽

Rapid Development ◽

Term Weighting ◽

Weighting Method ◽

Testing Stage ◽

Testing Accuracy ◽

Speech Classification

The rapid development of social media, added with the freedom of social media users to express their opinions, has influenced the spread of hate speech aimed at certain groups. Online based hate speech can be identified by the used of derogatory words in social media posts. Various studies on hate speech classification have been done, however, very few researches have been conducted on hate speech classification in the Indonesian language. This paper proposes a convolutional neural network method for classifying hate speech in tweets in the Indonesian language. Datasets for both the training and testing stages were collected from Twitter. The collected tweets were categorized into hate speech and non-hate speech. We used TF-IDF as the term weighting method for feature extraction. The most optimal training accuracy and validation accuracy gained were 90.85% and 88.34% at 45 epochs. For the testing stage, experiments were conducted with different amounts of testing data. The highest testing accuracy was 82.5%, achieved by the dataset with 50 tweets in each category.

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A Framework for Hate Speech Detection Using Deep Convolutional Neural Network

IEEE Access ◽

10.1109/access.2020.3037073 ◽

2020 ◽

Vol 8 ◽

pp. 204951-204962

Author(s):

Pradeep Kumar Roy ◽

Asis Kumar Tripathy ◽

Tapan Kumar Das ◽

Xiao-Zhi Gao

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Hate Speech ◽

Deep Convolutional Neural Network ◽

Speech Detection

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Bangla hate speech detection on social media using attention-based recurrent neural network

Journal of Intelligent Systems ◽

10.1515/jisys-2020-0060 ◽

2021 ◽

Vol 30 (1) ◽

pp. 578-591

Author(s):

Amit Kumar Das ◽

Abdullah Al Asif ◽

Anik Paul ◽

Md. Nur Hossain

Keyword(s):

Neural Network ◽

Machine Learning ◽

Social Media ◽

Hate Speech ◽

Negative Aspect ◽

Speech Detection ◽

Use Of Technology ◽

Machine Learning Model ◽

Bengali Language ◽

Facebook Pages

Abstract Hate speech has spread more rapidly through the daily use of technology and, most notably, by sharing your opinions or feelings on social media in a negative aspect. Although numerous works have been carried out in detecting hate speeches in English, German, and other languages, very few works have been carried out in the context of the Bengali language. In contrast, millions of people communicate on social media in Bengali. The few existing works that have been carried out need improvements in both accuracy and interpretability. This article proposed encoder–decoder-based machine learning model, a popular tool in NLP, to classify user’s Bengali comments from Facebook pages. A dataset of 7,425 Bengali comments, consisting of seven distinct categories of hate speeches, was used to train and evaluate our model. For extracting and encoding local features from the comments, 1D convolutional layers were used. Finally, the attention mechanism, LSTM, and GRU-based decoders have been used for predicting hate speech categories. Among the three encoder–decoder algorithms, attention-based decoder obtained the best accuracy (77%).

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A Web Interface for Analyzing Hate Speech

Future Internet ◽

10.3390/fi13030080 ◽

2021 ◽

Vol 13 (3) ◽

pp. 80

Author(s):

Lazaros Vrysis ◽

Nikolaos Vryzas ◽

Rigas Kotsakis ◽

Theodora Saridou ◽

Maria Matsiola ◽

...

Keyword(s):

Machine Learning ◽

Social Media ◽

Graphical User Interface ◽

Hate Speech ◽

Web Interface ◽

Learning Models ◽

Speech Detection ◽

Media Services ◽

The Web ◽

Machine Learning Models

Social media services make it possible for an increasing number of people to express their opinion publicly. In this context, large amounts of hateful comments are published daily. The PHARM project aims at monitoring and modeling hate speech against refugees and migrants in Greece, Italy, and Spain. In this direction, a web interface for the creation and the query of a multi-source database containing hate speech-related content is implemented and evaluated. The selected sources include Twitter, YouTube, and Facebook comments and posts, as well as comments and articles from a selected list of websites. The interface allows users to search in the existing database, scrape social media using keywords, annotate records through a dedicated platform and contribute new content to the database. Furthermore, the functionality for hate speech detection and sentiment analysis of texts is provided, making use of novel methods and machine learning models. The interface can be accessed online with a graphical user interface compatible with modern internet browsers. For the evaluation of the interface, a multifactor questionnaire was formulated, targeting to record the users’ opinions about the web interface and the corresponding functionality.

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Spam Detection on Social Media Using Semantic Convolutional Neural Network

International Journal of Knowledge Discovery in Bioinformatics ◽

10.4018/ijkdb.2018010102 ◽

2018 ◽

Vol 8 (1) ◽

pp. 12-26 ◽

Cited By ~ 16

Author(s):

Gauri Jain ◽

Manisha Sharma ◽

Basant Agarwal

Keyword(s):

Neural Network ◽

Social Media ◽

Convolutional Neural Network ◽

State Of The Art ◽

Spam Detection ◽

Learning Technology ◽

The Social ◽

Social Media Text ◽

Current Article ◽

Semantic Layer

This article describes how spam detection in the social media text is becoming increasing important because of the exponential increase in the spam volume over the network. It is challenging, especially in case of text within the limited number of characters. Effective spam detection requires more number of efficient features to be learned. In the current article, the use of a deep learning technology known as a convolutional neural network (CNN) is proposed for spam detection with an added semantic layer on the top of it. The resultant model is known as a semantic convolutional neural network (SCNN). A semantic layer is composed of training the random word vectors with the help of Word2vec to get the semantically enriched word embedding. WordNet and ConceptNet are used to find the word similar to a given word, in case it is missing in the word2vec. The architecture is evaluated on two corpora: SMS Spam dataset (UCI repository) and Twitter dataset (Tweets scrapped from public live tweets). The authors' approach outperforms the-state-of-the-art results with 98.65% accuracy on SMS spam dataset and 94.40% accuracy on Twitter dataset.

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Online Multilingual Hate Speech Detection: Experimenting with Hindi and English Social Media

10.20944/preprints202011.0646.v1 ◽

2020 ◽

Author(s):

Neeraj Vashistha ◽

Arkaitz Zubiaga

Keyword(s):

Social Media ◽

Hate Speech ◽

Model Performance ◽

Academic Community ◽

Human Interaction ◽

Superior Performance ◽

Competitive Performance ◽

Speech Detection ◽

Improve Model ◽

Use Of The Internet

The exponential increase in the use of the Internet and social media over the last two decades has changed human interaction. This has led to many positive outcomes, but at the same time it has brought risks and harms. While the volume of harmful content online, such as hate speech, is not manageable by humans, interest in the academic community to investigate automated means for hate speech detection has increased. In this study, we analyse six publicly available datasets by combining them into a single homogeneous dataset and classify them into three classes, abusive, hateful or neither. We create a baseline model and we improve model performance scores using various optimisation techniques. After attaining a competitive performance score, we create a tool which identifies and scores a page with effective metric in near-real time and uses the same as feedback to re-train our model. We prove the competitive performance of our multilingual model on two langauges, English and Hindi, leading to comparable or superior performance to most monolingual models.

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