scholarly journals A Large-Scale Dataset for Hate Speech Detection on Vietnamese Social Media Texts

2021 ◽  
pp. 415-426
Author(s):  
Son T. Luu ◽  
Kiet Van Nguyen ◽  
Ngan Luu-Thuy Nguyen
Keyword(s):  
Social Media ◽  
Large Scale ◽  
Hate Speech ◽  
Speech Detection ◽  
Large Scale Dataset ◽  
Media Texts

scholarly journals Ethical and technical challenges of AI in tackling hate speech

2021 ◽  
Vol 29 ◽  
Author(s):  
Diogo Cortiz ◽  
Arkaitz Zubiaga
Keyword(s):  
Social Media ◽  
Large Scale ◽  
Information Overload ◽  
Scientific Literature ◽  
Hate Speech ◽  
Freedom Of Expression ◽  
Speech Detection ◽  
Detection Algorithms ◽  
Technical Challenges

In this paper, we discuss some of the ethical and technical challenges of using Artificial Intelligence for online content moderation. As a case study, we used an AI model developed to detect hate speech on social networks, a concept for which varying definitions are given in the scientific literature and consensus is lacking. We argue that while AI can play a central role in dealing with information overload on social media, it could cause risks of violating freedom of expression (if the project is not well conducted). We present some ethical and technical challenges involved in the entire pipeline of an AI project - from data collection to model evaluation - that hinder the large-scale use of hate speech detection algorithms. Finally, we argue that AI can assist with the detection of hate speech in social media, provided that the final judgment about the content has to be made through a process with human involvement.

scholarly journals A Web Interface for Analyzing Hate Speech

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.

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

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.

scholarly journals Hate speech detection and racial bias mitigation in social media based on BERT model

PLoS ONE ◽  
2020 ◽  
Vol 15 (8) ◽  
pp. e0237861
Author(s):  
Marzieh Mozafari ◽  
Reza Farahbakhsh ◽  
Noël Crespi
Keyword(s):  
Social Media ◽  
Hate Speech ◽  
Racial Bias ◽  
Speech Detection

scholarly journals Time of Your Hate: The Challenge of Time in Hate Speech Detection on Social Media

2020 ◽  
Vol 10 (12) ◽  
pp. 4180 ◽  
Author(s):  
Komal Florio ◽  
Valerio Basile ◽  
Marco Polignano ◽  
Pierpaolo Basile ◽  
Viviana Patti
Keyword(s):  
Social Media ◽  
Hate Speech ◽  
Time Window ◽  
Classification Performance ◽  
Fine Tuning ◽  
Classification Model ◽  
Temporal Distance ◽  
Speech Detection ◽  
Highly Sensitive

The availability of large annotated corpora from social media and the development of powerful classification approaches have contributed in an unprecedented way to tackle the challenge of monitoring users’ opinions and sentiments in online social platforms across time. Such linguistic data are strongly affected by events and topic discourse, and this aspect is crucial when detecting phenomena such as hate speech, especially from a diachronic perspective. We address this challenge by focusing on a real case study: the “Contro l’odio” platform for monitoring hate speech against immigrants in the Italian Twittersphere. We explored the temporal robustness of a BERT model for Italian (AlBERTo), the current benchmark on non-diachronic detection settings. We tested different training strategies to evaluate how the classification performance is affected by adding more data temporally distant from the test set and hence potentially different in terms of topic and language use. Our analysis points out the limits that a supervised classification model encounters on data that are heavily influenced by events. Our results show how AlBERTo is highly sensitive to the temporal distance of the fine-tuning set. However, with an adequate time window, the performance increases, while requiring less annotated data than a traditional classifier.

Prototype Application Hate Speech Detection Website Using String Matching and Searching Algorithm

2018 ◽  
Vol 7 (2.5) ◽  
pp. 62 ◽  
Author(s):  
Nuning Kurniasih ◽  
Leon Andretti Abdillah ◽  
I Ketut Sudarsana ◽  
I Wayan Lali Yogantara ◽  
I Nyoman Temon Astawa ◽  
...  
Keyword(s):  
Social Media ◽  
Hate Speech ◽  
String Matching ◽  
Media Content ◽  
Speech Detection

Hate speech is now a problem for social media users such as Facebook, Twitter, Whatsapp and also Telegram. The current social media users are also a lot to post, share the content both consciously and unconsciously to various social media as well as even some hate speech postings are shared by irresponsible parties to gain profit from the chaos that he created, denigrating religion, vilify certain individuals even as an act of provocation. Prototype hate speech detection application created to detect hate speech on Facebook and it can give notification to users to be more aware of social media content and also careful in reading, share content that can trigger unpleasant actions.  

scholarly journals Depression Detection via Harvesting Social Media: A Multimodal Dictionary Learning Solution

2017 ◽  
Author(s):  
Guangyao Shen ◽  
Jia Jia ◽  
Liqiang Nie ◽  
Fuli Feng ◽  
Cunjun Zhang ◽  
...  
Keyword(s):  
Social Media ◽  
Dictionary Learning ◽  
Large Scale ◽  
Clinical Depression ◽  
Daily Lives ◽  
Face To Face ◽  
Large Scale Dataset ◽  
Depression Detection ◽  
Series Of Experiments ◽  
Global Burden Of Diseases

Depression is a major contributor to the overall global burden of diseases. Traditionally, doctors diagnose depressed people face to face via referring to clinical depression criteria. However, more than 70% of the patients would not consult doctors at early stages of depression, which leads to further deterioration of their conditions. Meanwhile, people are increasingly relying on social media to disclose emotions and sharing their daily lives, thus social media have successfully been leveraged for helping detect physical and mental diseases. Inspired by these, our work aims to make timely depression detection via harvesting social media data. We construct well-labeled depression and non-depression dataset on Twitter, and extract six depression-related feature groups covering not only the clinical depression criteria, but also online behaviors on social media. With these feature groups, we propose a multimodal depressive dictionary learning model to detect the depressed users on Twitter. A series of experiments are conducted to validate this model, which outperforms (+3% to +10%) several baselines. Finally, we analyze a large-scale dataset on Twitter to reveal the underlying online behaviors between depressed and non-depressed users.

scholarly journals Hate Speech Detection for Indonesia Tweets Using Word Embedding And Gated Recurrent Unit

2019 ◽  
Vol 13 (1) ◽  
pp. 43
Author(s):  
Junanda Patihullah ◽  
Edi Winarko
Keyword(s):  
Social Media ◽  
Support Vector Machine ◽  
Feature Extraction ◽  
Deep Learning ◽  
Hate Speech ◽  
Support Vector ◽  
Speech Detection ◽  
The People ◽  
Gated Recurrent Unit ◽  
Rule Out

Social media has changed the people mindset to express thoughts and moods. As the activity of social media users increases, it does not rule out the possibility of crimes of spreading hate speech can spread quickly and widely. So that it is not possible to detect hate speech manually. GRU is one of the deep learning methods that has the ability to learn information relations from the previous time to the present time. In this research feature extraction used is word2vec, because it has the ability to learn semantics between words. In this research the GRU performance will be compared with other supervision methods such as support vector machine, naive bayes, decision tree and logistic regression. The results obtained show that the best accuracy is 92.96% by the GRU model with word2vec feature extraction. The use of word2vec in the comparison supervision method is not good enough from tf and tf-idf.

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