scholarly journals A BERT-Based Transfer Learning Approach for Hate Speech Detection in Online Social Media

2019 ◽  
pp. 928-940 ◽  
Author(s):  
Marzieh Mozafari ◽  
Reza Farahbakhsh ◽  
Noël Crespi
Keyword(s):  
Social Media ◽  
Transfer Learning ◽  
Hate Speech ◽  
Learning Approach ◽  
Speech Detection ◽  
Online Social Media

scholarly journals Multimodal Hate Speech Detection in Greek Social Media

2021 ◽  
Vol 5 (7) ◽  
pp. 34
Author(s):  
Konstantinos Perifanos ◽  
Dionysis Goutsos
Keyword(s):  
Social Media ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Hate Speech ◽  
Language Model ◽  
Fine Tuning ◽  
Accuracy Score ◽  
Speech Detection ◽  
Online Social Media

Hateful and abusive speech presents a major challenge for all online social media platforms. Recent advances in Natural Language Processing and Natural Language Understanding allow for more accurate detection of hate speech in textual streams. This study presents a new multimodal approach to hate speech detection by combining Computer Vision and Natural Language processing models for abusive context detection. Our study focuses on Twitter messages and, more specifically, on hateful, xenophobic, and racist speech in Greek aimed at refugees and migrants. In our approach, we combine transfer learning and fine-tuning of Bidirectional Encoder Representations from Transformers (BERT) and Residual Neural Networks (Resnet). Our contribution includes the development of a new dataset for hate speech classification, consisting of tweet IDs, along with the code to obtain their visual appearance, as they would have been rendered in a web browser. We have also released a pre-trained Language Model trained on Greek tweets, which has been used in our experiments. We report a consistently high level of accuracy (accuracy score = 0.970, f1-score = 0.947 in our best model) in racist and xenophobic speech detection.

scholarly journals Ensemble-based Semi-Supervised Learning for Hate Speech Detection

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Safa Alsafari
Keyword(s):  
Social Media ◽  
Supervised Learning ◽  
Hate Speech ◽  
Classification Performance ◽  
Media Content ◽  
Learning Approach ◽  
Classification Methods ◽  
Speech Detection ◽  
Speech Classification

Large and accurately labeled textual corpora are vital to developing efficient hate speech classifiers. This paper introduces an ensemble-based semi-supervised learning approach to leverage the availability of abundant social media content. Starting with a reliable hate speech dataset, we train and test diverse classifiers that are then used to label a corpus of one million tweets. Next, we investigate several strategies to select the most confident labels from the obtained pseudo labels. We assess these strategies by re-training all the classifiers with the seed dataset augmented with the trusted pseudo-labeled data. Finally, we demonstrate that our approach improves classification performance over supervised hate speech classification methods.

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.

scholarly journals Quarantining online hate speech: technical and ethical perspectives

2019 ◽  
Vol 22 (1) ◽  
pp. 69-80 ◽  
Author(s):  
Stefanie Ullmann ◽  
Marcus Tomalin
Keyword(s):  
Hate Speech ◽  
Service Providers ◽  
Freedom Of Expression ◽  
Computer Software ◽  
Speech Detection ◽  
Detection Systems ◽  
Online Social Media ◽  
Psychological Harm ◽  
Social Media Platforms ◽  
Harmful Content

Abstract In this paper we explore quarantining as a more ethical method for delimiting the spread of Hate Speech via online social media platforms. Currently, companies like Facebook, Twitter, and Google generally respond reactively to such material: offensive messages that have already been posted are reviewed by human moderators if complaints from users are received. The offensive posts are only subsequently removed if the complaints are upheld; therefore, they still cause the recipients psychological harm. In addition, this approach has frequently been criticised for delimiting freedom of expression, since it requires the service providers to elaborate and implement censorship regimes. In the last few years, an emerging generation of automatic Hate Speech detection systems has started to offer new strategies for dealing with this particular kind of offensive online material. Anticipating the future efficacy of such systems, the present article advocates an approach to online Hate Speech detection that is analogous to the quarantining of malicious computer software. If a given post is automatically classified as being harmful in a reliable manner, then it can be temporarily quarantined, and the direct recipients can receive an alert, which protects them from the harmful content in the first instance. The quarantining framework is an example of more ethical online safety technology that can be extended to the handling of Hate Speech. Crucially, it provides flexible options for obtaining a more justifiable balance between freedom of expression and appropriate censorship.

Rumors on the Net: A Brackish Suspension of Speech and Hate

2019 ◽  
pp. 174387211988012 ◽  
Author(s):  
Anne Wagner ◽  
Sarah Marusek
Keyword(s):  
United States ◽  
Social Media ◽  
Hate Speech ◽  
Public Memory ◽  
The United States ◽  
Online Social Media ◽  
Cyber Culture ◽  
Social Media Platforms ◽  
The Impact ◽  
Mob Mentality

The legitimacy of public memory and socially normative standards of civility is questioned through rumors that abound on online social media platforms. On the Net, the proclivity of rumors is particularly prone to acts of bullying and frameworks of hate speech. Legislative attempts to limit rumors operate differently in France and throughout Europe from the United States. This article examines the impact of online rumors, the mob mentality, and the politicization of bullying critics within a cyber culture that operates within the limitations of law.

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