scholarly journals Gaming Algorithmic Hate-Speech Detection: Stakes, Parties, and Moves

2020 ◽  
Vol 6 (2) ◽  
pp. 205630512092477
Author(s):  
Jesse Haapoja ◽  
Salla-Maaria Laaksonen ◽  
Airi Lampinen
Keyword(s):  
Hate Speech ◽  
Detection Algorithm ◽  
Political Candidates ◽  
Speech Detection ◽  
Media Messages ◽  
Machine Learning Model ◽  
Multi Stakeholder ◽  
Municipal Election ◽  
Game Metaphor ◽  
Different Levels

A recent strand of research considers how algorithmic systems are gamed in everyday encounters. We add to this literature with a study that uses the game metaphor to examine a project where different organizations came together to create and deploy a machine learning model to detect hate speech from political candidates’ social media messages during the Finnish 2017 municipal election. Using interviews and forum discussions as our primary research material, we illustrate how the unfolding game is played out on different levels in a multi-stakeholder situation, what roles different participants have in the game, and how strategies of gaming the model revolve around controlling the information available to it. We discuss strategies that different stakeholders planned or used to resist the model, and show how the game is not only played against the model itself, but also with those who have created it and those who oppose it. Our findings illustrate that while “gaming the system” is an important part of gaming with algorithms, these games have other levels where humans play against each other, rather than against technology. We also draw attention to how deploying a hate-speech detection algorithm can be understood as an effort to not only detect but also preempt unwanted behavior.

scholarly journals Dynamics of online hate and misinformation

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Matteo Cinelli ◽  
Andraž Pelicon ◽  
Igor Mozetič ◽  
Walter Quattrociocchi ◽  
Petra Kralj Novak ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hate Speech ◽  
Learning Model ◽  
Large Set ◽  
Speech Detection ◽  
Machine Learning Model ◽  
Echo Chamber ◽  
Youtube Videos

AbstractOnline debates are often characterised by extreme polarisation and heated discussions among users. The presence of hate speech online is becoming increasingly problematic, making necessary the development of appropriate countermeasures. In this work, we perform hate speech detection on a corpus of more than one million comments on YouTube videos through a machine learning model, trained and fine-tuned on a large set of hand-annotated data. Our analysis shows that there is no evidence of the presence of “pure haters”, meant as active users posting exclusively hateful comments. Moreover, coherently with the echo chamber hypothesis, we find that users skewed towards one of the two categories of video channels (questionable, reliable) are more prone to use inappropriate, violent, or hateful language within their opponents’ community. Interestingly, users loyal to reliable sources use on average a more toxic language than their counterpart. Finally, we find that the overall toxicity of the discussion increases with its length, measured both in terms of the number of comments and time. Our results show that, coherently with Godwin’s law, online debates tend to degenerate towards increasingly toxic exchanges of views.

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

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%).

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.

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