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.

scholarly journals Emotionally Informed Hate Speech Detection: A Multi-target Perspective

2021 ◽  
Author(s):  
Patricia Chiril ◽  
Endang Wahyu Pamungkas ◽  
Farah Benamara ◽  
Véronique Moriceau ◽  
Viviana Patti
Keyword(s):  
Hate Speech ◽  
Binary Classification ◽  
Online Communication ◽  
Vulnerable Groups ◽  
Speech Detection ◽  
Social Media Platforms ◽  
Sentic Computing ◽  
Specific Manifestation ◽  
The Impact ◽  
First Time

AbstractHate Speech and harassment are widespread in online communication, due to users' freedom and anonymity and the lack of regulation provided by social media platforms. Hate speech is topically focused (misogyny, sexism, racism, xenophobia, homophobia, etc.), and each specific manifestation of hate speech targets different vulnerable groups based on characteristics such as gender (misogyny, sexism), ethnicity, race, religion (xenophobia, racism, Islamophobia), sexual orientation (homophobia), and so on. Most automatic hate speech detection approaches cast the problem into a binary classification task without addressing either the topical focus or the target-oriented nature of hate speech. In this paper, we propose to tackle, for the first time, hate speech detection from a multi-target perspective. We leverage manually annotated datasets, to investigate the problem of transferring knowledge from different datasets with different topical focuses and targets. Our contribution is threefold: (1) we explore the ability of hate speech detection models to capture common properties from topic-generic datasets and transfer this knowledge to recognize specific manifestations of hate speech; (2) we experiment with the development of models to detect both topics (racism, xenophobia, sexism, misogyny) and hate speech targets, going beyond standard binary classification, to investigate how to detect hate speech at a finer level of granularity and how to transfer knowledge across different topics and targets; and (3) we study the impact of affective knowledge encoded in sentic computing resources (SenticNet, EmoSenticNet) and in semantically structured hate lexicons (HurtLex) in determining specific manifestations of hate speech. We experimented with different neural models including multitask approaches. Our study shows that: (1) training a model on a combination of several (training sets from several) topic-specific datasets is more effective than training a model on a topic-generic dataset; (2) the multi-task approach outperforms a single-task model when detecting both the hatefulness of a tweet and its topical focus in the context of a multi-label classification approach; and (3) the models incorporating EmoSenticNet emotions, the first level emotions of SenticNet, a blend of SenticNet and EmoSenticNet emotions or affective features based on Hurtlex, obtained the best results. Our results demonstrate that multi-target hate speech detection from existing datasets is feasible, which is a first step towards hate speech detection for a specific topic/target when dedicated annotated data are missing. Moreover, we prove that domain-independent affective knowledge, injected into our models, helps finer-grained hate speech detection.

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.

Arabic Offensive and Hate Speech Detection Using a Cross-Corpora Multi-Task Learning Model

Informatics ◽  
2021 ◽  
Vol 8 (4) ◽  
pp. 69
Author(s):  
Wassen Aldjanabi ◽  
Abdelghani Dahou ◽  
Mohammed A. A. Al-qaness ◽  
Mohamed Abd Elaziz ◽  
Ahmed Mohamed Helmi ◽  
...  
Keyword(s):  
Social Media ◽  
Hate Speech ◽  
Detection System ◽  
Language Model ◽  
Arabic Language ◽  
Social Phenomena ◽  
Speech Detection ◽  
Task Learning ◽  
Opinion Expression ◽  
Social Media Platforms

As social media platforms offer a medium for opinion expression, social phenomena such as hatred, offensive language, racism, and all forms of verbal violence have increased spectacularly. These behaviors do not affect specific countries, groups, or communities only, extending beyond these areas into people’s everyday lives. This study investigates offensive and hate speech on Arab social media to build an accurate offensive and hate speech detection system. More precisely, we develop a classification system for determining offensive and hate speech using a multi-task learning (MTL) model built on top of a pre-trained Arabic language model. We train the MTL model on the same task using cross-corpora representing a variation in the offensive and hate context to learn global and dataset-specific contextual representations. The developed MTL model showed a significant performance and outperformed existing models in the literature on three out of four datasets for Arabic offensive and hate speech detection tasks.

scholarly journals Targets of Online Hate Speech in Context

Intersections ◽  
2019 ◽  
Vol 4 (4) ◽  
Author(s):  
Radu Mihai Meza ◽  
Hanna-Orsolya Vincze ◽  
Andreea Mogos
Keyword(s):  
News Media ◽  
Hate Speech ◽  
Freedom Of Expression ◽  
Political Agenda ◽  
Political Debate ◽  
Current Events ◽  
Social Media Platforms ◽  
The Media ◽  
Facebook Pages ◽  
Discursive Patterns

Online hate speech, especially on social media platforms, is the subject of both policy and political debate in Europe and globally - from the fragmentation of network publics to echo chambers and bubble phenomena, from networked outrage to networked populism, from trolls and bullies to propaganda and non-linear cyberwarfare. Both researchers and Facebook Community standards see the identification of the potential targets of hateful or antagonistic speech as key to classifying and distinguishing the latter from arguments that represent political viewpoints protected by freedom of expression rights. This research is an exploratory analysis of mentions of targets of hate speech in comments in the context of 106 public Facebook pages in Romanian and Hungarian from January 2015 to December 2017. A total of 1.8 million comments were collected through API interrogation and analyzed using a text-mining niche-dictionaries approach and co-occurrence analysis to reveal connections to events on the media and political agenda and discursive patterns. Findings indicate that in both countries the most prominent targets mentioned are connected to current events on the political and media agenda, that targets are most frequently mentioned in contexts created by politicians and news media, and that discursive patterns in both countries involve the proliferation of similar stereotypes about certain target groups.

scholarly journals Democratic Discourse in the Digital Public Sphere: Re-imagining Copyright Enforcement on Online Social Media Platforms

2021 ◽  
pp. 41-46
Author(s):  
Sunimal Mendis
Keyword(s):  
Service Providers ◽  
Legal Framework ◽  
Planning Theory ◽  
Content Sharing ◽  
Online Social Media ◽  
The Social ◽  
Social Media Platforms ◽  
Democratic Discourse ◽  
Copyright Enforcement

AbstractWithin the current European Union (EU) online copyright enforcement regime—of which Article 17 of the Copyright in the Digital Single Market Directive [2019] constitutes the seminal legal provision—the role of online content-sharing service providers (OCSSPs) is limited to ensuring that copyright owners obtain fair remuneration for content shared over their platforms (role of “content distributors”) and preventing unauthorized uses of copyright-protected content (“Internet police”). Neither role allows for a recognition of OCSSPs’ role as facilitators of democratic discourse and the duty incumbent on them to ensure that users’ freedom to engage in democratic discourse are preserved. This chapter proposes a re-imagining of the EU legal framework on online copyright enforcement—using the social planning theory of copyright law as a normative framework—to increase its fitness for preserving and promoting copyright law’s democracy-enhancing function.

scholarly journals Power, Hate Speech, Machine Learning, and Intersectional Approach

2021 ◽  
Author(s):  
Jae Yeon Kim
Keyword(s):  
Machine Learning ◽  
Group Dynamics ◽  
Hate Speech ◽  
Machine Learning Algorithms ◽  
Digital Content ◽  
Causal Mechanism ◽  
Power Dynamics ◽  
Speech Detection ◽  
Detection Systems ◽  
Racial Ethnic

The advent of social media has increased digital content and, with it, hate speech. Advancements in machine learning algorithms help detect online hate speech at scale; nevertheless, these systems are far from perfect. Human-annotated hate speech data, used to train automated hate speech detection systems, is susceptible to racial/ethnic, gender, and other bias. To address societal and historical biases in automated hate speech detection, scholars and practitioners need to focus on the power dynamics: who decides what comprises hate speech. Examining inter- and intra-group dynamics can facilitate understanding of this causal mechanism. This intersectional approach deepens knowledge of the limitations of automated hate speech detection systems and bridges social science and machine learning literature on biases and fairness.

scholarly journals Why Harmful Posts on Social Media should be regulated

2021 ◽  
Vol 10 (2) ◽  
pp. 241-258
Author(s):  
Maryam Abu-Sharida
Keyword(s):  
Social Media ◽  
Freedom Of Expression ◽  
The United States ◽  
American Constitution ◽  
Negative Effects ◽  
The People ◽  
The Social ◽  
Social Media Platforms ◽  
Adults And Children ◽  
Harmful Content

Harmful content over the internet is going viral nowadays on most of the social media platforms, which has negative effects on both adults and children, especially, with the increasing usage of social media tools during the Covid-19 situation. Therefore, social media’s harmful posts should be regulated. Through the recent legislative efforts, societies are still suffering from the influence of these posts. We observe that the people who share harmful posts often hide behind the First Amendment right and the Freedom of Expression of the American Constitution. This paper focuses on suggesting possible regulations to strike down social media’s harmful content regardless of the platforms it was posted on, to safeguard society from their negative effects. In addition, it highlights the attempts by Qatar’s government to regulate social media crimes and aims to assess if these efforts are enough. Also, it will take a general look at the situation in the United States and how it is dealing with this issue.

scholarly journals Understanding international perceptions of the severity of harmful content online

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0256762
Author(s):  
Jialun Aaron Jiang ◽  
Morgan Klaus Scheuerman ◽  
Casey Fiesler ◽  
Jed R. Brubaker
Keyword(s):  
Social Media ◽  
Status Quo ◽  
International Survey ◽  
Perceived Severity ◽  
Online Social Media ◽  
Internet Users ◽  
The World ◽  
Social Media Platforms ◽  
Harmful Content ◽  
Similarities And Differences

Online social media platforms constantly struggle with harmful content such as misinformation and violence, but how to effectively moderate and prioritize such content for billions of global users with different backgrounds and values presents a challenge. Through an international survey with 1,696 internet users across 8 different countries across the world, this empirical study examines how international users perceive harmful content online and the similarities and differences in their perceptions. We found that across countries, the perceived severity consistently followed an exponential growth as the harmful content became more severe, but what harmful content were perceived as more or less severe varied significantly. Our results challenge platform content moderation’s status quo of using a one-size-fits-all approach to govern international users, and provide guidance on how platforms may wish to prioritize and customize their moderation of harmful content.

scholarly journals ETHOS: a multi-label hate speech detection dataset

2022 ◽  
Author(s):  
Ioannis Mollas ◽  
Zoe Chrysopoulou ◽  
Stamatis Karlos ◽  
Grigorios Tsoumakas
Keyword(s):  
Hate Speech ◽  
User Interaction ◽  
Sampling Procedure ◽  
Daily Lives ◽  
Speech Detection ◽  
Ethical Concerns ◽  
Social Media Platforms ◽  
High Level ◽  
Social Profiles ◽  
Detection Mechanisms

AbstractOnline hate speech is a recent problem in our society that is rising at a steady pace by leveraging the vulnerabilities of the corresponding regimes that characterise most social media platforms. This phenomenon is primarily fostered by offensive comments, either during user interaction or in the form of a posted multimedia context. Nowadays, giant corporations own platforms where millions of users log in every day, and protection from exposure to similar phenomena appears to be necessary to comply with the corresponding legislation and maintain a high level of service quality. A robust and reliable system for detecting and preventing the uploading of relevant content will have a significant impact on our digitally interconnected society. Several aspects of our daily lives are undeniably linked to our social profiles, making us vulnerable to abusive behaviours. As a result, the lack of accurate hate speech detection mechanisms would severely degrade the overall user experience, although its erroneous operation would pose many ethical concerns. In this paper, we present ‘ETHOS’ (multi-labEl haTe speecH detectiOn dataSet), a textual dataset with two variants: binary and multi-label, based on YouTube and Reddit comments validated using the Figure-Eight crowdsourcing platform. Furthermore, we present the annotation protocol used to create this dataset: an active sampling procedure for balancing our data in relation to the various aspects defined. Our key assumption is that, even gaining a small amount of labelled data from such a time-consuming process, we can guarantee hate speech occurrences in the examined material.

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