Hate speech in online social media

2020 ◽  
pp. 1-8
Author(s):  
Mithun Das ◽  
Binny Mathew ◽  
Punyajoy Saha ◽  
Pawan Goyal ◽  
Animesh Mukherjee
Keyword(s):  
Social Media ◽  
Hate Speech ◽  
Online Social Media

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.

scholarly journals A Word Cloud Model based on Hate Speech in an Online Social Media Environment

2021 ◽  
Vol 18 (2(Suppl.)) ◽  
pp. 0937
Author(s):  
Valentina Ibrahim ◽  
Juhaid Abu Bakar ◽  
Nor Hazlyna Harun ◽  
Alaa Fareed Abdulateef
Keyword(s):  
Social Media ◽  
Hate Speech ◽  
Cloud Model ◽  
Model Development ◽  
Text Documents ◽  
Media Environment ◽  
Word Cloud ◽  
Model Based ◽  
Online Social Media ◽  
Word Clouds

Social media is known as detectors platform that are used to measure the activities of the users in the real world. However, the huge and unfiltered feed of messages posted on social media trigger social warnings, particularly when these messages contain hate speech towards specific individual or community. The negative effect of these messages on individuals or the society at large is of great concern to governments and non-governmental organizations. Word clouds provide a simple and efficient means of visually transferring the most common words from text documents. This research aims to develop a word cloud model based on hateful words on online social media environment such as Google News. Several steps are involved including data acquisition and pre-processing, feature extraction, model development, visualization and viewing of word cloud model result. The results present an image in a series of text describing the top words. This model can be considered as a simple way to exchange high-level information without overloading the user's details.

scholarly journals Hate Speech Detection using Modified Principal Component Analysis and Enhanced Convolution Neural Network on Twitter Dataset

2021 ◽  
Author(s):  
Majed Alowaidi
Keyword(s):  
Neural Network ◽  
Social Media ◽  
Principal Component Analysis ◽  
Feature Extraction ◽  
Social Networking ◽  
Hate Speech ◽  
Principal Component ◽  
Extraction Process ◽  
Online Social Media ◽  
The Given

Abstract Online social media are increasingly catching people’s eye among users of the Internet. Services provided by social networking vendors like Twitter and Facebook are very attractive, with widespread proliferation among internet users. As a downside of their predominance in the domain of social networking, Twitter and Facebook are frequently pestered with the problem of handling offensive, threat, fake, hate words. One of the major problems, apparent in online social media, is the toxic online content. In the existing system, the methods are not dealt with large dataset. Also the feature extraction method is not efficient to extract important features in the given dataset. To overcome the above mentioned issues, in this work, Modified Principal Component Analysis (MPCA) and Enhanced Convolution Neural Network (ECNN) is proposed. Natural Language Processing (NLP) is implemented to build an automatic system through the inclusion of syntactic and semantic analysis. This work contains main phases are such as pre-processing, feature extraction and classification process. The pre-processing is done by using normalization method which is used to remove the white spaces, replace the consecutive exclamation and question marks, and eliminate stop words. These preprocessed features are taken into feature extraction process. MPCA algorithm is applied to perform feature extraction process. It uses set of correlated features and extracts more informative features for the given dataset. Then the classification algorithm is proposed to detect the hate speech or abusive languages. ECNN is proposed to classify hate and non-hate from the online content more accurately. It takes many inputs and generates output with minimum amount of time with higher accuracy for larger dataset. Thus, the result concludes that the proposed MPCA+ECNN algorithm provides higher accuracy, precision, recall and F-measure values rather than the existing methods.

Dynamics of Attention and Public Opinion in Social Media

2019 ◽  
pp. 377-397
Author(s):  
Emilio Ferrara
Keyword(s):  
Social Media ◽  
Hate Speech ◽  
Social Systems ◽  
Group Behavior ◽  
Opinion Formation ◽  
Communication Behaviors ◽  
Research Perspective ◽  
Online Social Media ◽  
Political Conversation ◽  
Media Environments

Sociotechnical systems such as online social media play a central role in today’s society, connecting millions of people all over the world. From a research perspective, a multitude of studies have gathered data from these environments to frame and unveil major questions on human social behavior. The goal of this chapter is to overview some theory of social communication behaviors, attention, and opinion formation in techno-social systems and their effects on individuals’ and group behavior. Examples of data-driven studies on large social media illustrate how users of techno-social systems behave during social protests, how they engage in political conversation, how they contribute to diffuse misinformation, and how they react to the spreading of fear and panic during a crisis. A quantitative analysis of the Twitter conversation during two global events (the Gezi Park protest in Turkey and the 2014 Ebola crisis) is presented as a case study to illustrate these phenomena. Understanding the dynamics of attention and opinion formation online allows people to build safer social media environments, hindering the spread of misinformation campaigns, hate speech, and stigmas.

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.

Impact of Online Social Media on Consumer Decision Making: A Study in Dehradun

2012 ◽  
Vol 3 (5) ◽  
pp. 379-381
Author(s):  
Dr. Aruna Kumar Mishra ◽  
◽  
Narendra Kumar Narendra Kumar ◽  
Abhishek Sharma
Keyword(s):  
Decision Making ◽  
Social Media ◽  
Consumer Decision Making ◽  
Consumer Decision ◽  
Online Social Media

Pembelajaran Integrasi tentang Etika Penggunaan Media Sosial dalam Materi Ajar Pendidikan Agama Islam di Sekolah Menangah Atas

2019 ◽  
Vol 3 (1) ◽  
pp. 72
Author(s):  
Irfan Afandi
Keyword(s):  
Social Media ◽  
Industrial Revolution ◽  
Hate Speech ◽  
Islamic Education ◽  
Learning Models ◽  
Negative Effects ◽  
Education Material ◽  
Use Of Social Media ◽  
The Waves ◽  
Critical Reason

The humanitarian problem in the development of the industrial revolution 4.0 is very complex and is at the stage of worrying. No human being separated from the effect of the waves. High school is active users (user) of the results of the industrial revolution the 4.0. The problem that arises in the use of social media including the demise of expertise, the dissemination of hate speech and fabricated news. Teaching Islamic education material should be able to respond to this by providing normative information in the Qur'an and Hadith so that students can escape from its negative effects. One of the solutions offered was to integrate these materials with integratsi learning models in the themes that have been arranged in the school's learning policy. Integrating this material must through the phases between the awarding phase of learning, information or materials to grow a critical reason, generate hypotheses and generalities.

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