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.

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 Digital Public Sphere and Geography: The Influence of Physical Location on Twitter’s Political Conversation

2020 ◽  
Vol 8 (4) ◽  
pp. 96-106 ◽  
Author(s):  
Andreu Casero-Ripollés ◽  
Josep-Lluís Micó-Sanz ◽  
Míriam Díez-Bosch
Keyword(s):  
Social Media ◽  
Public Sphere ◽  
Hate Speech ◽  
Geographical Location ◽  
Negotiation Process ◽  
Machine Learning Techniques ◽  
The Political ◽  
Political Debate ◽  
Political Conversation ◽  
The Government

Social media has instituted new parameters for the political conversation in the digital public sphere. Previous research had identified several of these new phenomena: political polarisation, hate speech discourses, and fake news, among others. However, little attention has been paid to the users’ geographical location, specifically to the role location plays in political discussion on social media, and to its further implications in the digital public sphere. A priori, we might think that on the digital landscape geographical restrictions no longer condition political debate, allowing increasingly diverse users to participate in, and influence, the discussion. To analyse this, machine learning techniques were used to study Twitter’s political conversation about the negotiation process for the formation of the government in Spain that took place between 2015 and 2016. A big data sample of 127,3 million tweets associated with three Spanish cities (Madrid, Barcelona, and Valencia) was used. The results show that the geographical location of the users directly affects the political conversation on Twitter, despite the dissolution of the physical restrictions that the online environment favours. Demographics, cultural factors, and proximity to the centres of political power are factors conditioning the structure of digital political debate. These findings are a novel contribution to the design of more effective political campaigns and strategies, and provide a better understanding of the dynamics of the digital public sphere provided by Twitter.

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.

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

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.

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