Folk classification of social media services as grounds for explaining or predicting trends in use

2014 ◽  
Author(s):  
Gilbert Wilkes ◽  
Brian Trayor ◽  
Jaigris Hodson
Keyword(s):  
Social Media ◽  
Folk Classification ◽  
Media Services

scholarly journals Classification of unlabeled online media

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sakthi Kumar Arul Prakash ◽  
Conrad Tucker
Keyword(s):  
Social Media ◽  
Real World ◽  
Graphical Model ◽  
Ground Truth ◽  
Classification Problem ◽  
Machine Learning Algorithms ◽  
Social Media Networks ◽  
Online Social Media ◽  
Wide Range

AbstractThis work investigates the ability to classify misinformation in online social media networks in a manner that avoids the need for ground truth labels. Rather than approach the classification problem as a task for humans or machine learning algorithms, this work leverages user–user and user–media (i.e.,media likes) interactions to infer the type of information (fake vs. authentic) being spread, without needing to know the actual details of the information itself. To study the inception and evolution of user–user and user–media interactions over time, we create an experimental platform that mimics the functionality of real-world social media networks. We develop a graphical model that considers the evolution of this network topology to model the uncertainty (entropy) propagation when fake and authentic media disseminates across the network. The creation of a real-world social media network enables a wide range of hypotheses to be tested pertaining to users, their interactions with other users, and with media content. The discovery that the entropy of user–user and user–media interactions approximate fake and authentic media likes, enables us to classify fake media in an unsupervised learning manner.

scholarly journals A Novel Co-Training-Based Approach for the Classification of Mental Illnesses Using Social Media Posts

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 166165-166172
Author(s):  
Subhan Tariq ◽  
Nadeem Akhtar ◽  
Humaira Afzal ◽  
Shahzad Khalid ◽  
Muhammad Rafiq Mufti ◽  
...  
Keyword(s):  
Social Media ◽  
Mental Illnesses

scholarly journals Automated Classification of Fake News Spreaders to Break the Misinformation Chain

Information ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 248
Author(s):  
Simone Leonardi ◽  
Giuseppe Rizzo ◽  
Maurizio Morisio
Keyword(s):  
Social Media ◽  
Network Architecture ◽  
Diffusion Mechanism ◽  
Automated Classification ◽  
Fake News ◽  
Neural Network Architecture ◽  
User Classification ◽  
The Social ◽  
Network Topologies

In social media, users are spreading misinformation easily and without fact checking. In principle, they do not have a malicious intent, but their sharing leads to a socially dangerous diffusion mechanism. The motivations behind this behavior have been linked to a wide variety of social and personal outcomes, but these users are not easily identified. The existing solutions show how the analysis of linguistic signals in social media posts combined with the exploration of network topologies are effective in this field. These applications have some limitations such as focusing solely on the fake news shared and not understanding the typology of the user spreading them. In this paper, we propose a computational approach to extract features from the social media posts of these users to recognize who is a fake news spreader for a given topic. Thanks to the CoAID dataset, we start the analysis with 300 K users engaged on an online micro-blogging platform; then, we enriched the dataset by extending it to a collection of more than 1 M share actions and their associated posts on the platform. The proposed approach processes a batch of Twitter posts authored by users of the CoAID dataset and turns them into a high-dimensional matrix of features, which are then exploited by a deep neural network architecture based on transformers to perform user classification. We prove the effectiveness of our work by comparing the precision, recall, and f1 score of our model with different configurations and with a baseline classifier. We obtained an f1 score of 0.8076, obtaining an improvement from the state-of-the-art by 4%.

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.

Polarity Classification of Arabic Sentiments

2016 ◽  
Vol 11 (3) ◽  
pp. 32-49 ◽  
Author(s):  
Mohammed N. Al-Kabi ◽  
Heider A. Wahsheh ◽  
Izzat M. Alsmadi
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Operating Characteristic ◽  
Opinion Mining ◽  
Online Social Network ◽  
The Social ◽  
Polarity Classification ◽  
Arabic Sentiment Analysis ◽  
Modern Standard

Sentiment Analysis/Opinion Mining is associated with social media and usually aims to automatically identify the polarities of different points of views of the users of the social media about different aspects of life. The polarity of a sentiment reflects the point view of its author about a certain issue. This study aims to present a new method to identify the polarity of Arabic reviews and comments whether they are written in Modern Standard Arabic (MSA), or one of the Arabic Dialects, and/or include Emoticons. The proposed method is called Detection of Arabic Sentiment Analysis Polarity (DASAP). A modest dataset of Arabic comments, posts, and reviews is collected from Online social network websites (i.e. Facebook, Blogs, YouTube, and Twitter). This dataset is used to evaluate the effectiveness of the proposed method (DASAP). Receiver Operating Characteristic (ROC) prediction quality measurements are used to evaluate the effectiveness of DASAP based on the collected dataset.

Social Media Handbuch

2021 ◽  
Keyword(s):  
Social Media ◽  
Data Processing ◽  
Business Models ◽  
Practical Application ◽  
Strategy Model ◽  
The Social ◽  
Platform Economy

The Social Media Handbook provides guidance on long-term developments in the ever-changing social media sector and explains fundamental interrelationships in this field. It describes a strategy model for the development of one’s own solutions, summarises the theories, methods and models of leading authors and shows their practical application, while also highlighting current developments and dealing with the topic of data processing in social media. An examination of the platform economy with its economic functions facilitates the classification of business models in social media. The book also shows how platforms and their algorithms can influence our actions and shape our opinions. With contributions by Prof. Karin Bjerregaard Schlüter, Andrea Braun, Franziska Geue, Tobias Knopf, Markus Korbien, Prof. Dr. Daniel Michelis, Stefan Pfaff, Thanh H. Pham, Tom Reichstein, Prof. Dr. Anna Riedel, Michael Sarbacher, Prof. Dr. Dr. Thomas Schildhauer, Prof. Dr. Hendrik Send, Dr. Stefan Stumpp, Prof. Dr. Sebastian Volkmann, Jan-Benedikt Weber, Julia Weißhaupt, Norman Wiebach und Prof. Dr. Christian Wissing.

scholarly journals The Clickwrap: A Political Economic Mechanism for Manufacturing Consent on Social Media

2018 ◽  
Vol 4 (3) ◽  
pp. 205630511878477 ◽  
Author(s):  
Jonathan A. Obar ◽  
Anne Oeldorf-Hirsch
Keyword(s):  
Social Media ◽  
Digital Media ◽  
Agenda Setting ◽  
Survey Analysis ◽  
User Interactions ◽  
Qualitative Survey ◽  
Media Services ◽  
Social Media Service ◽  
Political Economic ◽  
Economic Function

The clickwrap is a digital prompt that facilitates consent processes by affording users the opportunity to quickly accept or reject digital media policies. A qualitative survey analysis was conducted ( N = 513), assessing user interactions with the consent materials of a fictitious social media service, NameDrop. Findings suggest that clickwraps serve a political economic function by facilitating the circumvention of consent materials. Herman and Chomsky’s notion of the “buying mood” guides the analysis to analogize how social media maintain flow to monetized sections of services while diverting attention from policies that might encourage dissent. Clickwraps accomplish this through an agenda-setting function whereby prompts encouraging circumvention are made more prominent than policy links. Results emphasize that clickwraps discourage engagement with privacy and reputation protections by suggesting that consent materials are unimportant, contributing to the normalization of this circumvention. The assertion that clickwraps serve a political economic function suggests that capitalist methods of production are successfully being integrated into social media services and have the ability to manufacture consent.

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