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

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.

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Automated Classification of Fake News Spreaders to Break the Misinformation Chain

Information ◽

10.3390/info12060248 ◽

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%.

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Polarity Classification of Arabic Sentiments

International Journal of Information Technology and Web Engineering ◽

10.4018/ijitwe.2016070103 ◽

2016 ◽

Vol 11 (3) ◽

pp. 32-49 ◽

Cited By ~ 5

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.

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

2014 IEEE International Professional Communication Conference (IPCC) ◽

10.1109/ipcc.2014.7020373 ◽

2014 ◽

Cited By ~ 2

Author(s):

Gilbert Wilkes ◽

Brian Trayor ◽

Jaigris Hodson

Keyword(s):

Social Media ◽

Folk Classification ◽

Media Services

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To the study of early dementia

Kazan medical journal ◽

10.17816/kazmj48798 ◽

1902 ◽

Vol 2 (9) ◽

pp. 480-481

Author(s):

V. Serbskiy

Keyword(s):

Clinical Course ◽

Mental Illnesses ◽

Dementia Praecox ◽

The Third ◽

Current State ◽

Early Dementia

In the first part of his article, the author examines the current state of the issue of secondary dementia and proves that a group of psychoses, known under the name secondary dementia, should be left in the classification of mental illnesses. The second part is devoted to the analysis of Krpelin's scholarship on dementia praecox, and the author fundamentally disagrees with many of the provisions of the latter. In the third part, the ethiology, clinical course and recognition of premature dementia are analyzed.

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Social Media Handbuch

10.5771/9783748907466 ◽

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.

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