scholarly journals A Conceptual Tool to Eliminate Filter Bubbles in Social Networks

2021 ◽  
Vol 25 ◽  
Author(s):  
Alireza Amrollahi
Keyword(s):  
Social Networks ◽  
Holistic Approach ◽  
Negative Consequences ◽  
Fake News ◽  
Conceptual Tool ◽  
Scientific Databases ◽  
Depth Analysis ◽  
Filter Bubbles ◽  
Source Of Information

Reliance on social media as a source of information has lead to several challenges, including the limitation of sources to viewers’ preferences and desires, also known as filter bubbles. The formation of filter bubbles is a known risk to democracy. It can bring negative consequences like polarisation of the society, users’ tendency to extremist viewpoints and the proliferation of fake news. Previous studies have focused on specific aspects and paid less attention to a holistic approach for eliminating the notion. The current study, however, aims to propose a model for an integrated tool that assists users in avoiding filter bubbles in social networks. To this end, a systematic literature review has been undertaken, and initially, 571 papers in six top-ranked scientific databases have been identified. After excluding irrelevant studies and performing an in-depth analysis of the remaining papers, a classification of research studies is proposed. This classification is then used to introduce an overall architecture for an integrated tool that synthesises all previous studies and offers new features for avoiding filter bubbles. The study explains the components and features of the proposed architecture and concludes with a list of implications for the recommended tool.

scholarly journals Mapeo del consumo de medios en los jóvenes: redes sociales, 'fake news' y confianza en tiempos de pandemia

2021 ◽  
Vol 11 (2) ◽  
pp. 187-208
Author(s):  
Ana Pérez-Escoda ◽  
◽  
Gema Barón-Dulce ◽  
Juana Rubio-Romero ◽  
◽  
...  
Keyword(s):  
Social Networks ◽  
Three Dimensions ◽  
Media Consumption ◽  
Quantitative Methodology ◽  
Fake News ◽  
Redes Sociales ◽  
Generation Z ◽  
The Media ◽  
Major Transformation ◽  
Source Of Information

The explosion of the Covid-19 pandemic has led to a major transformation in media consumption and the use of social networks. New habits and extensive exposure to connected devices coupled with unmanageable amounts of information warn of a worrying reality, especially among the younger population. The aim of this research is to discover the degree of trustworthiness of Generation Z towards the media, their media consumption preferences and the association they make between media consumption and fake news. Using a descriptive and exploratory quantitative methodology, a study is presented with a sample of 225 young people belonging to this population niche. The study addresses three dimensions: media consumption, social networks and perception of fake news. The results show that generation Z is an intensive consumer of the media they trust the least and perceive traditional media as the most trustworthy. The findings indicate that social networks are the main source of information consumption for this ge­neration, among other content, despite also being the least trustworthy and the most likely to distribute fake news according to their perceptions. There is a lack of media literacy from a critical rather than a formative perspective.

scholarly journals Classification of Cyber-Aggression Cases Applying Machine Learning

2019 ◽  
Vol 9 (9) ◽  
pp. 1828 ◽  
Author(s):  
Guadalupe Obdulia Gutiérrez-Esparza ◽  
Maite Vallejo-Allende ◽  
José Hernández-Torruco
Keyword(s):  
Social Networks ◽  
Violence Against Women ◽  
Virtual Community ◽  
Cyber Bullying ◽  
Spanish Language ◽  
The Internet ◽  
Negative Consequences ◽  
Cyber Aggression ◽  
Variable Importance Measures

The adoption of electronic social networks as an essential way of communication has become one of the most dangerous methods to hurt people’s feelings. The Internet and the proliferation of this kind of virtual community have caused severe negative consequences to the welfare of society, creating a social problem identified as cyber-aggression, or in some cases called cyber-bullying. This paper presents research to classify situations of cyber-aggression on social networks, specifically for Spanish-language users of Mexico. We applied Random Forest, Variable Importance Measures (VIMs), and OneR to support the classification of offensive comments in three particular cases of cyber-aggression: racism, violence based on sexual orientation, and violence against women. Experimental results with OneR improve the comment classification process of the three cyber-aggression cases, with more than 90% accuracy. The accurate classification of cyber-aggression comments can help to take measures to diminish this phenomenon.

scholarly journals Celebrity profiling through linguistic analysis of digital social networks

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Luis G. Moreno-Sandoval ◽  
Alexandra Pomares-Quimbaya ◽  
Jorge A. Alvarado-Valencia
Keyword(s):  
Social Networks ◽  
Complementary Information ◽  
The Third ◽  
Average Accuracy ◽  
Third Person ◽  
Selection For ◽  
And Gender ◽  
The Third Person ◽  
Source Of Information

AbstractDigital social networks have become an essential source of information because celebrities use them to share their opinions, ideas, thoughts, and feelings. This makes digital social networks one of the preferred means for celebrities to promote themselves and attract new followers. This paper proposes a model of feature selection for the classification of celebrities profiles based on their use of a digital social network Twitter. The model includes the analysis of lexical, syntactic, symbolic, participation, and complementary information features of the posts of celebrities to estimate, based on these, their demographic and influence characteristics. The classification with these new features has an F1-score of 0.65 in Fame, 0.88 in Gender, 0.37 in Birth year, and 0.57 in Occupation. With these new features, the average accuracy improve up to 0.14 more. As a result, extracted features from linguistic cues improved the performance of predictive models of Fame and Gender and facilitate explanations of the model results. Particularly, the use of the third person singular was highly predictive in the model of Fame.

Towards Source-Based Classification of Image Inpainting Techniques: A Survey

2021 ◽  
pp. 2150039
Author(s):  
I. J. Sreelakshmy ◽  
C. Kovoor Binsu
Keyword(s):  
Image Inpainting ◽  
Critical Factor ◽  
External Source ◽  
Comprehensive List ◽  
Host Image ◽  
Depth Analysis ◽  
New Perspective ◽  
Available Information ◽  
Source Of Information

Image inpainting is a process of reconstructing an incomplete image from the available information in a visually plausible way. In the proposed framework, existing image inpainting methods are classified in a new perspective. The information which is referred to, while reconstructing an image, is a critical factor of inpainting algorithms. Source of this information can be host image itself or an external source. The proposed framework broadly classifies inpainting algorithms into introspective and extrospective categories based on the source of information. Various parameters influencing the algorithms under these categories are identified in the proposed framework. A comprehensive list of all publicly available datasets along with the references are also summarized. Additionally, an in-depth analysis of the results obtained with the surveyed techniques is performed based on quantitative and qualitative parameters. The proposed framework aids the user in identifying the most suitable algorithm for various inpainting scenarios.

Index of suicide risk in Mexico using Twitter

2019 ◽  
pp. 1-13
Author(s):  
Luz Judith Rodríguez-Esparza ◽  
Diana Barraza-Barraza ◽  
Jesús Salazar-Ibarra ◽  
Rafael Gerardo Vargas-Pasaye
Keyword(s):  
Social Networks ◽  
Analytic Hierarchy Process ◽  
Suicide Risk ◽  
Opinion Mining ◽  
Real Data ◽  
Analytic Hierarchy ◽  
Specialized Care ◽  
The Analytic Hierarchy Process ◽  
Hierarchy Process

Objectives: To identify early suicide risk signs on depressive subjects, so that specialized care can be provided. Various studies have focused on studying expressions on social networks, where users pour their emotions, to determine if they show signs of depression or not. However, they have neglected the quantification of the risk of committing suicide. Therefore, this article proposes a new index for identifying suicide risk in Mexico. Methodology: The proposal index is constructed through opinion mining using Twitter and the Analytic Hierarchy Process. Contribution: Using R statistical package, a study is presented considering real data, making a classification of people according to the obtained index and using information from psychologists. The proposed methodology represents an innovative prevention alternative for suicide.

Networks

2020 ◽  
pp. 534-562
Author(s):  
Matthew O. Jackson ◽  
Brian W. Rogers ◽  
Yves Zenou
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Statistical Analysis ◽  
Labor Markets ◽  
Network Formation ◽  
Labor Market Outcomes ◽  
Negative Consequences ◽  
Social Scientists ◽  
Market Outcomes

What is the role of social networks in driving persistent differences between races and genders in education and labor market outcomes? What is the role of homophily in such differences? Why is such homophily seen even if it ends up with negative consequences in terms of labor markets? This chapter discusses social network analysis from the perspective of economics. The chapter is organized around the theme of externalities: the effects that one’s behavior has on others’ welfare. Externalities underlie the interdependencies that make networks interesting to social scientists. This chapter discusses network formation, as well as interactions between people’s behaviors within a given network, and the implications in a variety of settings. Finally, the chapter highlights some empirical challenges inherent in the statistical analysis of network-based data.

scholarly journals Realistic Aspects of Simulation Models for Fake News Epidemics over Social Networks

2021 ◽  
Vol 13 (3) ◽  
pp. 76
Author(s):  
Quintino Francesco Lotito ◽  
Davide Zanella ◽  
Paolo Casari
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Simulation Models ◽  
Epidemic Models ◽  
Time Varying ◽  
Fake News ◽  
Network Access ◽  
Spreading Process ◽  
Network Topologies ◽  
Access Patterns

The pervasiveness of online social networks has reshaped the way people access information. Online social networks make it common for users to inform themselves online and share news among their peers, but also favor the spreading of both reliable and fake news alike. Because fake news may have a profound impact on the society at large, realistically simulating their spreading process helps evaluate the most effective countermeasures to adopt. It is customary to model the spreading of fake news via the same epidemic models used for common diseases; however, these models often miss concepts and dynamics that are peculiar to fake news spreading. In this paper, we fill this gap by enriching typical epidemic models for fake news spreading with network topologies and dynamics that are typical of realistic social networks. Specifically, we introduce agents with the role of influencers and bots in the model and consider the effects of dynamical network access patterns, time-varying engagement, and different degrees of trust in the sources of circulating information. These factors concur with making the simulations more realistic. Among other results, we show that influencers that share fake news help the spreading process reach nodes that would otherwise remain unaffected. Moreover, we emphasize that bots dramatically speed up the spreading process and that time-varying engagement and network access change the effectiveness of fake news spreading.

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 Taking sociality seriously: the structure of multi-dimensional social networks as a source of information for individuals

2012 ◽  
Vol 367 (1599) ◽  
pp. 2108-2118 ◽  
Author(s):  
Louise Barrett ◽  
S. Peter Henzi ◽  
David Lusseau
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Life ◽  
The Other ◽  
Feedback Process ◽  
Cognitive Evolution ◽  
Systems Approaches ◽  
Entropy Measures ◽  
Free Ranging ◽  
Source Of Information

Understanding human cognitive evolution, and that of the other primates, means taking sociality very seriously. For humans, this requires the recognition of the sociocultural and historical means by which human minds and selves are constructed, and how this gives rise to the reflexivity and ability to respond to novelty that characterize our species. For other, non-linguistic, primates we can answer some interesting questions by viewing social life as a feedback process, drawing on cybernetics and systems approaches and using social network neo-theory to test these ideas. Specifically, we show how social networks can be formalized as multi-dimensional objects, and use entropy measures to assess how networks respond to perturbation. We use simulations and natural ‘knock-outs’ in a free-ranging baboon troop to demonstrate that changes in interactions after social perturbations lead to a more certain social network, in which the outcomes of interactions are easier for members to predict. This new formalization of social networks provides a framework within which to predict network dynamics and evolution, helps us highlight how human and non-human social networks differ and has implications for theories of cognitive evolution.

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