scholarly journals Anticollusion Attack Strategy Combining Trust Metrics and Secret Sharing for Friendships Protection

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Junfeng Tian ◽  
Yue Li
Keyword(s):  
Social Networks ◽  
Secret Sharing ◽  
Online Social Networks ◽  
Instant Messaging ◽  
Online Interaction ◽  
Collusion Attacks ◽  
New Type ◽  
Trust Metric ◽  
Trust Metrics ◽  
Attack Strategy

Online social networks provide users with services such as online interaction, instant messaging, and information sharing. The friend search engine, a new type of social application, provides users with the service for querying the list of other individuals’ friends. Currently, the existing research focuses on independent attacks for friend search engines while ignoring the more complicated collusion attacks, which can expose more friendships that users are not willing to share. Compared with independent attackers, collusion attackers share query results by cooperating with each other. In this article, we propose a resistance strategy against collusion attacks to protect the friendship privacy. The proposed trust metric is based on users’ behaviors and is combined with Shamir’s secret sharing system, which can transform friendships into secrets. Through secret distribution and reconfiguration, only the participants who meet the query requirements can successfully reconstruct the secret, while the participants who do not meet the query conditions cannot successfully obtain the secret fragments even if they obtain the secret fragments. Experiments are conducted to verify the effectiveness of the proposed strategy and proved that this strategy can greatly limit the number of malicious attackers, greatly reduce the probability of successful collusion attacks, and reduce the number of victims.

A group trust metric for identifying people of trust in online social networks

2012 ◽  
Vol 39 (18) ◽  
pp. 13173-13181 ◽  
Author(s):  
Samah Al-Oufi ◽  
Heung-Nam Kim ◽  
Abdulmotaleb El Saddik
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Trust Metric ◽  
Group Trust

scholarly journals WhatsApp is the Message: Out-of-Class Communication, Student-Teacher Relationship, and Classroom Environment

10.28945/4183 ◽  
2019 ◽  
Vol 18 ◽  
pp. 073-095
Author(s):  
Arnon Hershkovitz ◽  
Mohamed ali Abu Elhija ◽  
Daher Zedan
Keyword(s):  
Social Networks ◽  
High School Students ◽  
Student Teacher ◽  
Classroom Environment ◽  
Online Social Networks ◽  
Role Models ◽  
Instant Messaging ◽  
Unique Contribution ◽  
Teacher Communication ◽  
Student Teacher Relationship

Aim/Purpose: To study associations between elementary-, middle- and high-school students’ perceptions of classroom environment and student-teacher relationship and their out-of-class communication practices via WhatsApp app. Background: Communication between students and teachers is usually extended beyond the classroom’s time and space. This communication, referred to as out-of-class communication (OCC), may impact students’ academic, social, and emotional development. Today, OCC is facilitated via social media and instant messaging services, which may have impact on its nature. Methodology: Methodology was quantitative in nature. Data was collected using an online questionnaire (implemented on Google Forms, http://forms.google.com , during June-July 2016. Participants (n=300), from 5th-12th grades (11-18 years old), were recruited in schools in a few Arab villages in northern Israel, with the assistance of their teachers. Contribution: The present study expands the growing body of knowledge about student-teacher communication via online social networks, specifically regarding out-of-class communication. We identify the unique aspects of WhatsApp-based out-of-class communication, which shed light on student-teacher relationship at large. Findings from this study may assist educators (while in training and/or professional development programs) to reflect upon their own educational agenda and to check if and how they and their students can benefit from OCC. Findings: Overall, we identify WhatsApp’s important, unique role in promoting good student-teacher relationship and positive classroom environment. Recommendations for Practitioners: The findings regarding the unique contribution of WhatsApp to student-teacher out-of-class communication should be taken into consideration by policy makers while formulating policies for the use of online social networks in educational settings. Teachers should be aware of the important role this type of communication plays for their students and for their classroom. Both teachers and students should communicate respectfully, with teachers serving as role models for their students regarding proper digital behavior. Recommendation for Researchers: This study should be replicated to more populations and to more communication platforms, in order to validate its findings. Impact on Society: The associations between out-of-class communication via online social networks and student-teacher relationship have two main effects on society at large. First, promoting better student-teacher communication could improve learning and teaching. Second, if this communication is to be carried out properly, the students - who are the future citizens - will learn how to behave correctly in the digital age. Future Research: It is advised to explore the studied associations in other populations and regarding other communication platforms. Also, qualitative exploration is advisable, as it may shed more light on the unique aspects of WhatsApp-based student-teacher out-of-class communication.

Inter-Animation between Utterances in Collaborative Chat Conversations

2013 ◽  
pp. 63-93
Author(s):  
Traian Rebedea ◽  
Stefan Trausan-Matu ◽  
Costin Chiru
Keyword(s):  
Social Networks ◽  
Computer Science ◽  
Instant Messaging ◽  
Online Discussion ◽  
Online Communication ◽  
Discussion Forums ◽  
Collaborative Discourse ◽  
Online Discussion Forums ◽  
Discussion Threads ◽  
New Type

With the wide adoption of instant messaging, online discussion forums, blogs and social networks, online communication has shifted from narration to highly collaborative discussions with multiple authors and discussion threads. However, the theories and methodologies for analyzing this new type of discourse which is different from narration, but also from dialogue, have remained mostly the same. The authors propose a new method for the analysis of this type of discourse, designed especially for multi-party chat conversations where parallel discussion floors and threads exist at the same time. The theoretical underpinning of the inter-animation framework is the detection of links between utterances in order to build a conversation graph that may be used to discover the discussion threads. The framework has been used for analyzing chat conversations of students in Computer Science in order to assess the involvement of each student, the inter-animation of the conversation and the degree of collaborative discourse.

scholarly journals A Survey of Attacks Against Twitter Spam Detectors in an Adversarial Environment

Robotics ◽  
2019 ◽  
Vol 8 (3) ◽  
pp. 50 ◽  
Author(s):  
Niddal H. Imam ◽  
Vassilios G. Vassilakis
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Online Social Networks ◽  
Defense Mechanism ◽  
Malware Detection ◽  
Test Phase ◽  
Design Stage ◽  
Daily Lives ◽  
Prediction Test ◽  
New Type

Online Social Networks (OSNs), such as Facebook and Twitter, have become a very important part of many people’s daily lives. Unfortunately, the high popularity of these platforms makes them very attractive to spammers. Machine learning (ML) techniques have been widely used as a tool to address many cybersecurity application problems (such as spam and malware detection). However, most of the proposed approaches do not consider the presence of adversaries that target the defense mechanism itself. Adversaries can launch sophisticated attacks to undermine deployed spam detectors either during training or the prediction (test) phase. Not considering these adversarial activities at the design stage makes OSNs’ spam detectors vulnerable to a range of adversarial attacks. Thus, this paper surveys the attacks against Twitter spam detectors in an adversarial environment, and a general taxonomy of potential adversarial attacks is presented using common frameworks from the literature. Examples of adversarial activities on Twitter that were discovered after observing Arabic trending hashtags are discussed in detail. A new type of spam tweet (adversarial spam tweet), which can be used to undermine a deployed classifier, is examined. In addition, possible countermeasures that could increase the robustness of Twitter spam detectors to such attacks are investigated.

scholarly journals Análisis de la dinámica, la estructura y el contenido de los mensajes de Twitter: violencia sexual en #Cuéntalo

2022 ◽  
Author(s):  
Modesto Escobar ◽  
Elena Gil Moreno ◽  
Cristina Calvo López
Keyword(s):  
Social Networks ◽  
Content Analysis ◽  
Sexual Violence ◽  
Online Social Networks ◽  
Public Information ◽  
Redes Sociales ◽  
Social Scientists ◽  
New Type ◽  
Shared Narrative

Las redes sociales online se han ido convirtiendo en uno de los principales vehículos de comunicación y una de las mayores fuentes de información de actualidad. Esta creciente popularidad deja en evidencia la importancia de que los científicos sociales seamos capaces de analizar, interpretar y comprender en profundidad este nuevo tipo de herramientas. Este artículo tiene como objetivo mostrar los diversos métodos de análisis de la información pública obtenida a partir de una de estas redes, Twitter. Para ello tomamos como ejemplificación explicativa el caso #Cuéntalo, un episodio de narrativa compartida iniciado en esta red entre los días 26 y 28 de abril de 2018 tras la conocida sentencia de “La Manada”. A través de este caso se presentan aquí distintas metodologías para el estudio de los contenidos transmitidos, que van desde los análisis descriptivos más elementales hasta los análisis de contenido, pasando por la clasificación de actores relevantes y el descubrimiento de la estructura de las relaciones entre los protagonistas y sus mensajes. Los resultados muestran cómo esta polémica sentencia derivó en una conversación digital viral donde distintas usuarias (en especial periodistas, escritoras y activistas feministas) comenzaron a compartir sus relatos de situaciones de violencia sexual vividas por las participantes o sus conocidas usando esta etiqueta, siendo capaces de identificar a las principales protagonistas, las distintas relaciones que establecieron entre ellas y sus mensajes y los principales temas que se conformaron en torno a ellos. Online social networks have become one of the main communication vehicles and one of the greatest sources of current information. This growing popularity shows the importance of social scientists being able to analyze, interpret and understand in depth this new type of tools. This article aims to show the diverse methods of analysis of public information obtained from one of these networks, Twitter. To do this, we take as an explanatory example the case of #Cuéntalo, an episode of shared narrative that began on this network between April 26 and 28, 2018 after the well-known sentence of “La Manada”. Through this case, we present different methodologies for the study of broadcasted content, ranging from the most elementary descriptive tools to content analysis, passing through the classification of relevant actors and the discovery of the structure of the relationships amongst their protagonists and their messages. The results show how this controversial sentence led to a viral digital conversation where different users (especially journalists, writers, feminists and influencers) began to share their stories of situations of sexual violence experienced by the participants or their acquaintances using this label. Through this analysis, it was possible to identify the main protagonists, the different relationships that they established between them and their messages and the main themes that were formed around them.

THE SOCIAL VALUES ATTACHED TO THE TIKTOK SOCIAL PLATFORM OF THE AGE CATEGORY 50+. A SOCIOLOGICAL PERSPECTIVE

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
LORENA ȚĂRUȘ
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Social Values ◽  
The Other ◽  
Online Interaction ◽  
Sociological Perspective ◽  
The Social ◽  
Existing Data ◽  
Age Category

This paper analyzes the social values through which users over the age of 50 identify with the new TikTok social platform. Although the other online social networks are mainly aimed at a young audience, the TikTok application has overcome this barrier and included the age segment of over 50 years in the categories targeted by it. The ease with which one can make their proper creations and the intuitiveness of the application has made that two years after the launch of the new social platform a quarter of its users are in a more tangible age category of novelty and online interaction. The reality shows that a social application does not contradict people from early youth at all, and if you provide them with sufficiently clear tools to express themselves, they will take advantage of them and make their genuine creations. The present approach is based on existing data about the application in question and focuses on illustrating some values that emerge from the case study on TikTok.

scholarly journals A Survey of Attacks Against Twitter Spam Detectors in an Adversarial Environment

2019 ◽  
Author(s):  
Niddal Imam
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Online Social Networks ◽  
Defense Mechanism ◽  
Malware Detection ◽  
Test Phase ◽  
Design Stage ◽  
Daily Lives ◽  
Prediction Test ◽  
New Type

Online Social Networks (OSNs), such as Facebook and Twitter, have become a very important part of many people’s daily lives. Unfortunately, the high popularity of these platforms makes them very attractive to spammers. Machine-learning (ML) techniques have been widely used as a tool to address many cybersecurity application problems (such as spam and malware detection). However, most of the proposed approaches do not consider the presence of adversaries that target the defense mechanism itself. Adversaries can launch sophisticated attacks to undermine deployed spam detectors either during training or the prediction (test) phase. Not considering these adversarial activities at the design stage makes OSNs’ spam detectors prone to a range of adversarial attacks. This paper thus surveys the attacks against Twitter spam detectors in an adversarial environment. In addition, a general taxonomy of potential adversarial attacks is proposed by applying common frameworks from the literature. Examples of adversarial activities on Twitter were provided after observing Arabic trending hashtags. A new type of spam tweet (Adversarial spam tweet), which can be used to undermine deployed classifier, were found. In addition, possible countermeasures that could increase the robustness of Twitter spam detectors against such attacks are investigated.

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