scholarly journals Activeness and Loyalty Analysis in Event-Based Social Networks

Entropy ◽  
2020 ◽  
Vol 22 (1) ◽  
pp. 119
Author(s):  
Thanh Trinh ◽  
Dingming Wu ◽  
Joshua Zhexue Huang ◽  
Muhammad Azhar
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Time Windows ◽  
Social Groups ◽  
Time Frame ◽  
Machine Learning Techniques ◽  
Consecutive Time ◽  
Highly Correlated ◽  
Event Based ◽  
Association Matrix

Event-based social networks (EBSNs) are widely used to create online social groups and organize offline events for users. Activeness and loyalty are crucial characteristics of these online social groups in terms of determining the growth or inactiveness of the social groups in a specific time frame. However, there is less research on these concepts to clarify the existence of groups in event-based social networks. In this paper, we study the problem of group activeness and user loyalty to provide a novel insight into online social networks. First, we analyze the structure of EBSNs and generate features from the crawled datasets. Second, we define the concepts of group activeness and user loyalty based on a series of time windows, and propose a method to measure the group activeness. In this proposed method, we first compute a ratio of a number of events between two consecutive time windows. We then develop an association matrix to assign the activeness label for each group after several consecutive time windows. Similarly, we measure the user loyalty in terms of attended events gathered in time windows and treat loyalty as a contributive feature of the group activeness. Finally, three well-known machine learning techniques are used to verify the activeness label and to generate features for each group. As a consequence, we also find a small group of features that are highly correlated and result in higher accuracy as compared to the whole features.

scholarly journals What Accounts for the Variation in COVID-19 Vaccine Hesitancy in Eastern, Southern and Western Europe?

2022 ◽  
Author(s):  
Dimiter Toshkov
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Western Europe ◽  
Vaccine Hesitancy ◽  
Individual Level ◽  
Related Information ◽  
Health Related ◽  
Highly Correlated ◽  
Context Specific ◽  
Vaccination Campaigns

Attitudes towards vaccination have proven to be a major factor determining the pace of national COVID-19 vaccination campaigns throughout 2021. In Europe, large differences in levels of vaccine hesitancy and refusal have emerged, which are highly correlated with actual vaccination levels. This article explores attitudes towards COVID-19 vaccination in 27 European countries based on data from Eurobarometer (May 2021). The statistical analyses show that demographic variables have complex effects on vaccine hesitancy and refusal. Trust in different sources of health-related information has significant effects as well, with people who trust the Internet, social networks and ‘people around’ in particular being much more likely to express vaccine skepticism. As expected, beliefs in the safety and effectiveness of vaccines have large predictive power, but – more interestingly – net of these two beliefs, the effects of trust in Internet, online social networks and people as sources of health information are significantly reduced. This study shows that the effects of demographic, belief-related and other individual-level factors on vaccine hesitancy and refusal are context-specific. Yet, explanations of the differences in vaccine hesitancy across Europe need to consider primarily different levels of trust and vaccine-relevant beliefs, and to a lesser extent their differential effects.

Supporting Participation in Online Social Networks

2019 ◽  
pp. 122-142
Author(s):  
Agostino Poggi ◽  
Paolo Fornacciari ◽  
Gianfranco Lombardo ◽  
Monica Mordonini ◽  
Michele Tomaiuolo
Keyword(s):  
Social Networks ◽  
Social Networking ◽  
Online Social Networks ◽  
Online Social Network ◽  
Social Groups ◽  
Work Place ◽  
Social Phenomena ◽  
Birthday Party ◽  
Gender Education ◽  
The World

Social networking systems can be considered one of the most important social phenomena because they succeeded in involving billions of people all around the world and in attracting users from several social groups, regardless of age, gender, education, or nationality. Social networking systems blur the distinction between the private and working spheres, and users are known to use such systems both at home and at the work place both professionally and with recreational goals. Social networking systems can be equally used to organize a work meeting, a dinner with the colleagues, or a birthday party with friends. In the vast majority of cases, social networking platforms are still used without corporate blessing. However, several traditional information systems, such as CRMs and ERPs, have also been modified in order to include social aspects. This chapter discusses the participation in online social networking activities and, in particular, the technologies that support and promote the participation in online social network.

scholarly journals Link prediction in multiplex online social networks

2017 ◽  
Vol 4 (2) ◽  
pp. 160863 ◽  
Author(s):  
Mahdi Jalili ◽  
Yasin Orouskhani ◽  
Milad Asgari ◽  
Nazanin Alipourfard ◽  
Matjaž Perc
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Link Prediction ◽  
Support Vector ◽  
Svm Classifier ◽  
Multiplex Network ◽  
Average Accuracy ◽  
Potential Applications ◽  
Meta Path ◽  
Highly Correlated

Online social networks play a major role in modern societies, and they have shaped the way social relationships evolve. Link prediction in social networks has many potential applications such as recommending new items to users, friendship suggestion and discovering spurious connections. Many real social networks evolve the connections in multiple layers (e.g. multiple social networking platforms). In this article, we study the link prediction problem in multiplex networks. As an example, we consider a multiplex network of Twitter (as a microblogging service) and Foursquare (as a location-based social network). We consider social networks of the same users in these two platforms and develop a meta-path-based algorithm for predicting the links. The connectivity information of the two layers is used to predict the links in Foursquare network. Three classical classifiers (naive Bayes, support vector machines (SVM) and K-nearest neighbour) are used for the classification task. Although the networks are not highly correlated in the layers, our experiments show that including the cross-layer information significantly improves the prediction performance. The SVM classifier results in the best performance with an average accuracy of 89%.

scholarly journals Experiencing the Detection of Radicalized Criminals on Facebook Social Network and Data-related Issues

2020 ◽  
Author(s):  
Andrea Tundis ◽  
Leon Böck ◽  
Victoria Stanilescu ◽  
Max Mühlhäuser
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Network ◽  
Problem Solving ◽  
Online Social Networks ◽  
Data Availability ◽  
Digital Tools ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Public Dataset

Online social networks (OSNs) represent powerful digital tools to communicate and quickly disseminate information in a non-official way. As they are freely accessible and easy to use, criminals abuse of them for achieving their purposes, for example, by spreading propaganda and radicalising people. Unfortunately, due to their vast usage, it is not always trivial to identify criminals using them unlawfully. Machine learning techniques have shown benefits in problem solving belonging to different application domains, when, due to the huge dimension in terms of data and variables to consider, it is not feasible their manual assessment. However, since the OSNs domain is relatively young, a variety of issues related to data availability makes it difficult to apply and immediately benefit from such techniques, in supporting the detection of criminals on OSNs. In this perspective, this paper wants to share the experience conducted in using a public dataset containing information related to criminals in order to both (i) extract specific features and to build a model for the detection of terrorists on Facebook social network, and (ii) to highlight the current limits. The research methodology as well as the gathered results are fully presented and then the data-related issues, emerged from this experience, are discussed. .

scholarly journals A Survey of Psychological Personality Classification Approaches

2020 ◽  
Vol 4 (2) ◽  
pp. 79-89
Author(s):  
Mervat Ragab Bakry ◽  
Keyword(s):  
Social Networks ◽  
Personality Traits ◽  
Online Social Networks ◽  
Machine Learning Techniques ◽  
Big Five Model ◽  
Learning Techniques ◽  
Psychological Models ◽  
Compliance Model ◽  
Personality Classification ◽  
Share Information

Online social networks (OSNs) have become essential ways for users to socially share information and feelings, communicate, and thoughts with others through online social networks. Online social networks such as Twitter and Facebook are some of the most common OSNs among users. Users’ behaviors on social networks aid researchers for detecting and understanding their online behaviors and personality traits. Personality detection is one of the new difficulties in social networks. Machine learning techniques are used to build models for understanding personality, detecting personality traits, and classifying users into different kinds through user generated content based on different features and measures of psychological models such as PEN (Psychoticism, Extraversion, and Neuroticism) model, DISC (Dominance, Influence, Steadiness, and Compliance) model, and the Big-five model (Openness, Extraversion, Consciousness, Agreeableness, and Neurotic) which is the most accepted model of personality. This survey discusses the existing works on psychological personality classification.

scholarly journals Portrait of a Privacy Invasion

2015 ◽  
Vol 2015 (1) ◽  
pp. 41-60 ◽  
Author(s):  
Yan Shoshitaishvili ◽  
Christopher Kruegel ◽  
Giovanni Vigna
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Machine Learning Techniques ◽  
Novel Technique ◽  
The People ◽  
Political Views ◽  
Learning Techniques ◽  
Privacy Attack ◽  
Real World Datasets ◽  
Large Corpus

Abstract The popularity of online social networks has changed the way in which we share personal thoughts, political views, and pictures. Pictures have a particularly important role in the privacy of users, as they can convey substantial information (e.g., a person was attending an event, or has met with another person). Moreover, because of the nature of social networks, it has become increasingly difficult to control who has access to which content. Therefore, when a substantial amount of pictures are accessible to one party, there is a very serious potential for violations of the privacy of users. In this paper, we demonstrate a novel technique that, given a large corpus of pictures shared on a social network, automatically determines who is dating whom, with reasonable precision. More specifically, our approach combines facial recognition, spatial analysis, and machine learning techniques to determine pairs that are dating. To the best of our knowledge, this is the first privacy attack of this kind performed on social networks. We implemented our approach in a tool, called Creepic, and evaluated it on two real-world datasets. The results show that it is possible to automatically extract non-obvious, and nondisclosed, relationships between people represented in a group of pictures, even when the people involved are not directly part of a connected social clique.

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