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.

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 Evolutionary Algorithm for Improving Decision Tree with Global Discretization in Manufacturing

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2849
Author(s):  
Sungbum Jun
Keyword(s):  
Decision Tree ◽  
Evolutionary Algorithm ◽  
Decision Trees ◽  
Manufacturing Systems ◽  
Ensemble Methods ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Industrial Internet ◽  
Tree Models ◽  
Real World Datasets

Due to the recent advance in the industrial Internet of Things (IoT) in manufacturing, the vast amount of data from sensors has triggered the need for leveraging such big data for fault detection. In particular, interpretable machine learning techniques, such as tree-based algorithms, have drawn attention to the need to implement reliable manufacturing systems, and identify the root causes of faults. However, despite the high interpretability of decision trees, tree-based models make a trade-off between accuracy and interpretability. In order to improve the tree’s performance while maintaining its interpretability, an evolutionary algorithm for discretization of multiple attributes, called Decision tree Improved by Multiple sPLits with Evolutionary algorithm for Discretization (DIMPLED), is proposed. The experimental results with two real-world datasets from sensors showed that the decision tree improved by DIMPLED outperformed the performances of single-decision-tree models (C4.5 and CART) that are widely used in practice, and it proved competitive compared to the ensemble methods, which have multiple decision trees. Even though the ensemble methods could produce slightly better performances, the proposed DIMPLED has a more interpretable structure, while maintaining an appropriate performance level.

scholarly journals DeepOSN: Bringing deep learning as malicious detection scheme in online social network

2020 ◽  
Vol 9 (1) ◽  
pp. 146
Author(s):  
Putra Wanda ◽  
Marselina Endah Hiswati ◽  
Huang J. Jie
Keyword(s):  
Social Networks ◽  
Computer Vision ◽  
Deep Learning ◽  
Online Social Networks ◽  
Link Prediction ◽  
Online Social Network ◽  
Detection Scheme ◽  
Malicious Activity ◽  
Manual Analysis ◽  
Learning Techniques

Manual analysis for malicious prediction in Online Social Networks (OSN) is time-consuming and costly. With growing users within the environment, it becomes one of the main obstacles. Deep learning is growing algorithm that gains a big success in computer vision problem. Currently, many research communities have proposed deep learning techniques to automate security tasks, including anomalous detection, malicious link prediction, and intrusion detection in OSN. Notably, this article describes how deep learning makes the OSN security technique more intelligent for detecting malicious activity by establishing a classifier model.

scholarly journals DEVELOPMENT OF A MACHINE LEARNING ALGORITHM TO PREDICT AUTHOR’S AGE FROM TEXT

2020 ◽  
Vol 7 (10) ◽  
pp. 380-389
Author(s):  
Asogwa D.C ◽  
Anigbogu S.O ◽  
Anigbogu G.N ◽  
Efozia F.N
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Machine Learning Algorithm ◽  
Age Group ◽  
Political Views ◽  
Learning Techniques ◽  
Age Prediction

Author's age prediction is the task of determining the author's age by studying the texts written by them. The prediction of author’s age can be enlightening about the different trends, opinions social and political views of an age group. Marketers always use this to encourage a product or a service to an age group following their conveyed interests and opinions. Methodologies in natural language processing have made it possible to predict author’s age from text by examining the variation of linguistic characteristics. Also, many machine learning algorithms have been used in author’s age prediction. However, in social networks, computational linguists are challenged with numerous issues just as machine learning techniques are performance driven with its own challenges in realistic scenarios. This work developed a model that can predict author's age from text with a machine learning algorithm (Naïve Bayes) using three types of features namely, content based, style based and topic based. The trained model gave a prediction accuracy of 80%.

scholarly journals Development of Agriculture Chatbot using Machine Learning Techniques

2019 ◽  
Vol 9 (2S) ◽  
pp. 24-28
Keyword(s):  
Question Answering ◽  
Learning Algorithm ◽  
Related Question ◽  
Machine Learning Techniques ◽  
Meaningful Information ◽  
Related Information ◽  
Exact Answer ◽  
Deep Learning Algorithm ◽  
The People ◽  
Learning Techniques

Agriculture data is a main source of country’s economic growth. It is important to provide agriculture related information to all the people who are involved in agriculture activities as and when required. This meaningful information is used by people who supply services to agriculture domain and to take some correct decision related to agriculture to apply for their field. The solutions to this problem are given by the efficient interaction of computer with human. Chatbot system provides ability to extract the exact answer to the queries posed by farmers. The proposed system is called as Agriculture Chatbot system or even it is called as Question-Answering system for agriculture domain, where farmer is asking the agriculture related question which fetches the precise answers for the asked questions by farmers in natural language and processes the query using RNN (Recurrent Neural Network) deep learning algorithm to extract correct answer.

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.

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