scholarly journals A Methodology for Evaluating Algorithms That Calculate Social Influence in Complex Social Networks

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-20
Author(s):  
Vanja Smailovic ◽  
Vedran Podobnik ◽  
Ignac Lovrek
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Network ◽  
Social Influence ◽  
Online Social Networks ◽  
Ground Truth ◽  
Descriptive Statistics ◽  
The Social ◽  
Alternative Approaches

Online social networks are complex systems often involving millions or even billions of users. Understanding the dynamics of a social network requires analysing characteristics of the network (in its entirety) and the users (as individuals). This paper focuses on calculating user’s social influence, which depends on (i) the user’s positioning in the social network and (ii) interactions between the user and all other users in the social network. Given that data on all users in the social network is required to calculate social influence, something not applicable for today’s social networks, alternative approaches relying on a limited set of data on users are necessary. However, these approaches introduce uncertainty in calculating (i.e., predicting) the value of social influence. Hence, a methodology is proposed for evaluating algorithms that calculate social influence in complex social networks; this is done by identifying the most accurate and precise algorithm. The proposed methodology extends the traditional ground truth approach, often used in descriptive statistics and machine learning. Use of the proposed methodology is demonstrated using a case study incorporating four algorithms for calculating a user’s social influence.

scholarly journals A Framework for Detecting Intentions of Criminal Acts in Social Media: A Case Study on Twitter

Information ◽  
2020 ◽  
Vol 11 (3) ◽  
pp. 154 ◽  
Author(s):  
Ricardo Resende de Mendonça ◽  
Daniel Felix de Brito ◽  
Ferrucio de Franco Rosa ◽  
Júlio Cesar dos Reis ◽  
Rodrigo Bonacin
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Media ◽  
Social Network ◽  
Online Social Networks ◽  
Communication Planning ◽  
Flexible Model ◽  
Selection Of

Criminals use online social networks for various activities by including communication, planning, and execution of criminal acts. They often employ ciphered posts using slang expressions, which are restricted to specific groups. Although literature shows advances in analysis of posts in natural language messages, such as hate discourses, threats, and more notably in the sentiment analysis; research enabling intention analysis of posts using slang expressions is still underexplored. We propose a framework and construct software prototypes for the selection of social network posts with criminal slang expressions and automatic classification of these posts according to illocutionary classes. The developed framework explores computational ontologies and machine learning (ML) techniques. Our defined Ontology of Criminal Expressions represents crime concepts in a formal and flexible model, and associates them with criminal slang expressions. This ontology is used for selecting suspicious posts and decipher them. In our solution, the criminal intention in written posts is automatically classified relying on learned models from existing posts. This work carries out a case study to evaluate the framework with 8,835,290 tweets. The obtained results show its viability by demonstrating the benefits in deciphering posts and the effectiveness of detecting user’s intention in written criminal posts based on ML.

scholarly journals Online social networks as formal learning environments: Learner experiences and activities

2012 ◽  
Vol 13 (1) ◽  
pp. 144 ◽  
Author(s):  
George Veletsianos ◽  
Cesar Navarrete
Keyword(s):  
Social Network ◽  
Social Networking ◽  
Online Social Networks ◽  
Social Networking Sites ◽  
Learning Experience ◽  
Online Social Network ◽  
Online Course ◽  
Formal Learning ◽  
The Social

<p>While the potential of social networking sites to contribute to educational endeavors is highlighted by researchers and practitioners alike, empirical evidence on the use of such sites for formal online learning is scant. To fill this gap in the literature, we present a case study of learners’ perspectives and experiences in an online course taught using the Elgg online social network. Findings from this study indicate that learners enjoyed and appreciated both the social learning experience afforded by the online social network and supported one another in their learning, enhancing their own and other students’ experiences. Conversely, results also indicate that students limited their participation to course-related and graded activities, exhibiting little use of social networking and sharing. Additionally, learners needed support in managing the expanded amount of information available to them and devised strategies and “workarounds” to manage their time and participation.<br /><strong></strong></p>

Emergent Data Mining Tools for Social Network Analysis

2015 ◽  
pp. 1539-1556
Author(s):  
Dhiraj Murthy ◽  
Alexander Gross ◽  
Alex Takata
Keyword(s):  
Data Mining ◽  
Social Networks ◽  
Social Network ◽  
Network Analysis ◽  
Online Social Networks ◽  
Data Extraction ◽  
Future Directions ◽  
Effective Development ◽  
Mining Tools

This chapter identifies a number of the most common data mining toolkits and evaluates their utility in the extraction of data from heterogeneous online social networks. It introduces not only the complexities of scraping data from the diverse forms of data manifested in these sources, but also critically evaluates currently available tools. This analysis is followed by a presentation and discussion on the development of a hybrid system, which builds upon the work of the open-source Web-Harvest framework, for the collection of information from online social networks. This tool, VoyeurServer, attempts to address the weaknesses of tools identified in earlier sections, as well as prototype the implementation of key functionalities thought to be missing from commonly available data extraction toolkits. The authors conclude the chapter with a case study and subsequent evaluation of the VoyeurServer system itself. This evaluation presents future directions, remaining challenges, and additional extensions thought to be important to the effective development of data mining tools for the study of online social networks.

Multilevel Visualization Using Enhanced Social Network Analysis with Smartphone Data

2013 ◽  
Vol 5 (4) ◽  
pp. 34-54 ◽  
Author(s):  
Panagiotis Andriotis ◽  
Zacharias Tzermias ◽  
Anthi Mparmpaki ◽  
Sotiris Ioannidis ◽  
George Oikonomou
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Online Social Networks ◽  
Direct Marketing ◽  
Forensic Analysis ◽  
Three Dimensions ◽  
Fully Integrated ◽  
Aggregate Information ◽  
The Social ◽  
Correlated Information

While technology matures and becomes more productive, mobile devices can be affordable and, consequently, fully integrated in people's lives. After their unexpected bloom and acceptance, Online Social Networks are now sources of valuable information. The authors therefore use them for tasks varying from direct marketing to forensic analysis. The authors have already seen Social Network Forensics techniques focused on particular networks implementing methods that collect data from user accounts. During the forensic analysis it is common to aggregate information from different sources but, usually, this procedure causes correlation problems. Here, the authors present their method to correlate data gathered from various social networks in combination with smartphones creating a new form of social map of the user under investigation. In addition, the authors introduce a multi level graph that utilises the correlated information from the smartphone and the social networks and demonstrates in three dimensions the relevance of each contact with the suspect.

Building Online Social Networks to Engage Female Students in Information Systems

2015 ◽  
Vol 10 (4) ◽  
pp. 33-51
Author(s):  
Jaymeen R. Shah ◽  
Hsun-Ming Lee
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Information Systems ◽  
Online Social Networks ◽  
Social Networking Sites ◽  
Role Models ◽  
Online Social Network ◽  
Research Process ◽  
Female Students ◽  
The Social

During the next decade, enrollment growth in Information Systems (IS) related majors is unlikely to meet the predicted demand for qualified IS graduates. Gender imbalance in the IS related program makes the situation worse as enrollment and retention of women in the IS major has been proportionately low compared to male. In recent years, majority of high school and college students have integrated social networking sites in their daily life and habitually use these sites. Providing female students access to role models via an online social network may enhance their motivation to continue as an IS major and pursue a career in IS field. For this study, the authors follow the action research process – exploration of information systems development. In particular, a Facebook application was developed to build the social network connecting role models and students. Using the application, a basic framework is tested based on the gender of participants. The results suggest that it is necessary to have adequate number of role models accessible to students as female role-models tend to select fewer students to develop relationships with a preference for female students. Female students likely prefer composite role models from a variety of sources. This pilot study yields valuable lessons to provide informal learning fostered by role modeling via online social networks. The Facebook application may be further expanded to enhance female students' interests in IS related careers.

The Alt-social Network of Natalie Bookchin’s Testament

2016 ◽  
Vol 18 (5) ◽  
pp. 459-477
Author(s):  
Sarah Whitcomb Laiola
Keyword(s):  
Data Mining ◽  
Social Networks ◽  
Social Media ◽  
Social Network ◽  
Online Social Networks ◽  
Social Web ◽  
User Interactions ◽  
Media Criticism ◽  
Direct Translation ◽  
The Social

This article addresses issues of user precarity and vulnerability in online social networks. As social media criticism by Jose van Dijck, Felix Stalder, and Geert Lovink describes, the social web is a predatory system that exploits users’ desires for connection. Although accurate, this critical description casts the social web as a zone where users are always already disempowered, so fails to imagine possibilities for users beyond this paradigm. This article examines Natalie Bookchin’s composite video series, Testament, as it mobilizes an alt-(ernative) social network of vernacular video on YouTube. In the first place, the alt-social network works as an iteration of “tactical media” to critically reimagine empowered user-to-user interactions on the social web. In the second place, it obfuscates YouTube’s data-mining functionality, so allows users to socialize online in a way that evades their direct translation into data and the exploitation of their social labor.

scholarly journals The Application of Social Networks in Assisting out-of-class Interactions: A case Study of the Faculty of Economics and Business Administration, Dalat University

2017 ◽  
Vol 33 (3) ◽  
Author(s):  
Nguyen Van Tuan ◽  
Nguyen Thanh Hong An
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Teaching And Learning ◽  
Business Administration ◽  
Positive Feedbacks ◽  
Pilot Scheme ◽  
Academic Activities ◽  
The Social ◽  
Academic Year

The purpose of this study is to evaluate the application of social networks in assisting out-of-class interactions in the context of Vietnam. A group of lecturers and students from the Faculty of Economics and Business Administration, Dalat University was chosen to participate in a pilot scheme, using a social network called Edmodo to assist the out-of-class interactions between lecturers and students in the courses in charge in one academic year. The results show that the social network improves the efficiency of out-of-class interactions among participants and receive positive feedbacks from both students and lecturers. The results also indicate that the use of social networks in education is feasible and will improve the teaching and learning efficiency. However, the results also suggest that lecturers should carefully plan academic activities and provide students with proper incentives to motivate their participation into the class online interactive activities.

How Social Networks Shape Social Comparison

2019 ◽  
pp. 443-461
Author(s):  
Jingwen Zhang ◽  
Damon Centola
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Comparison ◽  
Online Social Networks ◽  
Experimental Studies ◽  
Social Perceptions ◽  
Negative Consequences ◽  
Online Networks ◽  
Comparison Research ◽  
The Social

While social comparison research has focused on the processes and consequences of how the comparer gleans information from the comparison other (individual or group), recent research on social networks demonstrates how information and influence are distributed across persons in a network. This chapter reviews social influence processes in social networks. The authors first review recent research on social comparison and its negative consequences in online social networks. Then the authors delve into discussing the social network causes of biased social perceptions online and how this can be remedied by building more accurate perceptions through constructed online networks. Lastly, the authors discuss findings from recent experimental studies that illustrate how constructed online networks can harness social comparison to induce significant changes in health behavior.

Preventing Identity Disclosure in Social Networks Using Intersected Node

2016 ◽  
Vol 10 (3) ◽  
pp. 25-41 ◽  
Author(s):  
Amardeep Singh ◽  
Divya Bansal ◽  
Sanjeev Sofat
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Online Social Networks ◽  
Privacy And Security ◽  
Identity Disclosure ◽  
The Social ◽  
Confidential Data ◽  
Noise Nodes ◽  
Graphical Structure

Social networks like Facebook, Twitter, Pinterest etc. provide data of its users to the demanding organizations to better comprehend the quality of their potential clients. Publishing confidential data of social network users in its raw form raises several privacy and security concerns. Recently, some anonymization techniques have been developed to address these issues. In this paper, a technique to prevent identity disclosure through structure attacks has been proposed which not only prevents identity disclosure but also preserves utility of data published by online social networks. Algorithms have been developed by using noise nodes/edges with the consideration of introducing minimum change in the original graphical structure of social networks. The authors' work is unique in the sense that previous works are based on edge editing only but their proposed work protects against structure attacks using mutual nodes in the social network and the effectiveness of the proposed technique has been proved using APL (Average Path Length) and information loss as parameters.

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