Exploring the Properties of Online Social Network Data and Their Implications for Consumer Social Data Analytics

2014 ◽  
pp. 210-230
Author(s):  
Yamen Koubaa
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Consumer Behavior ◽  
Online Social Networks ◽  
Data Analytics ◽  
Online Social Network ◽  
Network Data ◽  
Social Data ◽  
Social Network Data ◽  
Consumer Data

The prediction of consumer behavior is largely based on the analysis of consumer data using statistics as a tool for prediction. Thanks to online social networks, large quantities of heterogeneous consumer data are now available at competitive costs. Though they have much in common with conventional data, online social network datasets display several different properties. The exploration of these unique properties is indispensable to insuring the accuracy of predictions and data analytics. This chapter presents online social data, discusses seven properties of online social network data, suggests some analysis tools, and draws implications regarding the use of social data analytics.

scholarly journals QUADRIVEN: A Framework for Qualitative Taxi Demand Prediction Based on Time-Variant Online Social Network Data Analysis

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 4882 ◽  
Author(s):  
Fernando Terroso-Saenz ◽  
Andres Muñoz ◽  
José Cecilia
Keyword(s):  
Social Network ◽  
Data Analysis ◽  
Online Social Networks ◽  
Road Traffic ◽  
Online Social Network ◽  
Network Data ◽  
Regression Problem ◽  
Factors Affecting ◽  
Demand Prediction ◽  
Social Network Data

Road traffic pollution is one of the key factors affecting urban air quality. There is a consensus in the community that the efficient use of public transport is the most effective solution. In that sense, much effort has been made in the data mining discipline to come up with solutions able to anticipate taxi demands in a city. This helps to optimize the trips made by such an important urban means of transport. However, most of the existing solutions in the literature define the taxi demand prediction as a regression problem based on historical taxi records. This causes serious limitations with respect to the required data to operate and the interpretability of the prediction outcome. In this paper, we introduce QUADRIVEN (QUalitative tAxi Demand pRediction based on tIme-Variant onlinE social Network data analysis), a novel approach to deal with the taxi demand prediction problem based on human-generated data widely available on online social networks. The result of the prediction is defined on the basis of categorical labels that allow obtaining a semantically-enriched output. Finally, this proposal was tested with different models in a large urban area, showing quite promising results with an F1 score above 0.8.

scholarly journals Privacy-Preserving Assessment of Social Network Data Trustworthiness

2014 ◽  
Vol 23 (02) ◽  
pp. 1441004 ◽  
Author(s):  
Chenyun Dai ◽  
Fang-Yu Rao ◽  
Traian Marius Truta ◽  
Elisa Bertino
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Online Social Networks ◽  
Network Data ◽  
Challenging Problem ◽  
Privacy Concerns ◽  
Useful Knowledge ◽  
Social Network Data ◽  
Series Of Experiments ◽  
Data Trustworthiness

Extracting useful knowledge from social network datasets is a challenging problem. While large online social networks such as Facebook and LinkedIn are well known and gather millions of users, small social networks are today becoming increasingly common. Many corporations already use existing social networks to connect to their customers. Seeing the increasing usage of small social networks, such companies will likely start to create in-house online social networks where they will own the data shared by customers. The trustworthiness of these online social networks is essentially important for decision making of those companies. In this paper, our goal is to assess the trustworthiness of local social network data by referencing external social networks. To add to the difficulty of this problem, privacy concerns that exist for many social network datasets have restricted the ability to analyze these networks and consequently to maximize the knowledge that can be extracted from them. This paper addresses this issue by introducing the problem of data trustworthiness in social networks when repositories of anonymized social networks exist that can be used to assess such trustworthiness. Three trust score computation models (absolute, relative, and weighted) that can be instantiated for specific anonymization models are defined and algorithms to calculate these trust scores are developed. Using both real and synthetic social networks, the usefulness of the trust score computation is validated through a series of experiments.

scholarly journals How migrants manifest their transnational identity through online social networks: comparative findings from a case of Koreans in Germany

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Sunyoung Park ◽  
Lasse Gerrits
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Online Social Networks ◽  
Online Social Network ◽  
Point Of View ◽  
Comparative Case Study ◽  
Transnational Identity ◽  
Transnational Identities ◽  
Web 3.0 ◽  
Situational Contexts

AbstractAlthough migration has long been an imperative topic in social sciences, there are still needs of study on migrants’ unique and dynamic transnational identity, which heavily influences the social integration in the host society. In Online Social Network (OSN), where the contemporary migrants actively communicate and share their stories the most, different challenges against migrants’ belonging and identity and how they cope or reconcile may evidently exist. This paper aims to scrutinise how migrants are manifesting their belonging and identity via different technological types of online social networks, to understand the relations between online social networks and migrants’ multi-faceted transnational identity. The research introduces a comparative case study on an online social movement led by Koreans in Germany via their online communities, triggered by a German TV advertisement considered as stereotyping East Asians given by white supremacy’s point of view. Starting with virtual ethnography on three OSNs representing each of internet generations (Web 1.0 ~ Web 3.0), two-step Qualitative Data Analysis is carried out to examine how Korean migrants manifest their belonging and identity via their views on “who we are” and “who are others”. The analysis reveals how Korean migrants’ transnational identities differ by their expectation on the audience and the members in each online social network, which indicates that the distinctive features of the online platform may encourage or discourage them in shaping transnational identity as a group identity. The paper concludes with the two main emphases: first, current OSNs comprising different generational technologies play a significant role in understanding the migrants’ dynamic social values, and particularly, transnational identities. Second, the dynamics of migrants’ transnational identity engages diverse social and situational contexts. (keywords: transnational identity, migrants’ online social networks, stereotyping migrants, technological evolution of online social network).

Quantifying Virality of Information in Online Social Networks

2012 ◽  
Vol 4 (1) ◽  
pp. 32-45 ◽  
Author(s):  
Abhishek Vaish ◽  
Rajiv Krishna G. ◽  
Akshay Saxena ◽  
Dharmaprakash M. ◽  
Utkarsh Goel
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Online Social Networks ◽  
Online Social Network ◽  
Critical Factors ◽  
Asset Valuation ◽  
Valuation Technique ◽  
Information Item ◽  
Viral Nature ◽  
Over Time

The aim of this research is to propose a model through which the viral nature of an information item in an online social network can be quantified. Further, the authors propose an alternate technique for information asset valuation by accommodating virality in it which not only complements the existing valuation system, but also improves the accuracy of the results. They use a popularly available YouTube dataset to collect attributes and measure critical factors such as share-count, appreciation, user rating, controversiality, and comment rate. These variables are used with a proposed formula to obtain viral index of each video on a given date. The authors then identify a conventional and a hybrid asset valuation technique to demonstrate how virality can fit in to provide accurate results.The research demonstrates the dependency of virality on critical social network factors. With the help of a second dataset acquired, the authors determine the pattern virality of an information item takes over time.

scholarly journals Defining Inspection Techniques for Detecting Privacy Problems in Online Social Networks

2019 ◽  
Vol 10 ◽  
pp. 35
Author(s):  
Andrey  Rodrigues ◽  
Natasha  M. C. Valentim ◽  
Eduardo  Feitosa
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Empirical Study ◽  
Qualitative Analysis ◽  
Online Social Networks ◽  
Online Social Network ◽  
Effective Alternative ◽  
Daily Lives ◽  
Study Participants ◽  
Inspection Techniques

In the last few years, Online Social Networks (OSN) have experienced growth in the number of users, becoming an increasingly embedded part of people’s daily lives. Privacy expectations of OSNs are higher as more members start realizing potential privacy problems they face by interacting with these systems. Inspection methods can be an effective alternative for addressing privacy problems because they detect possible defects that could be causing the system to behave in an undesirable way. Therefore, we proposed a set of privacy inspection techniques called PIT-OSN (Privacy Inspection Techniques for Online Social Network). This paper presents the description and evolution of PIT-OSN through the results of a preliminary empirical study. We discuss the quantitative and qualitative results and their impact on improving the techniques. Results indicate that our techniques assist non-expert inspectors uncover privacy problems effectively, and are considered easy to use and useful by the study participants. Finally, the qualitative analysis helped us improve some technique steps that might be unclear.

Privacy Preserving Approaches for Online Social Network Data Publishing

2021 ◽  
pp. 119-132
Author(s):  
Kamalkumar Macwan ◽  
Sankita Patel
Keyword(s):  
Social Network ◽  
Privacy Preservation ◽  
Differential Privacy ◽  
Online Social Network ◽  
Data Publishing ◽  
Network Data ◽  
User Privacy ◽  
Social Network Data ◽  
Huge Data ◽  
Popularity Prediction

Recently, the social network platforms have gained the attention of people worldwide. People post, share, and update their views freely on such platforms. The huge data contained on social networks are utilized for various purposes like research, market analysis, product popularity, prediction, etc. Although it provides so much useful information, it raises the issue regarding user privacy. This chapter discusses the various privacy preservation methods applied to the original social network dataset to preserve privacy against attacks. The two areas for privacy preservation approaches addressed in this chapter are anonymization in social network data publication and differential privacy in node degree publishing.

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.

scholarly journals SOCIAL NETWORK DATA RETRIEVAL USING SEMANTIC TECHNOLOGY

2017 ◽  
Vol 10 (13) ◽  
pp. 31 ◽  
Author(s):  
Abhilash Srivastav ◽  
Alok Chauhan
Keyword(s):  
Social Network ◽  
Semantic Web ◽  
Web Application ◽  
Data Analytics ◽  
Data Retrieval ◽  
Network Data ◽  
Semantic Technology ◽  
Network Applications ◽  
Social Network Data ◽  
Data Mining Algorithms

Social network data analysis is an important problem due to proliferation of social network applications, amount of data these applications generate and potential of insight based on this big data. The objective of present work is to propose architecture for a semantic web application to facilitate meaningful social network data analytics as well as answering query about concerned ontology. Proposed technique links, on one hand, tools based on semantic technology provided by social network applications with data analytics tools and on the other hand extends this link to ontology authoring tools for further inference.   Results obtained from data analytics tool, results of query on generated ontology and benchmarking of the performance of data analytics tool are shown. It has been observed that a semantic web application utilizing above mentioned tools and technologies is more versatile and flexible and further improvements are possible by applying generic data mining algorithms to the above scenario.    

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