scholarly journals Mining social network data for personalisation and privacy concerns: a case study of Facebook's Beacon

2013 ◽  
Vol 13 (2) ◽  
pp. 173 ◽  
Author(s):  
Arshad Jamal ◽  
Jane Coughlan ◽  
Muhammad Kamal
Keyword(s):  
Social Network ◽  
Network Data ◽  
Privacy Concerns ◽  
Social Network Data

scholarly journals Online Data Preprocessing: A Case Study Approach

2019 ◽  
Vol 9 (4) ◽  
pp. 2620
Author(s):  
Mohammed Zuhair Al-Taie ◽  
Seifedine Kadry ◽  
Joel Pinho Lucas
Keyword(s):  
Social Network ◽  
Data Preprocessing ◽  
Quality Data ◽  
The Internet ◽  
Network Data ◽  
Quality Of Data ◽  
Standard Format ◽  
Social Network Data

<span lang="EN-US">Besides the Internet search facility and e-mails, social networking is now one of the three best uses of the Internet. A tremendous number of volunteers every day write articles, share photos, videos and links at a scope and scale never imagined before. However, because social network data are huge and come from heterogeneous sources, the data are highly susceptible to inconsistency, redundancy, noise, and loss. For data scientists, preparing the data and getting it into a standard format is critical because the quality of data is going to directly affect the performance of mining algorithms that are going to be applied next. Low-quality data will certainly limit the analysis and lower the quality of mining results. To this end, the goal of this study is to provide an overview of the different phases involved in data preprocessing, with a focus on social network data. As a case study, we will show how we applied preprocessing to the data that we collected for the Malaysian Flight MH370 that disappeared in 2014.</span>

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.

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