scispace - formally typeset
Proceedings ArticleDOI

Study and analysis of Social network Aggregator

TLDR
A review on different Social network Aggregators and issues in integrating social network is given, Exposing criminal behaviours in e-commerce, computer intrusions identification, detecting health problems, analysing satellite images.
Abstract
Organizing contacts, friends, sharing thoughts, emotions searching content and data are the basic means that a social networking site provides. By sharing, and managing content the users form a social network for example, Facebook, Linkedin, twitter, Orkut, etc. A user may exist on several different social networking sites and the problem to maintain the account on these networks prevails. This brings us to define a Social network aggregation i.e. collecting the social content from different social networks and integrate it at a single location/site. It is an attempt to organize a user's social networking experience as whole. This paper gives a review on different Social network Aggregators and issues in integrating social network. Exposing criminal behaviours in e-commerce, computer intrusions identification, detecting health problems, analysing satellite images.

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Citations
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Journal ArticleDOI

A user-centered approach for integrating social data into groups of interest

TL;DR: A new user-centered approach for integrating social data into groups of interest that makes it possible for a group to tap into its members' social data scattered over different social network sites and extract from these data the information relevant to the group's topic of interests.
Journal ArticleDOI

A user-centered and group-based approach for social data filtering and sharing

TL;DR: A user-centered and group-based approach for social data filtering and sharing that allows users to aggregate their social data from different SNSs and to extract relevant contents, and is expected to extend its first user- centered purpose by allowing group- based information sharing and management.
Book ChapterDOI

Online Social Networks Misuse, Cyber Crimes, and Counter Mechanisms

TL;DR: This chapter presents an overview of various cyber-crimes associated with OSN environment to gain insight into ongoing cyber-attacks and counter mechanisms in the form of tools, techniques, and frameworks are suggested.
Journal ArticleDOI

Online Social Network Management Systems: State of The Art☆

TL;DR: A state of the art of the existing platforms for managing online social networks is presented with a framework that guides the analysis of each social media management system according to its own characteristics and the characteristics of the online social media content that the system helps to manage.
References
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Proceedings ArticleDOI

GroupLens: an open architecture for collaborative filtering of netnews

TL;DR: GroupLens is a system for collaborative filtering of netnews, to help people find articles they will like in the huge stream of available articles, and protect their privacy by entering ratings under pseudonyms, without reducing the effectiveness of the score prediction.
Proceedings ArticleDOI

Sampling from large graphs

TL;DR: The best performing methods are the ones based on random-walks and "forest fire"; they match very accurately both static as well as evolutionary graph patterns, with sample sizes down to about 15% of the original graph.
Proceedings ArticleDOI

A face(book) in the crowd: social Searching vs. social browsing

TL;DR: It is suggested that users are largely employing Facebook to learn more about people they meet offline, and are less likely to use the site to initiate new connections.
Proceedings ArticleDOI

Walking in Facebook: A Case Study of Unbiased Sampling of OSNs

TL;DR: The goal in this paper is to obtain a representative (unbiased) sample of Facebook users by crawling its social graph using several candidate techniques, and introduces online formal convergence diagnostics to assess sample quality during the data collection process.
Proceedings ArticleDOI

Short and tweet: experiments on recommending content from information streams

TL;DR: This paper studied content recommendation on Twitter to better direct user attention and explored three separate dimensions in designing such a recommender: content sources, topic interest models for users, and social voting.