Efficient Data Sharing over Large-Scale Distributed Communities

Li, Juan; Khan, Samee U.; Li, Qingrui; Ghani, Nasir; Min‐Allah, Nasro; Bouvry, Pascal; Zhang, Weiyi

doi:10.1007/978-3-642-21271-0_7

Cited by 5 publications

(3 citation statements)

References 22 publications

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“…and blogs. Most of the surveys centered on the applying of various text mining techniques on unstructured data however do not specifically target the datasets in social networking websites [21][22][23][24][25]. Classification and clustering are the two text mining techniques that are widely used for mining the unstructured text available on the web.…”

Section: Text Miningmentioning

confidence: 99%

Survey on Online Social Networks Analysis Concepts and Knowledge Discovery Techniques

Negm¹,

Mahgoub²

2019

IJCA

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In the recent decade, the Online Social Network (OSN) has gained remarkable attention. Accessing to OSN sites such as Twitter, Facebook, LinkedIn and Google Plus; the most dominant social media in the world, through the internet and the web 2.0 technologies has become more comfortable. These days through these online social networks, it becomes very easy for anyone to meet the people of the same interests for learning and sharing precious information. Online Social Network Analysis (OSNA) is an essential and important technique to understand the social structure, social relationships and social behaviors of OSN. OSNA deals with the interaction between individuals by considering them as nodes of a network whereas their relations are mapped as network edges. Now, it has increased various challenges for the evolution of the web and simultaneously increased the dynamic changes in its structure so it became harder to manually analyze very broad OSN. This survey investigates the current progression in the field of knowledge discovery in OSNA and covers all basic techniques of Data, Text, and Web mining that are widely used for the exploration of the unstructured and structured data available on the OSNA. The targets for OSNA are mainly focused on resources from the web, such as content, structure, and user behaviors. The main goal of this paper is to introduce a roadmap for the researchers who are interesting on the topics of knowledge discovery techniques for discovering totally different trends in OSN data. Discussion of all the challenges that face researchers in OSNA is also included.

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Section: Text Miningmentioning

confidence: 99%

Survey on Online Social Networks Analysis Concepts and Knowledge Discovery Techniques

Negm¹,

Mahgoub²

2019

IJCA

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“…The substantial amount of content generated and shared by social networking users offers new research opportunities across a wide variety of disciplines, including media and communication studies, linguistics, sociology, psychology, information and computer sciences, and education. For example, social media can help to solve communication and coordination problems that might arise due to geographical distances in case of extreme events or emergencies (Li et al, 2011b), or they can increase the effectiveness of social campaigns by helping with the dissemination of the required information (Li et al, 2011a). This situation, in combination with the continuous growth of social media data, creates the imperative need of organising the content.…”

Section: Introductionmentioning

confidence: 99%

Short-text learning in social media: a review

Tommasel

Godoy

2019

The Knowledge Engineering Review

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Social networks occupy a ubiquitous and pervasive place in the life of their users. The substantial amount of content generated and shared by social networking users offers new research opportunities across a wide variety of disciplines, including media and communication studies, linguistics, sociology, psychology, information and computer sciences, or education. This situation, in combination with the continuous growth of social media data, creates an imperative need for content organisation. Thus, large-scale text learning tasks in social environments arise as one of the most relevant problems in machine learning and data mining. Interestingly, social media data pose several challenges due to its sparse, high-dimensional and large-volume characteristics. This survey reviews the field of social media data learning, focusing on classification and clustering techniques, as they are two of the most frequent learning tasks. It reviews not only new techniques that have been developed to tackle the new challenges posed by short-texts, but also how traditional techniques can be adapted to overcome such challenges. Then, open issues and research opportunities for social media data learning are discussed.

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“…Most of the scientific literature (Xu et al , 2008; Tekiner et al , 2009; Li et al , 2011a) focuses on specific techniques of text mining for information extraction from text documents. However, a thorough discussion is lacking on the actual analysis of different text mining approaches.…”

Section: Introductionmentioning

confidence: 99%

A survey on text mining in social networks

Irfan¹,

King²,

Grages³

et al. 2015

The Knowledge Engineering Review

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In this survey, we review different text mining techniques to discover various textual patterns from the social networking sites. Social network applications create opportunities to establish interaction among people leading to mutual learning and sharing of valuable knowledge, such as chat, comments, and discussion boards. Data in social networking websites is inherently unstructured and fuzzy in nature. In everyday life conversations, people do not care about the spellings and accurate grammatical construction of a sentence that may lead to different types of ambiguities, such as lexical, syntactic, and semantic. Therefore, analyzing and extracting information patterns from such data sets are more complex. Several surveys have been conducted to analyze different methods for the information extraction. Most of the surveys emphasized on the application of different text mining techniques for unstructured data sets reside in the form of text documents, but do not specifically target the data sets in social networking website. This survey attempts to provide a thorough understanding of different text mining techniques as well as the application of these techniques in the social networking websites. This survey investigates the recent advancement in the field of text analysis and covers two basic approaches of text mining, such as classification and clustering that are widely used for the exploration of the unstructured text available on the Web.

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Efficient Data Sharing over Large-Scale Distributed Communities

Cited by 5 publications

References 22 publications

Survey on Online Social Networks Analysis Concepts and Knowledge Discovery Techniques

Survey on Online Social Networks Analysis Concepts and Knowledge Discovery Techniques

Short-text learning in social media: a review

A survey on text mining in social networks

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