Content-Based Filtering in On-Line Social Networks

Vanetti, Marco; Binaghi, Elisabetta; Carminati, Barbara; Carullo, Moreno; Ferrari, Elena

doi:10.1007/978-3-642-19896-0_11

Cited by 39 publications

(37 citation statements)

References 17 publications

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“…Content Based Filtering in online social network by exploiting ML techniques [2,3,6]The aim of the system is to experimentally appraise an automated system called Filtered Wall. It is able to filter unwanted messages from user wall which is achieved by using Flexible rule based system.…”

Section: Filtered Wallmentioning

confidence: 99%

“…Filtering is based on descriptions of administrator. [2] Filtering is considered as removing of data from an incoming content or finding data in that content. Content-based post filtering service for Online Social Networks (OSNs).…”

Section: Introductionmentioning

confidence: 99%

“…Content-based post filtering service for Online Social Networks (OSNs). Our system allows OSN users to have a direct control on the messages that are posted on their walls [2] [3]. A user can follow any other user only with their consent [4].…”

Section: Introductionmentioning

confidence: 99%

“…We believe that the proposed strategy is a key service for social networks in that in today social networks users have little control on the posts displayed on their walls. For example, Facebook allows users to filter the contents in their walls (i.e., friends, friends of friends, or defined groups of friends) [2]. Filtering is based on descriptions of administrator.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Secure Authentication Scheme for Social Networking

Primya¹,

Kanagaraj²,

Suresh³

et al. 2017

International Advanced Research Journal in Science, Engineering

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A social networking service is an online platform that is used by people to build social networks or social relations with other people who share similar personal or career interests, activities, backgrounds or real-life connections. It plays a major role in our society. Privacy concerns with social networking services is a subset of data privacy, involving the right of mandating personal privacy concerning storing, re-purposing, provision to third parties, and displaying of information pertaining to oneself via the Internet. Maintaining privacy has a major problem, our proposed system provides additional security to the social network which is achieved by using Adaptive Privacy Policy Prediction (A3P).This system provides the privacy setting for the user. The features are selecting friend from friend list, follower request, news feed filtering, block the person and send the notification to the user when the violation is found. Our solution relies on an image classifies which may be related with similar policies, and on a policy prediction algorithm to automatically generate a policy for all the image, also according to users' social features.

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Section: Filtered Wallmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Secure Authentication Scheme for Social Networking

Primya¹,

Kanagaraj²,

Suresh³

et al. 2017

International Advanced Research Journal in Science, Engineering

View full text Add to dashboard Cite

show abstract

“…Which has content based recommending system that develop information extraction and machine learning algorithm for text classifications. B.Carminati, M.Vanetti, E.ferrari, M.Carullo, and E.Binaghi [7] Performance of classification includes different semantics for filtering rules is considered as the main aim. This system can usually take decision about the messages which has to block, due to the tolerance depends on preclassified data set.…”

Section: Related Workmentioning

confidence: 99%

A Survey on Filtering Unwanted Messages from Online Social Network Users Wall Using Text Classification

Bagal¹,

Gadage²

2014

IJIRCCE

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-Today one of the best communication between people is using the Online social network to which they can share information. Therefore the users who are using Online Social Networks(OSN) requires control over the unwanted messages that are posted on thier walls and to avoid the unwanted content which is displayed on private space of user. OSN provide us little support to the users requirement. To provide this, we propose a system which allows OSN users to have a direct control on the messages posted on users walls. This is achieved through a flexible rule based system in which users to customize the filtering criteria to be applied to their walls, and a Machine Learning (ML) based soft classification and short text classification which automatically produce membership labels in support of content-based filtering of a unwanted messages.

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Controlling and Mitigating Targeted Socio-Economic Attacks

Kumar

Dasari

Nath

et al. 2016

Social Media: The Good, the Bad, and the Ugly

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The transformation of social media has paved a way to express one's views, ideas, and opinions in an effective and lucid manner which has resulted in its increased popularity. However, there are both pros and cons of this sociotechnological revolution. This may lead to its misuse with planned and targeted attacks which often have the potential of massive economic effects. This paper articulates the negative aspects, especially, of how the social media is being misused for greedy needs. Spammers may defame the product to achieve their greedy goal of earning more profit by decreasing the competing effect of their opponents. This paper discusses, analyzes and proposes two novel techniques by which one can either decrease or completely abolish these types of socioeconomic attacks.

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Content-Based Filtering in On-Line Social Networks

Cited by 39 publications

References 17 publications

Secure Authentication Scheme for Social Networking

Secure Authentication Scheme for Social Networking

A Survey on Filtering Unwanted Messages from Online Social Network Users Wall Using Text Classification

Controlling and Mitigating Targeted Socio-Economic Attacks

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