Across the globe, right from people who are technologically naive to people who are with high-technical knowledge, use, share, spread, and connect with each other through online social networks. People start believing and sharing the social network content without any proof of its authenticity. In several cases, the reliability of the information that gets shared between the users remains questionable due to the anonymity of the information creators. In this work, a fuzzy detection algorithm is proposed to identify the trustable content in social media. The proposed methodology is evaluated on Twitter social network platform. By computing the predictive measures, the efficiency of the proposed approach is well established. © The Institution of Engineering and Technology 2019

Nirmala M

Department of Smart Computing

School of Information Technology and Engineering

Vellore Campus

Rajasekhara Babu M

School of Computer Science and Engineering

Fulltext

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

IET Networks

Evolutionary Intelligence

Evolving dictionary based sentiment scoring framework for patient authored text

Online Social networks have gained much prominence in the recent years such that it has become an unavoidable means of daily communication. The element of trust in social networks has been studied ever since the inception of online social networks. Trust in online social networks is extremely fragile in nature due to the virtual connections between users in the network. The level of trustworthiness of each user in a social network varies and is usually computed using reputation level of the users. This paper focuses on identifying the features that determine the trust of a user in online social networks using benchmark datasets. We propose a new probabilistic reputation feature model that is better than the raw reputation features. The enhanced trust prediction framework has been tested and validated on three benchmark datasets namely Wikipedia election dataset, Epinions dataset and Slashdot dataset. The proposed probabilistic feature enhances the overall accuracy, F1 score, and area under the ROC for the classifier results significantly. The results have been compared with other state of the art techniques and are found to be efficient. © 2016 Elsevier B.V.

Neurocomputing

An enhanced trust prediction strategy for online social networks using probabilistic reputation features

There has been a surge in the usage and spread of online social networks in the past few years. This paper focuses on online social networks, which is the most prominent complex network in the modern world. People use online social networks for making friendships, learning, for entertainment and to review product items. This expanded impact of online social networks has prompted the rise of a research area named online social network analysis. This paper reflects the research aspects in the detection of influential nodes in online social networks with respect to topological properties, network generation models and analytical measures in online social networks. We have clearly presented the concepts, issues and future research directions in the influential node detection in online social network analysis. The factors and conditions for the evolution and growth of online social networks and the pre-requisites for generating a random graph for simulation of online social networks have been discussed. The paper gives a clear direction on the relation between computation of influential users and the properties, models and measures in online social networks. © 2017 Inderscience Enterprises Ltd.

International Journal of Web Based Communities

A survey on topological properties, network models and analytical measures in detecting influential nodes in online social networks

The internet era has created highly favourable circumstances for business and online services. The need of getting personified recommendations from the increasing pile of information had forced researchers to build recommendation system. In most of the e-commerce websites, recommendation preferences are made by analysing user's behaviour, existing relevant information, browsing patterns, transactional data, item rating and by extracting information from social network profiles. Recommendations predicted from this kind of information can also go futile. Since many of the users gives irrelevant information and many change their views with changing in timely trends. In our work, we are concentrating on generating recommendations based on user's locality. Initially, offline clusterisation is done based on existing location data using CNM-algorithm. The cluster that corresponds to the user request is analysed, and fuzzy preference-based tree is constructed to generate recommendations. © Copyright 2016 Inderscience Enterprises Ltd.

International Journal of Business Intelligence and Data Mining

A locality centred recommendation system combining CNM clustering technique with fuzzy preference tree-based ranking algorithm

Online social networks have gained importance in the past few years and have become an inseparable form of communication. People use online social networks for making friends, gaining knowledge, for entertainment and to review products. This increased influence of social networks has led to the emergence of a research area named online social network analysis. In this paper we propose a model fuzzy inference system that analyze and quantify the network measures used in the analysis of online social networks. We have divided the network measures into two classes namely connection based measures and network structure based measure. We have linked the connection based measures with the network structure based measure and have produced a fuzzy inference system for online social network analysis. © 2015 IEEE.

Journal	IET Networks
Publisher	Institution of Engineering and Technology (IET)
ISSN	2047-4954
Open Access	0