Recurrent Neural Network (RNN) to Analyse Mental Behaviour in Social Media

Bouarara, Hadj Ahmed

doi:10.4018/ijssci.2021070101

Cited by 36 publications

(8 citation statements)

References 11 publications

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“…Some recent studies have shown that there is a huge negative impact on the mental health of the user on social media. Bouarara 202 proposed a technique for detecting psychological disorders' of Twitter users by analyzing their tweets and using recurrent neural networks (RNN). Chen et al 203 also designed a framework for checking the psychological characteristics of an OSN user and predicting his/her consumption behaviors by analyzing his/her marketing mining data.…”

Section: Ml‐based Solutions For Osn Platformmentioning

confidence: 99%

A comprehensive survey on online social networks security and privacy issues: Threats, machine learning‐based solutions, and open challenges

Bhattacharya

Roy

Chattopadhyay

et al. 2022

Security and Privacy

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Over the past few years, online social networks (OSNs) have become an inseparable part of people's daily lives. Instead of being passive readers, people are now enjoying their role as content contributors. OSN has permitted its users to share their information including the multimedia content. OSN users can express themselves in virtual communities by providing their opinions and interacting with others. As a consequence, the privacy and security threats in OSNs have emerged as a major concern to the research and business world. In the recent past, a number of survey works have been conducted to discuss different security and privacy threats in OSNs. However, till date, no survey work has been conducted that aims to classify and analyze various machine learning (ML)‐based solutions adapted for the security defense of OSNs. In this survey article, we present a detailed taxonomy with a classification of various works done on various security attacks in OSNs. We then review and summarize the existing state of art survey works on OSN security, and indicate the merits and limitations of these survey works. Next, we review all recent works that aim to provide ML‐based solutions toward defense of security attacks on OSNs. Finally, we discuss the future road‐map on OSN security and provide a comprehensive analysis on the open research issues with feasible measurements and possible solutions.

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Section: Ml‐based Solutions For Osn Platformmentioning

confidence: 99%

A comprehensive survey on online social networks security and privacy issues: Threats, machine learning‐based solutions, and open challenges

Bhattacharya

Roy

Chattopadhyay

et al. 2022

Security and Privacy

View full text Add to dashboard Cite

show abstract

“…Sahoo et al focused on the detection of fake news in OSNs 18 and proposed an approach for fake news detection on Facebook in which multiple features associated with Facebook accounts were incorporated and analyzed through deep learning. Aiming at helping users in OSNs to deal with mental health issues, Bouarara proposed an approach to identifying people with the behavior of mental disorders in OSNs 19 in which the recurrent neural network (RNN) was adopted and shown to exhibit some advantages. Deep learning has provided a new direction for private inference, making it worthwhile to design deep learning based methods in private inference to improve both effectiveness and efficiency.…”

Section: Related Workmentioning

confidence: 99%

Social influence‐based privacy inference attacks in online social networks

Zhu

et al. 2021

Security and Privacy

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Privacy inference attacks in online social networks aim at inferring hidden attributes of target users based on released attributes of the users as well as information about social relationships in the networks. Although many privacy inference methods have been proposed in recent years, most of the methods have mostly focused on pursuing high accuracy of the inference results without paying too much attention on identifying users who make the most contributions to the inference, making it less useful in the development of effective countermeasures. In this paper, by taking full consideration of the roles of users in privacy inference, we propose a privacy inference method based on graph attention networks that can provide information about not only the private attributes of the target users, but also the importance of the users in the process of inferring the results. We also design a novel mechanism for the selection of influential users to make our proposed method adaptive to the characteristics of user influence diffusion, resulting in involving fewer number of users in privacy inference and hence improving the efficiency. We also performed some experiment to evaluate the proposed method using a real online social network dataset to show the effectiveness of the proposed method. Comparison with other comparable methods also shows that the results of our proposed method are more accurate.

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“…People are increasingly reliant on the Internet for information and also to fulfill other social needs. Scholars have conducted a series of studies based on big data on the Internet, such as sentiment analysis using big data to forewarn users of mental health problems ( Mohammed et al, 2022 ; Bouarara, 2021 ), account security ( Masud et al,2020 ), construction of identification systems ( Campomanes-Alvarez et al, 2019 ), conducting academic research ( Tembhurne et al, 2022 ; Fayoumi & Hajjar, 2020 ; Noor et al,2020 ), and even to predict global sustainable development goals ( Chopra et al, 2022 ), and a host of other issues.…”

Section: Introductionmentioning

confidence: 99%