Personality prediction model for social media using machine learning Technique

Kamalesh, Murari Devakannan; Bharathi, B.

doi:10.1016/j.compeleceng.2022.107852

Cited by 22 publications

(5 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The prediction of human behavior, personality, and emotions in general, and using data from social media platforms, in particular, is receiving tremendous attention among researchers 23 . Some of these studies are summarized in Table 2 .…”

Section: Related Workmentioning

confidence: 99%

A machine learning model to predict privacy fatigued users from social media personalized advertisements

Alwafi,

Fakieh

2024

Sci Rep

View full text Add to dashboard Cite

The increasing use of social media platforms as personalized advertising channels is a double-edged sword. A high level of personalization on these platforms increases users’ sense of losing control over personal data: This could trigger the privacy fatigue phenomenon manifested in emotional exhaustion and cynicism toward privacy, which leads to a lack of privacy-protective behavior. Machine learning has shown its effectiveness in the early prediction of people’s psychological state to avoid such consequences. Therefore, this study aims to classify users with low and medium-to-high levels of privacy fatigue, based on their information privacy awareness and big-five personality traits. A dataset was collected from 538 participants via an online questionnaire. The prediction models were built using the Support Vector Machine, Naïve Bayes, K-Nearest Neighbors, Decision Tree, and Random Forest classifiers, based on the literature. The results showed that awareness and conscientiousness trait have a significant relationship with privacy fatigue. Support Vector Machine and Naïve Bayes classifiers outperformed the other classifiers by attaining a classification accuracy of 78%, F1 of 87%, recall of 100% and 98%, and precision of 78% and 79% respectively, using five-fold cross-validation.

show abstract

Section: Related Workmentioning

confidence: 99%

A machine learning model to predict privacy fatigued users from social media personalized advertisements

Alwafi,

Fakieh

2024

Sci Rep

View full text Add to dashboard Cite

show abstract

“…• Privacy fatigue has a greater impact on intention to disclose personal information than on privacy concerns Mobile apps [22] Explore the effect of privacy paradox from the perspective of privacy calculus and privacy fatigue on disclosure intention The prediction of human behavior, personality, and emotions in general, and using data from social media platforms, in particular, is receiving tremendous attention among researchers [23]. For instance, ML is used to predict aggressive behaviors on these platforms, such as cyberbullying.…”

Section: Purposementioning

confidence: 99%

A machine learning model to predict privacy fatigued users from social media personalized advertisements

Alwafi,

Fakieh

2023

Preprint

View full text Add to dashboard Cite

The increasing use of social media platforms as personalized advertising channels is a double-edged sword. A high level of personalization on these platforms increases users’ sense of losing control over personal data: This could trigger the privacy fatigue phenomenon manifested in emotional exhaustion and cynicism toward privacy, which leads to a lack of privacy-protective behavior. Machine learning has shown its effectiveness in the early prediction of people’s psychological state to avoid such consequences. Therefore, this study aims to classify users with low and medium-to-high levels of privacy fatigue, based on their information privacy awareness and big-five personality traits. A dataset was collected from 538 participants via an online questionnaire. The prediction models were built using the Support Vector Machine, Naïve Bayes, K-Nearest Neighbors, Decision Tree, and Random Forest classifiers based on the literature. The results showed that awareness and conscientiousness trait have a significant relationship with privacy fatigue. Support Vector Machine and Naïve Bayes classifiers outperformed the other classifiers by attaining a classification accuracy of 78%, F1 of 87%, recall of 100% and 98%, and precision of 78% and 79% respectively, using 5-fold cross-validation.

show abstract

“…Link prediction focuses on determining the likelihood that an edge will exist in a future state, given the network's current state. In the past, this operation was carried out in a static environment, which means that future links were predicted using a single network snapshot 6 . Both methods are still being investigated in current link prediction research, and basic topological properties that are simple to estimate and provide an unexpectedly deep insight into link probability are still being studied.…”

Section: Introductionmentioning

confidence: 99%

“…In the past, this operation was carried out in a static environment, which means that future links were predicted using a single network snapshot. 6 Both methods are still being investigated in current link prediction research, and basic topological properties that are simple to estimate and provide an unexpectedly deep insight into link probability are still being studied. DL has an exceptional ability to uncover previously hidden patterns and is used to analyze complex data relationships.…”

mentioning

confidence: 99%

Social network based link correlation using graph neural network with deep learning architectures for feature vectors prediction and classification

Sonti,

Mulpuri Santhi Sri,

Pamulapati

2024

Concurrency and Computation

View full text Add to dashboard Cite

SummaryIn recent years, social network analysis has received a lot of interest. A critical area of research in this field is link prediction. Link prediction is researched for other forms of social networks. Still, because social link networks (SLNs) change over time and depend on the discussed topics, this network has unique difficulties. Recent studies have focused on three main issues: extending link prediction to a dynamic environment, forecasting formation, and destroying network linkages that change over time. Although it is a challenging issue, deep learning (DL) techniques have been demonstrated to increase prediction accuracy significantly. This research proposes a novel approach to link correlation for social networks based on DL architectures in feature vector prediction and classification. Here the input data has been processed for smoothening and normalization with noise removal. Then, the feature vector was predicted using a dynamically structured convolutional radial basis neural network for this data. The expected feature vector has been classified using a stochastic gradient‐based graph neural network. The experimental analysis is carried out for various social network data in terms of accuracy of 98%, precision of 85%, recall of 86%, F‐1 score of 75%, AUC of 72%, and RMSE of 76%.

show abstract

Personality prediction model for social media using machine learning Technique

Cited by 22 publications

References 8 publications

A machine learning model to predict privacy fatigued users from social media personalized advertisements

A machine learning model to predict privacy fatigued users from social media personalized advertisements

A machine learning model to predict privacy fatigued users from social media personalized advertisements

Social network based link correlation using graph neural network with deep learning architectures for feature vectors prediction and classification

Contact Info

Product

Resources

About