Personality Predictions Based on User Behavior on the Facebook Social Media Platform

Tadesse, Michael M.; Lin, Hongfei; Xu, Bo; Yang, Liang

doi:10.1109/access.2018.2876502

Cited by 147 publications

(69 citation statements)

References 42 publications

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“…In order to quantify the importance of each feature, Pearson correlation coefficient analysis was conducted between three sets of features and figures. Three machine learning algorithms were used to compare with the primary classifier, namely Support Vector Machine (SVM), Logistic Regression and Gradient Boosting.The XGBoost classifier-based personality predicti on scheme exceeds the average baseline for all function sets, with a maximum forecast precision of 74.2 percent [6]. The user's behavioral traits are linked to their personality.…”

Section: B General Framework For Personality Predictionmentioning

confidence: 95%

“…In addition to the explosive popularity of social media, online social networks have conducted numerous studies for personality recognition [2][3][4][5][6].Author Tommy Tandera [2] has developed a prediction system that can automatically predict user personality based on their activities on Facebook. This study utilizes the personality of Big Five.…”

Section: A Research Reviewmentioning

confidence: 99%

“…Words to stop are not erased. Author M. Tadesse [6] used the myPersonality project information set to examine the existence of social network structures and language characteristics related to personality relationships. They also analyzed and compared four models of machine learning and performed the correlation between each of the feature sets and character traits.…”

Section: A Research Reviewmentioning

confidence: 99%

“…Based on the scores of mutual links, the items could be suggested to an individual customer. Using a collaborative filtering method, users with comparable tastes could be chosen and recommended for the items [6].Fuzzy logic could be used to convert each personality trait score to actual terms in natural language in the proposed model so that output of the model would be more informative [8]. The suggested framework could use distinct machine learning algorithms other than SVM and separate domain datasets to enhance precision.…”

Section: Future Directionsmentioning

confidence: 99%

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Personality Prediction from Social Networks text using Machine Learning

Bhamare*,

Kumar

2019

IJRTE

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Personality, a typical way of thinking, feeling, and behaviour. Personality embraces moods, attitudes and views and is expressed most obviously in relationships with others. It involves both intrinsic and acquired behavioural features that differentiate one individual from another and can be found in the relationships of people with the surroundings and with the social group. With the development of social networks, a broad variety of techn iques have been developed to identify user personalities based on their social activities and language usage practices. In terms of distinct machine learning algorithms, information sources and function sets, particular methods vary. Personality prediction has been an important research topic for describing user profiles and person not only in psychology but also in computer science. This paper presents a systematic survey of current work done of personality prediction from social networks. We also prepared a Comparison chart of existing techniques for personality prediction on the basis of relevant parameters. Based on this survey, we finally presented a few future research directions related to personality prediction.

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Section: B General Framework For Personality Predictionmentioning

confidence: 95%

Section: A Research Reviewmentioning

confidence: 99%

Section: A Research Reviewmentioning

confidence: 99%

Section: Future Directionsmentioning

confidence: 99%

See 2 more Smart Citations

Personality Prediction from Social Networks text using Machine Learning

Bhamare*,

Kumar

2019

IJRTE

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“…Michael et al [5] have investigated approaches that define user characteristics based on social activities and language usage habits. In this study, machine learning algorithms, data sources and feature sets are considered.…”

Section: Introductionmentioning

confidence: 99%

Event Characteristics of Crypto Currency and Security

2019

IJRTE

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Recently, there has been a continuous occurrence of a security incident on a crypto currency exchange. This background is not related to the current social situation. This is because the social interest in crypto currency provides an attacker with a chance to attack. In this paper, we have started to investigate the relationship between crypto currency and security incidents of block chain. This paper focuses on analysis of crypto currency event of block chain. In this paper, we analyzed the amount of Google data retrieval around specific keywords during a specific period. And we analysis the relevance of this keyword to specific keywords related to security. For example, we analyzed the decrypted bitch coin or etherium, the nice money exchange, nicehash, coincheck, BTCglobal, BITGRAIL, Blackwallet. We are focused on the relationship between the time of the security incident and the public awareness of the related crypto currency exchange. According to the results of the study, it can be assumed that a security incident occurred at a certain point in each exchange. Through this study, we were able to confirm public interest in crypto currency miners. I was able to confirm the degree of interest by time. Cryptographic digger was mainly focused on BitMiner, CGMiner, MultiMiner, and BFGMiner. Most of the public interest in these mining equipment is peaking in December 2017. We also looked at public interest in cryptic bit coin and etherium, mainly in December 2017. The results of this paper can be used to analyze the point of time of the attack on the crypto currency exchange. Crypto currency exchange attacks will continue to occur in the future. If so, when is this attack going to take place? At that point, we need to know at what point the exchange will have public interest. At that point, we should also look at the exchange for vulnerabilities.

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EEG based personality prediction using genetic programming

Bhardwaj

Tomar

Sakalle

et al. 2023

Asian Journal of Control

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The reliable correlation between personality and brain signal ensures that inferences from cognitive processes can be achieved. This research aims primarily to predict one's personality using brain signals. On grounds of Psychology, the inference of personality in this work is performed on the basis of the Myers–Briggs Type Indicator (MBTI) personality inventory. Personality consists of different types of thinking, feeling and behavior patterns. EEG signals are produced when a person is exposed to situations or scenarios via visual information and experiences various emotions or sentiments. In this study, by evaluating brain waves while a person watches personality traits elicitation materials, the identification of the personality traits of an individual is done. The elicitation materials used for the collection of the dataset comprise approximately 50 videos with the pre‐defined personality of the dramatic personae and therefore, it is considered to be the ground truth for the experimental procedure of this work. For creating a dataset, sixty participants contributed and gave brain signals. The GP model with the proposed BSH crossover, known as the BSHGP model, is implemented. The maximum performance of the BSHGP model for a 10‐fold partition scheme is 84.34%.

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Personality Predictions Based on User Behavior on the Facebook Social Media Platform

Cited by 147 publications

References 42 publications

Personality Prediction from Social Networks text using Machine Learning

Personality Prediction from Social Networks text using Machine Learning

Event Characteristics of Crypto Currency and Security

EEG based personality prediction using genetic programming

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