Analyzing Facebook Activities for Personality Recognition

Laleh, Asadzadeh; Rahimi, Shahram

doi:10.1109/icmla.2017.00-29

Cited by 20 publications

(9 citation statements)

References 13 publications

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“…They focused on the way to predict their personality traits by exploitation the user text information. They used the correlation analysis and principle part investigations to pick out the usage info and so used the multiple correlation models, the grey prediction model and therefore the multitasking model to predict and analyze the results.Author A. Laleh [8] suggested model that gets the users Facebook likes and predicts their Big five trait ratings of personality. The LASSO algorithm was used to pick the finest characteristics of Facebook users and predict the Big five model.…”

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: A Research Reviewmentioning

confidence: 99%

Section: Future Directionsmentioning

confidence: 99%

Personality Prediction from Social Networks text using Machine Learning

Bhamare*,

Kumar

2019

IJRTE

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“…With massive amount of user's data stored on social media, it is possible to predicts user's opinion, interest, and future behaviour based on their footprint. Asadzadeh et al presents a work to predict psychological traits based on their recent activity on social media [8]. Based on their study on 92,255 users, Openness and Extraversion are the easiest to be detected by studying their footprints on social media followed by users with Conscientiousness and Neuroticism.…”

Section: Related Workmentioning

confidence: 99%

Post Cambridge Analytica Fallout: Observing Facebook Users Awareness Regarding Data Security

Tarigan¹,

Zamza²

2018

IJET

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In early 2018, Cambridge Analytica, a UK based political consulting group, was in the middle of the spotlight regarding its activity collecting Facebook users’ personal data. The data was harvested without users’ consent and was used to influence voter opinion to support a presidency campaign. This event triggered numerous movement aimed to inform and motivate the public to be concern of the use of their data in social media. However, whether these actions have raised the awareness of social media users is yet to be investigated. The objective of this paper is to harvest the awareness of social media users in Indonesia. We’ve performed a survey and collected answers from 312 responders who actively use social media in everyday life. The result shows that most of the responders have a low awareness regarding the event and its impact to social media industry. Over 24% of our responders have heard of the event and only 7% of the responders were able to describe the event correctly. Moreover, most of these users did not aware how their profile information in social media may be used to achieve commercial or political purpose by social media industry or collaborating third party without their consent.

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“…Particularly, some research studies focused on predicting personality from various social media tools that have become a popular academic endeavor. Among many studies in the extant literature, some research remarked the existence of important association among personality and various Facebook activities such as likes, tags, status updates, locations, friends, events, and posts (Amichai-Hamburger & Viniztky, 2010; Kalghatgi et al, 2015; Laleh & Shahram, 2017; Tandera et al, 2017; Tareaf et al, 2019; Xue et al, 2018; Zhu, 2020). Some examples regarding the importance of predicting personality from Facebook user generated data could be as follows.…”

Section: Introductionmentioning

confidence: 99%

A Neural Network Approach for Predicting Personality From Facebook Data

Başaran

Ejimogu

2021

Sage Open

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Everyday, social media usage particularly Facebook usage are growing exponentially. Simply, inspecting Facebook usage provides meaningful information concerning users’ daily interactions and hence about their personality traits. Numerous studies have been done to harness such streams of Facebook data to obtain accurate prediction of human behavior, social interactions, and personality. The aim of this study is to build a neural network–based predictive model that uses Facebook user’s data and activity to predict the Big 5 personalities. This study combines the inference features highlighted in three different relevant studies which are; number of likes, events, groups, tags, updates, network size, relationship status, age, and gender. The study was conducted on 7,438 unique Facebook participants obtained from the myPersonality database. The findings of this study showed how much a person’s personality can be predicted only by analyzing their Facebook activity. The proposed artificial neural network model was able to correctly classify an individual’s personality at an 85% prediction accuracy.

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Analyzing Facebook Activities for Personality Recognition

Cited by 20 publications

References 13 publications

Personality Prediction from Social Networks text using Machine Learning

Personality Prediction from Social Networks text using Machine Learning

Post Cambridge Analytica Fallout: Observing Facebook Users Awareness Regarding Data Security

A Neural Network Approach for Predicting Personality From Facebook Data

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