Two-Faced Humans on Twitter and Facebook: Harvesting Social Multimedia for Human Personality Profiling

Yang, Qi; Farseev, Aleksandr; Filchenkov, Andrey

doi:10.1145/3463944.3469270

Cited by 6 publications

(4 citation statements)

References 35 publications

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“…In this research, it is necessary to test the efficiency and prediction accuracy of an algorithm, so two sets of data are used in the research, the training, and the testing data. They are all collected from the Personality Cafe website forums randomly [7]. The training data consists of 6505 rows, with 2 columns and there are 2169 rows with 2 columns in test data set.…”

Section: Datasetmentioning

confidence: 99%

Prediction of MBTI Personality Leveraging Machine Learning Algorithms

2023

ACE

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In this study, the author attempted to implement a machine learning approach to determine users' corresponding MBTI personality types by relying only on the content of their online forum postings. Models based on different algorithms are built and trained, and the natural language of the collected data set is converted into machine language for machine learning and used in subsequent tests to determine the correctness of the predicting results. The data set is collected from the forum and divided into two parts, the training set is leveraged to train the model and the test data set is leveraged to make personality predictions and compare with the training data set to measure the correctness of the predicting outcomes. The results show that logistic regression algorithm and vectorized representation of text with TfidfVectorizer can best accomplish the prediction task. This study completed a preliminary comparison of algorithms for personality prediction from text, which became the basis for subsequent personality model predictions using other media.

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Section: Datasetmentioning

confidence: 99%

Prediction of MBTI Personality Leveraging Machine Learning Algorithms

2023

ACE

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“…Another work by Yang et al, 87 used the social media posts from various sources, along with the Myers–Briggs type indicator scores. They found out that binary classification is more accurate than classifying the whole personality.…”

Section: Literature Reviewmentioning

confidence: 99%

Psychometric profiling of individuals using Twitter profiles: A psychological Natural Language Processing based approach

Rathi

Verma

Jain³

et al. 2022

Concurrency and Computation

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The recent pandemic saw the operations of many businesses shifting to virtual mode. Tasks like psychometric analysis of individuals, for various applications, are conducted online. In this article, we introduce a novel system to analyze the semantics of an individual's tweets from their Twitter profile using LIWC and SALLEE scores. These scores can be used to evaluate less fortunate, thin‐filed candidates using their Twitter profiles. With increased access to phones and the internet, many organizations are focusing on making credit systems available to the masses by introducing psychometric analysis. This article proposes a dynamic model for evaluating the personality of a Twitter user using the textual content shared on their page. The model will allow stakeholders to ascertain the personality of user according to any personality model. To analyze if this is viable and flexible approach to model any kind of personality model, we take MBTI personality dataset and train classifier to predict personality types. Then these results are correlated with a linguistic score to find correlation between the two. We found that proposed approach, outperformed the other relevant works also some aspects of these linguistic scores show a heavy correlation with certain personality types.

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“…While multimodal data has already been tackled in these works, all of them still lacked multi-source cross-social network data processing [16], which limits their applicability in the majority of real-world scenarios. Therefore, in this work, we base our personality profiling on the PERS framework [79] that is able to learn from multi-view data for personality profiling by efficiently leveraging highly varied data from diverse social multimedia sources.…”

Section: Related Workmentioning

confidence: 99%

“…To obtain a representation of personality traits, we used the PERS model proposed in [79]. This model predicts user personality traits from multi-modal inputs (collected user posts), producing four classifiers for each of the four MBTI trait pairs (E-I, S-N, T-F, and J-P).…”

Section: User Representation Learningmentioning

confidence: 99%

Personality-Driven Social Multimedia Content Recommendation

Yang,

Nikolenko,

Huang

et al. 2022

Preprint

Self Cite

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Social media marketing plays a vital role in promoting brand and product values to wide audiences. In order to boost their advertising revenues, global media buying platforms such as Facebook Ads constantly reduce the reach of branded organic posts, pushing brands to spend more on paid media ads. In order to run organic and paid social media marketing efficiently, it is necessary to understand the audience, tailoring the content to fit their interests and online behaviours, which is impossible to do manually at a large scale. At the same time, various personality type categorization schemes such as the Myers-Briggs Personality Type indicator make it possible to reveal the dependencies between personality traits and user content preferences on a wider scale by categorizing audience behaviours in a unified and structured manner. Still, McKinsey-style manual categorization is a very labour-intensive task that is probably impractical in a real-world scenario, so automated incorporation of audience behaviour and personality mining into industrial applications is necessary. This problem is yet to be studied in depth by the research community, while the level of impact of different personality traits on content recommendation accuracy has not been widely utilised and comprehensively evaluated so far. Even worse, there is no dataset available for the research community to serve as a benchmark and drive further research in this direction. The present study is one of the first attempts to bridge this important industrial gap, contributing not just a novel personality-driven content recommendation approach and dataset, but also facilitating a real-world ready solution which is scalable and sufficiently accurate to be applied in real-world settings. Specifically, in this work we investigate the impact of human personality traits on the content recommendation model by applying a novel personality-driven multi-view content recommender system called Personality Content Marketing Recommender Engine, or PersiC. Our experimental results and real-world case study demonstrate not just PersiC's ability to perform efficient human personality-driven multi-view content recommendation, but also allow for actionable

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Two-Faced Humans on Twitter and Facebook: Harvesting Social Multimedia for Human Personality Profiling

Cited by 6 publications

References 35 publications

Prediction of MBTI Personality Leveraging Machine Learning Algorithms

Prediction of MBTI Personality Leveraging Machine Learning Algorithms

Psychometric profiling of individuals using Twitter profiles: A psychological Natural Language Processing based approach

Personality-Driven Social Multimedia Content Recommendation

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