Automatic Personality Prediction using Deep Learning Based on Social Media Profile Picture and Posts

Jeremy, Nicholaus Hendrik; George, Christian; Kamal, Muhammad Fadil; Suhartono, Derwin; Suryaningrum, Kristien Margi

doi:10.1109/isriti54043.2021.9702873

Cited by 7 publications

(3 citation statements)

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“…However, especially with the emergence of new analytical methods through which users' posts are analyzed via Natural Language Processing or Image Analytics (Egger, 2022), socio-demographic and psychographic characteristics remain completely absent, making it difficult to classify “Instagrammers” in the population. Future research could intertwine the findings from this study with the promising approaches put forth in Ferwerda and Tkalcic (2018) and Jeremy et al (2021) to derive personality traits from Instagram images. In this respect, this study provides a better assessment of Instagram users as well as a better interpretation of Instagram user samples.…”

Section: Discussionmentioning

confidence: 85%

“…The results revealed that people with higher extraversion and lower neuroticism tend to use Instagram longer, higher agreeableness is more likely to be found in users with private profiles and a positive correlation between extraversion and a user’s number of following and followers could be found. Interestingly, there are also a number of publications (Ferwerda and Tkalcic, 2018; Wu et al , 2022; Azucar et al , 2018; Jeremy et al , 2021) that could determine individuals’ personalities based on their Instagram posts alone.…”

Section: Literature Reviewmentioning

confidence: 99%

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Characterizing traveling Instagrammers: an exploration of socio-psychological concepts, travel typologies and posting motives

Egger

2023

CBTH

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Purpose This study aims to understand how traveling Instagrammers should be characterized by bridging socio-psychological concepts with Instagram usage and travel typologies. Design/methodology/approach Using analysis of variance, differences between non-Instagrammers and passive and active users were identified with regard to their personality traits and human values; traveling Instagrammers and their travel types were characterized in more detail using k-means clustering, and, finally, posting behavior was examined using multiple regression. Findings The results demonstrate significant differences between the user groups in terms of their personality traits, their human values and their travel behavior. The multiple regressions provide deeper insights into tourists’ Instagram posting. Research limitations/implications This study was conducted on 1,100 German travelers, meaning that the results are only valid for this cultural context. All data were collected using an online panel and participants received incentives, and thus extrinsic rewards, instead of using merely intrinsic motivation to take part in the survey. Originality/value This study contributes to a better understanding of Instagram usage behavior when traveling and adds valuable insights to this field of research. At the same time, the results are relevant for tourism businesses wishing to optimize their communication and marketing strategies.

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Section: Discussionmentioning

confidence: 85%

Section: Literature Reviewmentioning

confidence: 99%

Characterizing traveling Instagrammers: an exploration of socio-psychological concepts, travel typologies and posting motives

Egger

2023

CBTH

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“…Furthermore, research was conducted on 1290 Twitter users [6] using their profile pictures and tweets to predict their personalities automatically. They extracted facial features using a face detection model combined with smile detection and other features like hue, saturation, and value.…”

Section: Related Workmentioning

confidence: 99%

Analysis and Prediction of Personality Traits Using a Self-Generated Database of Moroccan Instagram Users: Impact of Gender on Image Content and Quantity on Prediction Accuracy

BAHY

ABOUTABIT

Hafidi

2023

Preprint

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Data shared on social networks can reflect numerous information about users, such as personality traits, inclinations, and interests. Consequently, research on predicting user personality from social network data has become increasingly focused. This study focuses on the Instagram application and is the first, to our knowledge, to examine Moroccan Instagram users. To study the effect of personality traits and gender on photo content, we predict and analyze the personalities of female and male Instagram users separately, based on characteristics extracted from the photos they share. This study is the first to predict the personality of both genders separately, given differences in the nature and preferences of genders. Additionally, we examine the effect of the number of images on prediction accuracy. Furthermore, three categories of features have been extracted from the images: visual, emotional, and content. Moreover, the Big Five model is used to represent users' personalities. Additionally, we collected a database of 316 Instagram users; it is the largest compared to previous works and includes users from different regions and social classes, which makes the results more generalizable. A root mean square error (RMSE) related to the [1,5] score scale was used to indicate prediction accuracy. In general, good results were obtained for all traits, with outperforming results in predicting consciousness for females (RMSE=0.59) and openness for males(RMSE=0.59), compared to previous studies.

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Analyzing Instagram Images to Predict Personality Traits

Bahy¹,

Aboutabit²,

Hafidi³

2023

Lecture Notes in Networks and Systems

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Automatic Personality Prediction using Deep Learning Based on Social Media Profile Picture and Posts

Cited by 7 publications

References 20 publications

Characterizing traveling Instagrammers: an exploration of socio-psychological concepts, travel typologies and posting motives

Characterizing traveling Instagrammers: an exploration of socio-psychological concepts, travel typologies and posting motives

Analysis and Prediction of Personality Traits Using a Self-Generated Database of Moroccan Instagram Users: Impact of Gender on Image Content and Quantity on Prediction Accuracy

Analyzing Instagram Images to Predict Personality Traits

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