What your Facebook Profile Picture Reveals about your Personality

Segalin, Cristina; Celli, Fabio; Polonio, Luca; Kosinski, Michal; Stillwell, David; Sebe, Nicu; Cristani, Marco; Lepri, Bruno

doi:10.1145/3123266.3123331

Cited by 82 publications

(54 citation statements)

References 49 publications

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“…With regards to personality prediction, early efforts have shown that bag-of-visual-words and Facebook profile images could predict users' personality [68]. Various sets of features have been obtained from the images of 11,736 Facebook users were extracted to build a computational model which has more predictive power than human raters for predicting similar personality traits [69].…”

Section: Social Media and Image Analysismentioning

confidence: 99%

Multimodal mental health analysis in social media

et al. 2020

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Depression is a major public health concern in the U.S. and globally. While successful early identification and treatment can lead to many positive health and behavioral outcomes, depression, remains undiagnosed, untreated or undertreated due to several reasons, including denial of the illness as well as cultural and social stigma. With the ubiquity of social media platforms, millions of people are now sharing their online persona by expressing their thoughts, moods, emotions, and even their daily struggles with mental health on social media. Unlike traditional observational cohort studies conducted through questionnaires and self-reported surveys, we explore the reliable detection of depressive symptoms from tweets obtained, unobtrusively. Particularly, we examine and exploit multimodal big (social) data to discern depressive behaviors using a wide variety of features including individuallevel demographics. By developing a multimodal framework and employing statistical techniques to fuse heterogeneous sets of features obtained through the processing of visual, textual, and user interaction data, we significantly enhance the current state-of-the-art approaches for identifying depressed individuals on Twitter (improving the average F1-Score by 5 percent) as well as facilitate demographic inferences from social media. Besides providing insights into the relationship between demographics and mental health, our research assists in the design of a new breed of demographic-aware health interventions.

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Section: Social Media and Image Analysismentioning

confidence: 99%

Multimodal mental health analysis in social media

et al. 2020

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“…Work of Ferwerda, Schedl, and Tkalcic [11,13] on Instagram pictures, showed that the way filters are applied to create a certain distinctiveness can be used to predict personality traits of the poster. Others (e.g., [2,26]) have focused on the content of pictures. They showed that compositions of Facebook profile pictures consist of indicators of users' personality.…”

Section: Related Workmentioning

confidence: 99%

Predicting Users' Personality from Instagram Pictures

Ferwerda

Tkalčič

2018

Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization

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Instagram is a popular social networking application that allows users to express themselves through the uploaded content and the different filters they can apply. In this study we look at personality prediction from Instagram picture features. We explore two different features that can be extracted from pictures: 1) visual features (e.g., hue, valence, saturation), and 2) content features (i.e., the content of the pictures). To collect data, we conducted an online survey where we asked participants to fill in a personality questionnaire and grant us access to their Instagram account through the Instagram API. We gathered 54,962 pictures of 193 Instagram users. With our results we show that visual and content features can be used to predict personality from and perform in general equally well. Combining the two however does not result in an increased predictive power. Seemingly, they are not adding more value than they already consist of independently.

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“…Previous studies have discussed several major advantages of computers over humans in dealing with such classification problems. For example, segalin et al think that computers have the ability to store a large amount of information, which is difficult for humans to access [45]. Wei et al believe that computers can utilize a tremendous amount of information to optimize the judgmental accuracy, while humans may be affected by various biases sometimes [54].…”

Section: Learning Models and Performancementioning

confidence: 99%

Will Online Digital Footprints Reveal Your Relationship Status?

Wang

et al. 2020

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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With the increasing social acceptance and openness, more and more sexual-minority men (SMM) have succeeded in creating and sustaining steady relationships in recent years. Maintaining steady relationships is beneficial to the wellbeing of SMM both mentally and physically. However, the relationship maintaining for them is also challenging due to the much less supports compared to the heterosexual couples, so that it is important to identify those SMM in steady relationship and provide corresponding personalized assistance. Furthermore, knowing SMM's relationship and the correlations with other visible features is also beneficial for optimizing the social applications' functionalities in terms of privacy preserving and friends recommendation. With the prevalence of SMM-oriented social apps (called SMMSA for short), this paper investigates the relationship status of SMM from a new perspective, that is, by introducing the SMM's online digital footprints left on SMMSA (e.g., presented profile, social interactions, expressions, sentiment, and mobility trajectories). Specifically, using a filtered dataset containing 2,359 active SMMSA users with their self-reported relationship status and publicly available app usage data, we explore the correlations between SMM's relationship status and their online digital footprints on SMMSA and present a set of interesting findings. Moreover, we demonstrate that by utilizing such correlations, it has the potential to construct machine-learning-based models for relationship status inference. Finally, we elaborate on the implications of our findings from the perspective of better understanding the SMM community and improving their social welfare. CCS Concepts: • Information systems → Data mining; • Human-centered computing → User models; • Social and professional topics → Relationship Status.

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What your Facebook Profile Picture Reveals about your Personality

Cited by 82 publications

References 49 publications

Multimodal mental health analysis in social media

Multimodal mental health analysis in social media

Predicting Users' Personality from Instagram Pictures

Will Online Digital Footprints Reveal Your Relationship Status?

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