Guest Editorial: Apparent Personality Analysis

Escalera, Sérgio; Baró, Xavier; Guyon, Isabelle

doi:10.1109/taffc.2018.2864230

Cited by 8 publications

(5 citation statements)

References 17 publications

(36 reference statements)

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“…Both have paid special attention to deep learning-based approaches. Jacques et al (2018) and Escalera et al (2018) have limited the scope of the review to apparent personality prediction.…”

Section: Related Reviewsmentioning

confidence: 99%

Inferred vs traditional personality assessment: are we predicting the same thing?

Novikov,

Mararitsa,

Nozdrachev

2021

Preprint

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Machine learning methods are widely used by researchers to predict psychological characteristics from digital records. To answer whether automatic personality estimates retain the properties of the original traits, we reviewed 220 recent articles.First, we put together the predictive quality estimates from a subset of the studies which declare separation of training, validation, and testing phases, which is critical for ensuring the correctness of quality estimates in machine learning (Hastie et al. (2009); Zheng (2015); Kumar (2019)). Only 20% of the reviewed papers met this criterion. To compare the reported quality estimates, we converted them to approximate Pearson correlations. The credible upper limits for correlations between predicted and self-reported personality traits vary in a range between 0.42 and 0.48, depending on the specific trait. The achieved values are substantially below the correlations between traits measured with distinct self-report questionnaires. This suggests that we cannot readily interpret personality predictions as estimates of the original traits or expect predicted personality traits to reproduce known relationships with life outcomes regularly.Next, we complement quality estimates evaluation with evidence on psychometric properties of predicted traits. The few existing results suggest that predicted traits are less stable with time and have lower effective dimensionality than self-reported personality. The predictive text-based models perform substantially worse outside their training domains but stay above a random baseline. The evidence on the relationships between predicted traits and external variables is mixed. Predictive features are difficult to use for validation, due to the lack of prior hypotheses. Thus, predicted personality traits fail to retain important properties of the original characteristics. This calls for the cautious use and targeted validation of the predictive models.

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“…Both have paid special attention to deep learning-based approaches. Jacques et al (2018) and Escalera et al (2018) have limited the scope of the review to apparent personality prediction.…”

Section: Related Reviewsmentioning

confidence: 99%

Inferred vs traditional personality assessment: are we predicting the same thing?

Novikov,

Mararitsa,

Nozdrachev

2021

Preprint

View full text Add to dashboard Cite

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“…They annotated perceived personality and trait dimensions of politicians (i.e., trustworthy, attractive, competence, and so on) to train models to predict election outcomes and party affiliation of politicians. (See also You et al (2015) for another computer vision based model for election prediction) Several methods have been proposed to automatically infer perceived (apparent) personality from visual cues (Ventura, Masip, and Lapedriza 2017;Escalera et al 2018;Escalante et al 2018), and a recent study demonstrated that a similar automated method can reliably detect nonverbal behaviors of candidates during debates (Joo, Bucy, and Seidel 2019). also uses social media data to characterize conservative and liberal voters, using Twitter followers of Trump and Clinton in 2016 U.S. Presidential Election .…”

Section: Motivation: Political Behaviors and Visual Mediamentioning

confidence: 99%

Understanding the Political Ideology of Legislators from Social Media Images

Liou

et al. 2020

ICWSM

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In this paper, we seek to understand how politicians use images to express ideological rhetoric through Facebook images posted by members of the U.S. House and Senate. In the era of social media, politics has become saturated with imagery, a potent and emotionally salient form of political rhetoric which has been used by politicians and political organizations to influence public sentiment and voting behavior for well over a century. To date, however, little is known about how images are used as political rhetoric. Using deep learning techniques to automatically predict Republican or Democratic party affiliation solely from the Facebook photographs of the members of the 114th U.S. Congress, we demonstrate that predicted class probabilities from our model function as an accurate proxy of the political ideology of images along a left–right (liberal–conservative) dimension. After controlling for the gender and race of politicians, our method achieves an accuracy of 59.28% from single photographs and 82.35% when aggregating scores from multiple photographs (up to 150) of the same person. To better understand image content distinguishing liberal from conservative images, we also perform in-depth content analyses of the photographs. Our findings suggest that conservatives tend to use more images supporting status quo political institutions and hierarchy maintenance, featuring individuals from dominant social groups, and displaying greater happiness than liberals.

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“…This study is a response to the call for research into personality computing [40], [48], [49]. In traditional personality computing, validating APR using manually labeled features from any possible detectable distal cues was quite complicated [6].…”

Section: Discussionmentioning

confidence: 99%

“…In traditional personality computing, validating APR using manually labeled features from any possible detectable distal cues was quite complicated [6]. Thus, some recent studies have adopted DL-based architectures to predict personality based on third-party datasets, such as Amazon's Mechanical Turk or ChaLearn's First Impressions dataset [40]. However, most of these studies used APP, in which the DL engines mimicked human raters as observers detecting an interviewee's nonverbal cues and made inferences concerning the interviewees' personality traits in the context of zero-acquaintance judgements.…”

Section: Discussionmentioning

confidence: 99%

TensorFlow-Based Automatic Personality Recognition Used in Asynchronous Video Interviews

2019

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With the development of artificial intelligence (AI), the automatic analysis of video interviews to recognize individual personality traits has become an active area of research and has applications in personality computing, human-computer interaction, and psychological assessment. Advances in computer vision and pattern recognition based on deep learning (DL) techniques have led to the establishment of convolutional neural network models that can successfully recognize human nonverbal cues and attribute their personality traits with the use of a camera. In this paper, an end-to-end AI interviewing system was developed using asynchronous video interview (AVI) processing and a TensorFlow AI engine to perform automatic personality recognition (APR) based on the features extracted from the AVIs and the true personality scores from the facial expressions and self-reported questionnaires of 120 real job applicants. The experimental results show that our AI-based interview agent can successfully recognize the ''big five'' traits of an interviewee at an accuracy between 90.9% and 97.4%. Our experiment also indicates that although the machine learning was conducted without large-scale data, the semisupervised DL approach performed surprisingly well with regard to APR despite the lack of labor-intensive manual annotation and labeling. The AI-based interview agent can supplement or replace existing self-reported personality assessment methods that job applicants may distort to achieve socially desirable effects.INDEX TERMS Big five, convolutional neural network (CNN), personality computing, TensorFlow.

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Guest Editorial: Apparent Personality Analysis

Cited by 8 publications

References 17 publications

Inferred vs traditional personality assessment: are we predicting the same thing?

Inferred vs traditional personality assessment: are we predicting the same thing?

Understanding the Political Ideology of Legislators from Social Media Images

TensorFlow-Based Automatic Personality Recognition Used in Asynchronous Video Interviews

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