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REVIEW 3 major objections 6 minor 115 references

Recent Trends in Deep Learning Based Personality Detection

T0 review · 3 major / 6 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read Deep multimodal models now lead personality detection, survey finds

desk verdict Useful survey, but Table 4 can't support the SOTA claim. read the letter →

arxiv 1908.03628 v2 pith:YKIWKCZW submitted 2019-08-07 cs.LG cs.AIcs.HC

Yash Mehta , Navonil Majumder , Alexander Gelbukh , Erik Cambria This is my paper · ORCID
classification cs.LGcs.AIcs.HC
keywords personalitydetectiondeeplearningmultimodalfusionBigFivetraitsaffectivecomputingsurveytextmodalityvisual
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This survey tries to give the first up-to-date, bird's-eye view of machine-learning-based personality detection, with deep learning as the main focus. It organizes the field by input modality — text, audio, visual, and their combinations — and collects the datasets, feature-extraction tools, and published accuracies that a newcomer would need. The paper argues that the current state of the art comes from deep learning models that fuse features from more than one modality, most often audio and vision. If that map is accurate, it gives researchers a reliable starting point for choosing methods and benchmarks, and it points to trimodal fusion and better labelled data as the next open problems.

What carries the argument

The central object is the modality-based taxonomy of personality-detection systems: text, audio, visual, bimodal, and trimodal, with Table 4 ('Performance of the state-of-the-art methods on popular personality-detection datasets') as the comparative engine. The taxonomy carries the argument by showing where deep architectures have displaced shallow classifiers, and Table 4 supplies the empirical basis for saying that multimodal deep fusion sets current best results, with late fusion of audio and visual predictions being the most common winning recipe.

What would settle it

Pick one dataset from Table 4, re-run the listed deep multimodal method and a strong unimodal or non-deep baseline under an identical train/test split and metric; if the unimodal or shallow model matches or beats the multimodal deep model, the paper's central state-of-the-art claim would not hold for that benchmark.

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Extended reading notes

Core claim

The paper's central claim is that deep learning combined with multimodal feature fusion has become the dominant route to accurate automatic personality detection. It reports that visual features are the strongest single modality, that combining modalities usually beats any single one, that deep convolutional networks are the standard tool for visual personality inference, and that few systems yet exploit all three modalities together. On this basis it positions itself as the first review covering recent deep-learning-based and multimodal personality-detection work, and it uses a comparison table of 'Mean Best Accuracy' across popular datasets to support the state-of-the-art conclusion.

Load-bearing premise

The survey's conclusions about what is state of the art assume that the 'Mean Best Accuracy' numbers in Table 4 are correctly transcribed and comparable across different datasets and metrics.

Editorial extensions

If this is right

  • A newcomer can use the paper's dataset list and accuracy table to pick a benchmark and a baseline without redoing the literature search.
  • The reported pattern predicts that adding a text stream to audio-visual systems (trimodal fusion) is the most promising near-term direction, since few published systems try it.
  • If the field follows the paper's expectation, personality detection will move from social-media text toward audio, video, and multimodal inputs for applications such as assistants, job screening, and recommendation.
  • The comparison table implies that Big Five datasets dominate the field, so other measures such as MBTI and PEN remain under-resourced for deep learning.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Beyond the paper: if the accuracy table is read at face value, the wide spread of architectures achieving similar scores suggests the binding constraint is labelled data, not model design.
  • Beyond the paper: the finding that visual features are most accurate in unimodal settings suggests perceived-personality benchmarks may reward appearance cues more than genuine behaviour; a fair test would compare systems on 'true personality' labels collected from self-reports.
  • Beyond the paper: the survey's own account implies an untested scalability claim — the end-to-end deep models it praises need large labelled datasets, yet most listed datasets are small; testing whether the same models hold up on a large newly collected corpus would be a direct check.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 6 minor

Summary. This manuscript is a survey of recent deep learning methods for automated personality detection, organized by input modality (text, audio, visual, bimodal, and trimodal). It reviews personality measures and applications, compiles popular datasets and feature-extraction tools, describes representative architectures, and presents a table of reported performance results. The paper's central claims are that it offers the first bird's-eye view of this area and that state-of-the-art personality detection is currently achieved by deep learning techniques combined with multimodal feature fusion.

Significance. If the survey's factual map is reliable, it would serve as a useful entry point for newcomers to the field, since it consolidates datasets, tools, and model families that are otherwise scattered across venue-specific papers. The paper also explicitly discusses fairness and ethics, which is a welcome dimension in this literature. The authors deserve credit for covering a broad range of papers across modalities and for making the survey's scope explicit. However, the central comparative claim about state-of-the-art methods rests on a performance table whose entries are not directly comparable, and the table is internally inconsistent with the text about the ChaLearn challenge winner. These issues are load-bearing because the survey draws its headline conclusion from that table.

major comments (3)
  1. [Section 5 and Table 4] The claim in Section 5 that 'the state of the art in personality detection has been achieved using deep learning techniques along with multimodal fusion of features' is not supported by Table 4. The column labeled 'Mean Best Accuracy' mixes incompatible quantities: classification accuracy percentages (Essays, MBTI, FriendFeed, AMI, ELEA, Color FERET), an R-squared value (YouTube Vlogs, 0.092), and ChaLearn first-impression scores around 0.91 that are 1 minus mean absolute error rather than classification accuracy. Since the rows also differ in dataset, trait set, label source, and train/test partition, the table cannot be used to compare methods across rows. The only two rows touching the same dataset (ChaLearn visual, 90.94 from [39]; ChaLearn multimodal, 91.7 from [38]) still require confirmation that both used the official challenge partition and evaluation metric. I recommend restructuring the table to state the metric explicitly for every row and to make comparisons only within the same dataset and metric.
  2. [Section 4.4 and Table 4] The text states that Deep Bimodal Regression (DBR) [111] 'achieved the highest accuracy in the ChaLearn Challenge 2016 for perceived personality analysis,' but [111] is absent from Table 4. Instead, the table reports ChaLearn results for [39] (visual only, 90.94) and [38] (multimodal, 91.7). If [111] is the challenge winner, its score should appear in the table with the official metric; otherwise the table may attribute the best result to [38] without justification. This inconsistency directly affects the paper's multimodal-fusion state-of-the-art claim, because the table must show whether the best reported ChaLearn number comes from a multimodal system and under which evaluation protocol.
  3. [Section 1.1] The Big-Five traits are described as 'binary (yes/no) values,' but standard Big-Five instruments such as the NEO-FFI and BFI-10 use continuous or multi-point Likert scales, and many of the papers discussed in this survey train regression models rather than binary classifiers. This is not merely a wording issue: it obscures the fact that Table 4 mixes classification and regression results. Please correct the definition and clarify how the different label representations used in the cited works map to classification versus regression.
minor comments (6)
  1. [Section 5] The sentence 'The MBTI personality measure is the most popular personality measure used across the world right now' seems to conflict with Section 1.1, where the Big-Five is described as 'by far' the most popular measure in the automated personality detection literature. Please qualify the claim by domain (e.g., commercial use vs. academic research).
  2. [Section 4.5] The claim that the architecture of [81] 'performs better than the state of the art on IEMOCAP, MOUD and MOSI' refers to sentiment analysis datasets, not personality detection. Since the sentence appears in a personality-detection survey, please state explicitly that these are multimodal sentiment benchmarks and explain why the result is relevant to personality detection, or remove the sentence.
  3. [Table 4] The column header 'Mean Best Accuracy' is misleading for rows reporting R-squared or ChaLearn agreement scores. Please rename the column to something like 'Reported performance (metric)' and indicate the metric used in each row.
  4. [Table 2] The Aurora2 corpus and Columbia deception corpus are listed as audio datasets for personality detection, but their 'Personality Measure' entries are blank and they appear to be used for other tasks (speech recognition and deception detection). Please clarify their role in the surveyed personality-detection literature or remove them from the table.
  5. [Various] There are several typos and formatting issues, including 'Random Forrest' for 'Random Forest' (Section 4.2) and 'the the various image processing techniques' (Section 2).
  6. [References] Some references are incomplete or lack venue details, for example [43] and [107]. Please supply full bibliographic information so readers can locate the cited works.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the survey makes no predictions to derive; its claims are literature summaries, and co-authored references are illustrative rather than load-bearing.

full rationale

This manuscript is a literature survey, not a derivation. Its central claims are (i) that it offers a first bird's-eye view of recent deep-learning personality-detection work and (ii) that the current state of the art is achieved by deep learning with multimodal fusion. Neither claim is derived from fitted parameters or from equations that define the output in terms of the input. The Section 5 state-of-the-art sentence is a reading of Table 4, which transcribes results from cited papers; the few co-authored entries ([64], [83]) are supporting examples rather than the load-bearing evidence, and the multimodal rows that carry the strong SOTA claim ([38], [56]) are not by the present authors. The frequently noted problems with Table 4, such as mixing classification accuracy, R-squared, and the ChaLearn agreement score, and omitting the challenge winner [111], are evidence-quality or correctness concerns, not circularity. There is no fitted-input-called-prediction step, no uniqueness theorem imported from the authors' prior work, and no ansatz smuggled in by self-citation. The 'first survey' novelty assertion is a claim about the literature, not a conclusion entailed by its own definitions. Accordingly, no circular step can be exhibited, and the appropriate score is 0.

Assumptions & free parameters 0 free parameters · 2 assumptions · 0 invented entities

The paper introduces no free parameters or invented entities. The central claim rests on the fidelity of the cited literature and the representativeness of the selected papers, both of which are assumed rather than demonstrated.

assumptions (2)
  • domain assumption The performance numbers reported in Table 4 are accurately transcribed from the cited papers and are comparable across methods.
    The survey uses these numbers to rank methods and draw conclusions about state-of-the-art, but no verification or normalization of metrics is performed.
  • domain assumption The set of papers reviewed is representative of the field of deep learning based personality detection.
    The selection criteria for the surveyed papers are not stated; the paper simply asserts coverage. Section 2 claims to give a 'bird's-eye view' without describing the search or inclusion process.

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Cite this review

Pith. "Pith review of Recent Trends in Deep Learning Based Personality Detection." pith.science (2026). https://pith.science/paper/YKIWKCZW

@misc{pith2026190803628,
  author       = {Pith},
  title        = {Pith review of: Recent Trends in Deep Learning Based Personality Detection},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/YKIWKCZW}},
  note         = {Machine review of arXiv:1908.03628}
}
read the original abstract

Recently, the automatic prediction of personality traits has received a lot of attention. Specifically, personality trait prediction from multimodal data has emerged as a hot topic within the field of affective computing. In this paper, we review significant machine learning models which have been employed for personality detection, with an emphasis on deep learning-based methods. This review paper provides an overview of the most popular approaches to automated personality detection, various computational datasets, its industrial applications, and state-of-the-art machine learning models for personality detection with specific focus on multimodal approaches. Personality detection is a very broad and diverse topic: this survey only focuses on computational approaches and leaves out psychological studies on personality detection.

Figures

Figures reproduced from arXiv: 1908.03628 by the authors.

Figure 2
Figure 2. Fig.2. The neural network specifically focuses on the facial region, mainly areas correspond [PITH_FULL_IMAGE:figures/full_fig_p013_2.png] view at source ↗

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Pith tools

Reviewed August 14, 2026 · model on record in the stance chip above.