# Personality Analysis from Online Short Video Platforms with Multi-domain Adaptation

arXiv: https://arxiv.org/abs/2411.00813 · HTML: https://mlsanity.com/paper/2411.00813/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 41 references that are in the map.
- First posted on arXiv 26 Oct 2024.

## Builds on (most cited first, 11 of 11)

- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://mlsanity.com/paper/2110.13900/) (2021)
- [Is Space-Time Attention All You Need for Video Understanding?](https://mlsanity.com/paper/2102.05095/) (2021)
- [All in One: Multi-task Prompting for Graph Neural Networks](https://mlsanity.com/paper/2307.01504/) (2023)
- [CTC-Segmentation of Large Corpora for German End-to-end Speech Recognition](https://mlsanity.com/paper/2007.09127/) (2020)
- [Self-supervised Hypergraph Representation Learning for Sociological Analysis](https://mlsanity.com/paper/2212.11440/) (2022)
- [Deep Impression: Audiovisual Deep Residual Networks for Multimodal Apparent Personality Trait Recognition](https://mlsanity.com/paper/1609.05119/) (2016)
- [Bi-modal First Impressions Recognition using Temporally Ordered Deep Audio and Stochastic Visual Features](https://mlsanity.com/paper/1610.10048/) (2016)
- [What your Facebook Profile Picture Reveals about your Personality](https://mlsanity.com/paper/1708.01292/) (2017)
- [Personality-Driven Social Multimedia Content Recommendation](https://mlsanity.com/paper/2207.12236/) (2022)
- [When LLM Meets Hypergraph: A Sociological Analysis on Personality via Online Social Networks](https://mlsanity.com/paper/2407.03568/) (2024)

## Built on by (most cited first, 1 of 1)

- [Cross-Protocol Personality Assessment with LLMs: Aligning Behavioral Evidence with Human Ratings](https://mlsanity.com/paper/2610.16050/) (2026)

## Abstract (arXiv)

Personality analysis from online short videos has gained prominence due to its applications in personalized recommendation systems, sentiment analysis, and human-computer interaction. Traditional assessment methods, such as questionnaires based on the Big Five Personality Framework, are limited by self-report biases and are impractical for large-scale or real-time analysis. Leveraging the rich, multi-modal data present in short videos offers a promising alternative for more accurate personality inference. However, integrating these diverse and asynchronous modalities poses significant challenges, particularly in aligning time-varying data and ensuring models generalize well to new domains with limited labeled data. In this paper, we propose a novel multi-modal personality analysis framework that addresses these challenges by synchronizing and integrating features from multiple modalities and enhancing model generalization through domain adaptation. We introduce a timestamp-based modality alignment mechanism that synchronizes data based on spoken word timestamps, ensuring accurate correspondence across modalities and facilitating effective feature integration. To capture temporal dependencies and inter-modal interactions, we employ Bidirectional Long Short-Term Memory networks and self-attention mechanisms, allowing the model to focus on the most informative features for personality prediction. Furthermore, we develop a gradient-based domain adaptation method that transfers knowledge from multiple source domains to improve performance in target domains with scarce labeled data. Extensive experiments on real-world datasets demonstrate that our framework significantly outperforms existing methods in personality prediction tasks, highlighting its effectiveness in capturing complex behavioral cues and robustness in adapting to new domains.