# Emotion Alignment: Discovering the Gap Between Social Media and Real-World Sentiments in Persian Tweets and Images

arXiv: https://arxiv.org/abs/2504.10662 · HTML: https://mlsanity.com/paper/2504.10662/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 93 references that are in the map.
- First posted on arXiv 14 Apr 2025.

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language-agnostic BERT Sentence Embedding](https://mlsanity.com/paper/2007.01852/) (2020)
- [Sentiment Analysis in the Era of Large Language Models: A Reality Check](https://mlsanity.com/paper/2305.15005/) (2023)
- [COVID-Twitter-BERT: A Natural Language Processing Model to Analyse COVID-19 Content on Twitter](https://mlsanity.com/paper/2005.07503/) (2020)
- [ParsBERT: Transformer-based Model for Persian Language Understanding](https://mlsanity.com/paper/2005.12515/) (2020)
- [Transformer-based Multimodal Information Fusion for Facial Expression Analysis](https://mlsanity.com/paper/2203.12367/) (2022)
- [Recent Trends in Deep Learning Based Personality Detection](https://mlsanity.com/paper/1908.03628/) (2019)
- [Emotion Detection and Analysis on Social Media](https://mlsanity.com/paper/1901.08458/) (2019)
- [Comprehensive Study on Sentiment Analysis: From Rule-based to modern LLM based system](https://mlsanity.com/paper/2409.09989/) (2024)
- [Optimization Techniques for Sentiment Analysis Based on LLM (GPT-3)](https://mlsanity.com/paper/2405.09770/) (2024)
- [Early Discovery of Emerging Entities in Persian Twitter with Semantic Similarity](https://mlsanity.com/paper/2207.02434/) (2022)
- [Deep Learning-based Sentiment Analysis in Persian Language](https://mlsanity.com/paper/2403.11069/) (2024)

## Abstract (arXiv)

In contemporary society, widespread social media usage is evident in people's daily lives. Nevertheless, disparities in emotional expressions between the real world and online platforms can manifest. We comprehensively analyzed Persian community on X to explore this phenomenon. An innovative pipeline was designed to measure the similarity between emotions in the real world compared to social media. Accordingly, recent tweets and images of participants were gathered and analyzed using Transformers-based text and image sentiment analysis modules. Each participant's friends also provided insights into the their real-world emotions. A distance criterion was used to compare real-world feelings with virtual experiences. Our study encompassed N=105 participants, 393 friends who contributed their perspectives, over 8,300 collected tweets, and 2,000 media images. Results indicated a 28.67% similarity between images and real-world emotions, while tweets exhibited a 75.88% alignment with real-world feelings. Additionally, the statistical significance confirmed that the observed disparities in sentiment proportions.