Emotion Alignment: Discovering the Gap Between Social Media and Real-World Sentiments in Persian Tweets and Images
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866917991850967040 |
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| author | Elahimanesh, Sina Mohammadkhani, Mohammadali Kasaei, Shohreh |
| author_facet | Elahimanesh, Sina Mohammadkhani, Mohammadali Kasaei, Shohreh |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2504_10662 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Emotion Alignment: Discovering the Gap Between Social Media and Real-World Sentiments in Persian Tweets and Images Elahimanesh, Sina Mohammadkhani, Mohammadali Kasaei, Shohreh Human-Computer Interaction Machine Learning Social and Information Networks 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. |
| title | Emotion Alignment: Discovering the Gap Between Social Media and Real-World Sentiments in Persian Tweets and Images |
| topic | Human-Computer Interaction Machine Learning Social and Information Networks |
| url | https://arxiv.org/abs/2504.10662 |