Feeds Don't Tell the Whole Story: Measuring Online-Offline Emotion Alignment
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866910083932225536 |
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| author | Elahimanesh, Sina Mohammadkhani, Mohammadali Kasaei, Shohreh |
| author_facet | Elahimanesh, Sina Mohammadkhani, Mohammadali Kasaei, Shohreh |
| contents | In contemporary society, social media is deeply integrated into daily life, yet emotional expression often differs between real and online contexts. We studied the Persian community on X to explore this gap, designing a human-centered pipeline to measure alignment between real-world and social media emotions. Recent tweets and images of participants were collected and analyzed using Transformers-based text and image sentiment modules. Friends of participants provided insights into their real-world emotions, which were compared with online expressions using a distance criterion. The study involved N=105 participants, 393 friends, over 8,300 tweets, and 2,000 media images. Results showed only 28% similarity between images and real-world emotions, while tweets aligned about 76% with participants' real-life feelings. Statistical analyses confirmed significant disparities in sentiment proportions across images, tweets, and friends' perceptions, highlighting differences in emotional expression between online and offline environments and demonstrating practical utility of the proposed pipeline for understanding digital self-presentation. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2603_27782 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Feeds Don't Tell the Whole Story: Measuring Online-Offline Emotion Alignment Elahimanesh, Sina Mohammadkhani, Mohammadali Kasaei, Shohreh Human-Computer Interaction In contemporary society, social media is deeply integrated into daily life, yet emotional expression often differs between real and online contexts. We studied the Persian community on X to explore this gap, designing a human-centered pipeline to measure alignment between real-world and social media emotions. Recent tweets and images of participants were collected and analyzed using Transformers-based text and image sentiment modules. Friends of participants provided insights into their real-world emotions, which were compared with online expressions using a distance criterion. The study involved N=105 participants, 393 friends, over 8,300 tweets, and 2,000 media images. Results showed only 28% similarity between images and real-world emotions, while tweets aligned about 76% with participants' real-life feelings. Statistical analyses confirmed significant disparities in sentiment proportions across images, tweets, and friends' perceptions, highlighting differences in emotional expression between online and offline environments and demonstrating practical utility of the proposed pipeline for understanding digital self-presentation. |
| title | Feeds Don't Tell the Whole Story: Measuring Online-Offline Emotion Alignment |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2603.27782 |