Feeds Don't Tell the Whole Story: Measuring Online-Offline Emotion Alignment
This work provides a practical pipeline for understanding digital self-presentation, addressing the gap between online and offline emotional expression for social media users.
The study measured the alignment between real-world and social media emotions in the Persian community on X, finding that tweets aligned 76% with real-life feelings while images showed only 28% similarity, highlighting significant disparities in emotional expression online versus offline.
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.