Feeds Don't Tell the Whole Story: Measuring Online-Offline Emotion Alignment

2026-03-29

AI Summary

This study addresses the discrepancy between emotional expressions on social media and users’ real-world affective states, a gap for which quantitative assessment methods have been lacking. Focusing on Persian-speaking users of the X platform, the work proposes a human-centered, multimodal framework that integrates users’ textual posts, accompanying images, and third-party evaluations of their offline emotions provided by close contacts. Leveraging Transformer-based models for text and image sentiment analysis, multi-source data fusion, and a principled distance metric, the authors introduce a quantifiable approach to evaluate alignment between online self-presentation and offline emotional reality. Applied to a cohort of 105 participants, the method reveals a 76% alignment rate between tweets and real-life emotions, compared to only 28% for images, with statistically significant differences across the three modalities—demonstrating both the efficacy of the proposed framework and the nuanced complexity of digital self-representation.

Abstract

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.

Problem

• emotion alignment
• online-offline discrepancy
• social media emotion
• digital self-presentation
• sentiment analysis

Innovation

• emotion alignment
• online-offline discrepancy
• multimodal sentiment analysis
• digital self-presentation
• human-centered pipeline