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

TL;DR

This study develops a pipeline to measure emotional alignment between online social media expressions and real-world emotions, revealing significant disparities especially in images versus text.

Contribution

Introduces a human-centered pipeline utilizing Transformers to quantify emotional differences between online and offline contexts in social media.

Findings

Only 28% similarity between images and real-world emotions.

Tweets aligned about 76% with real-life feelings.

Significant disparities in sentiment across images, tweets, and perceptions.

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.