The effects of perceived social status and context valence on the inference of message senders’ emotions and facial expressions from emojis

Contemporary smartphone users frequently exchange emoji-laden messages, often interpreting the sender's psychological intent through these symbols. This study examined how perceived social status (PSS) and emotional context valence affect the interpretation of emoji-based digital messages. Across three experiments, participants read messages containing crying or smiling emojis embedded in negative, neutral, or positive contexts. In Experiment 1, participants judged the similarity between the crying emoji and the sender's facial expression. In Experiments 2 and 3, they inferred whether the emoji matched the sender's actual feelings. Results showed that individuals with low-perceived social status were highly reactive to negative emotional contexts, often interpreting emojis as sarcastic or emotionally incongruent. In contrast, high-perceived social status individuals applied more stable interpretations across conditions. These patterns were observed across both crying (Experiment 2) and smiling (Experiment 3) emojis. These findings provide empirical support for the role of social status and emotional context in shaping the interpretation of emojis in ambiguous digital interactions. Conclusions offer valuable insights for theoretical and practical implications in interpersonal digital communication.

Authors

Institutions

Publication Details

Journal
Computers in Human Behavior Reports
Published
2025-08-01
DOI
https://doi.org/10.1016/j.chbr.2025.100773
Primary Topic
Digital Communication and Language
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The effects of perceived social status and context valence on the inference of message senders’ emotions and facial expressions from emojis

Guk-Hee Lee, Do Hung Han
Computers in Human Behavior Reports
Digital Communication and Language
article

The effects of perceived social status and context valence on the inference of message senders’ emotions and facial expressions from emojis

Guk-Hee Lee, Do Hung Han
article en

Abstract

Contemporary smartphone users frequently exchange emoji-laden messages, often interpreting the sender's psychological intent through these symbols. This study examined how perceived social status (PSS) and emotional context valence affect the interpretation of emoji-based digital messages. Across three experiments, participants read messages containing crying or smiling emojis embedded in negative, neutral, or positive contexts. In Experiment 1, participants judged the similarity between the crying emoji and the sender's facial expression. In Experiments 2 and 3, they inferred whether the emoji matched the sender's actual feelings. Results showed that individuals with low-perceived social status were highly reactive to negative emotional contexts, often interpreting emojis as sarcastic or emotionally incongruent. In contrast, high-perceived social status individuals applied more stable interpretations across conditions. These patterns were observed across both crying (Experiment 2) and smiling (Experiment 3) emojis. These findings provide empirical support for the role of social status and emotional context in shaping the interpretation of emojis in ambiguous digital interactions. Conclusions offer valuable insights for theoretical and practical implications in interpersonal digital communication.

Computers in Human Behavior ReportsVol. 19
Seoul National University (KR), Kyonggi University (KR)
Reduced inequalities
Openalex Percentile: Top 79%
Digital Communication and Language
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.