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Emotional labeling is a cognitive process that involves identifying and naming one's emotions, such as anger, fear, happiness, or sadness. It allows individuals to recognize and express their internal emotional states, a critical aspect of emotional regulation and communication. Labeling emotions requires more than mere recognition; it also involves drawing upon memory and contextual cues to understand the current situation and apply a corresponding emotional label. For instance, feeling...
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Exploring the Use of Isolated Expressions and Film Clips to Evaluate Emotion Recognition by People with Traumatic Brain Injury
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Social media emotions annotation guide (SMEmo): Development and initial validity.

Susannah B F Paletz1, Ewa M Golonka2, Nick B Pandža2,3

  • 1College of Information Studies, University of Maryland, College Park, MD, USA. paletz@umd.edu.

Behavior Research Methods
|September 11, 2023
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Summary
This summary is machine-generated.

This study introduces a new method for measuring emotions in social media, enhancing our understanding of online behavior and information sharing. The validated scheme accurately quantifies emotional expression across different languages and platforms.

Keywords:
Annotation schemeEmotionMethodsSocial mediaVirality

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Area of Science:

  • Computational Social Science
  • Psychology
  • Natural Language Processing

Background:

  • Accurate emotion measurement is crucial for understanding social media's impact on information sharing.
  • Existing emotion quantification schemes lack dimensions, multimedia, and contextual depth.
  • Recent emotion theory necessitates an updated approach to social media emotion analysis.

Purpose of the Study:

  • To develop and validate a standardized annotation scheme for quantifying emotions in social media.
  • To assess the scheme's reliability and validity across different languages and media types.
  • To improve the dimensional, multimedia, and contextual scope of emotion measurement in digital communication.

Main Methods:

  • Human annotators rated social media posts and their own reactions on emotion scales (0-100).
  • Two studies were conducted: Study 1 (English, Twitter/YouTube) and Study 2 (Polish/Lithuanian, Facebook).
  • Convergent and discriminant validity were tested using established text-based emotion measures (LIWC, VADER, EmoLex, NRC-EIL).

Main Results:

  • Strong inter-coder reliability (intraclass correlations ≥ .80) was achieved for multiple emotions in both studies.
  • The annotation scheme demonstrated convergent and discriminant validity, even with lower correlations in non-English samples.
  • The scheme successfully quantified emotions in multimedia social media posts across English, Polish, and Lithuanian.

Conclusions:

  • The developed annotation scheme offers a robust and comprehensive method for measuring social media emotions.
  • This research advances the field by incorporating greater emotional dimensionality, multimedia content, and contextual understanding.
  • The findings support the scheme's utility for diverse research applications in computational social science and digital communication.