Evidence map›Paper›PMID 37697206›Full record

ArticleBehavior research methods2024

Social media emotions annotation guide (SMEmo): Development and initial validity.

Susannah B F Paletz, Ewa M Golonka, Nick B Pandža, Grace Stanton, David Ryan, Nikki Adams, C Anton Rytting, Egle E Murauskaite, Cody Buntain, Michael A Johns and 1 more

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Article in Behavior research methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

11 authors.

Susannah B F PaletzCollege of Information Studies, University of Maryland, College Park, MD, USA. paletz@umd.edu.ORCID http://orcid.org/0000-0002-9513-2799
Ewa M GolonkaApplied Research Laboratory for Intelligence and Security (ARLIS), University of Maryland, College Park, MD, USA.
Nick B PandžaApplied Research Laboratory for Intelligence and Security (ARLIS), University of Maryland, College Park, MD, USA.
Grace StantonDepartment of Criminology, University of Maryland, College Park, MD, USA.
David RyanFeminist, Gender, and Sexuality Studies, Stanford University, Stanford, CA, USA.
Nikki AdamsApplied Research Laboratory for Intelligence and Security (ARLIS), University of Maryland, College Park, MD, USA.
C Anton RyttingApplied Research Laboratory for Intelligence and Security (ARLIS), University of Maryland, College Park, MD, USA.
Egle E MurauskaiteICONS Project, University of Maryland, College Park, MD, USA.
Cody BuntainCollege of Information Studies, University of Maryland, College Park, MD, USA.
Michael A JohnsApplied Research Laboratory for Intelligence and Security (ARLIS), University of Maryland, College Park, MD, USA.
Petra BradleyApplied Research Laboratory for Intelligence and Security (ARLIS), University of Maryland, College Park, MD, USA.

Funding

Minerva Research Initiative / United States Government Office of Naval Research Grant #N00014-19-1-2506United States Government Office of Naval Research (ONR) 12398640
6 · The paper itself

Abstract

The proper measurement of emotion is vital to understanding the relationship between emotional expression in social media and other factors, such as online information sharing. This work develops a standardized annotation scheme for quantifying emotions in social media using recent emotion theory and research. Human annotators assessed both social media posts and their own reactions to the posts' content on scales of 0 to 100 for each of 20 (Study 1) and 23 (Study 2) emotions. For Study 1, we analyzed English-language posts from Twitter (N = 244) and YouTube (N = 50). Associations between emotion ratings and text-based measures (LIWC, VADER, EmoLex, NRC-EIL, Emotionality) demonstrated convergent and discriminant validity. In Study 2, we tested an expanded version of the scheme in-country, in-language, on Polish (N = 3648) and Lithuanian (N = 1934) multimedia Facebook posts. While the correlations were lower than with English, patterns of convergent and discriminant validity with EmoLex and NRC-EIL still held. Coder reliability was strong across samples, with intraclass correlations of .80 or higher for 10 different emotions in Study 1 and 16 different emotions in Study 2. This research improves the measurement of emotions in social media to include more dimensions, multimedia, and context compared to prior schemes.

Indexed as

EmotionsSocial MediaHumansReproducibility of ResultsAnnotation schemeEmotionMethodsSocial mediaVirality

Identifiers

PMID37697206

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