ArticleRoyal Society open science2021
Twitter, time and emotions.
Article in Royal Society open science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Large-scale patterns of self-reported mood and sleep across the day and week on an online cognitive-training platform.PLOS digital health · 2026Article
- Examining contraception-related discourse on social media after the Dobbs v. Jackson Women's Health Organization Supreme Court decision: a textual analysis of user-generated content on X (formerly Twitter).Reproductive health · 2026Article
- Forging Online Community Among People in Recovery From Substance Use: Natural Language Processing and Deep-Learning Analysis of The Phoenix App User-Generated Data.JMIR mHealth and uHealth · 2025Article
- A Social Media Study of Portrayals of Bipolar Disorders on YouTube: Content and Thematic Analyses.Journal of medical Internet research · 2025Article
- Early morning hour and evening usage habits increase misinformation-spread.Scientific reports · 2024Article
- Article
- Text as signal. A tutorial with case studies focusing on social media (Twitter).Behavior research methods · 2023Article
- Associations between emotions expressed in internet news and subsequent emotional content on twitter.Heliyon · 2022Article
- Article
- Spread of COVID-19 Vaccine Misinformation in the Ninth Inning: Retrospective Observational Infodemic Study.JMIR infodemiologyArticle
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The study of temporal trajectories of emotions shared in tweets has shown that both positive and negative emotions follow nonlinear circadian (24 h) and circaseptan (7-day) patterns. But to this point, such findings could be instrument-dependent as they rely exclusively on coding using the Linguistic Inquiry Word Count. Further, research has shown that self-referential content has higher relevance and meaning for individuals, compared with other types of content. Investigating the specificity of self-referential material in temporal patterns of emotional expression in tweets is of interest, but current research is based upon generic textual productions. The temporal variations of emotions shared in tweets through emojis have not been compared to textual analyses to date. This study hence focuses on several comparisons: (i) between Self-referencing tweets versus Other topic tweets, (ii) between coding of textual productions versus coding of emojis, and finally (iii) between coding of textual productions using different sentiment analysis tools (the Linguistic Inquiry and Word Count-LIWC; the Valence Aware Dictionary and sEntiment Reasoner-VADER and the Hu Liu sentiment lexicon-Hu Liu). In a collection of more than 7 million Self-referencing and close to 18 million Other topic content-coded tweets, we identified that (i) similarities and differences in terms of shape and amplitude can be observed in temporal trajectories of expressed emotions between Self-referring and Other topic tweets, (ii) that all tools feature significant circadian and circaseptan patterns in both datasets but not always, and there is often a correspondence in the shape of circadian and circaseptan patterns, and finally (iii) that circadian and circaseptan patterns obtained from the coding of emotional expression in emojis sometimes depart from those of the textual analysis, indicating some complementarity in the use of both modes of expression. We discuss the implications of our findings from the perspective of the literature on emotions and well-being.
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