ArticlePNAS nexus2023
Negative expressions are shared more on Twitter for public figures than for ordinary users.
Article in PNAS nexus, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- Measuring complex constructs in large-scale text with computational social mixed methods.Behavior research methods · 2026Article
- The cultural ecology of social media.Evolutionary human sciences · 2026Article
- Social feedback amplifies emotional language in online video live chats.Communications psychology · 2025Article
- Emoji or real emotions? The effect of digitalization on the quality of life through social media addiction - a multigroup SEM analysis, moderated by positive and negative affect.BMC psychology · 2025Article
- An Exploration of How Functional Neurological Disorder Is Discussed on X (Twitter): Mixed Methods Study Using Social Network and Content Analysis.Journal of medical Internet research · 2025Article
- Public Health Discussions on Social Media: Evaluating Automated Sentiment Analysis Methods.JMIR formative research · 2025Article
- Nrf1 acts as a highly-conserved determinon for maintaining robust redox homeostasis in the eco-evo-devo process of life histories.Cell stress · 2025Review
- Article
- GPT is an effective tool for multilingual psychological text analysis.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Political uses of the ancient past on social media are predominantly negative and extreme.PloS one · 2024Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Social media users tend to produce content that contains more positive than negative emotional language. However, negative emotional language is more likely to be shared. To understand why, research has thus far focused on psychological processes associated with tweets' content. In the current study, we investigate if the content producer influences the extent to which their negative content is shared. More specifically, we focus on a group of users that are central to the diffusion of content on social media-public figures. We found that an increase in negativity was associated with a stronger increase in sharing for public figures compared to ordinary users. This effect was explained by two user characteristics, the number of followers and thus the strength of ties and the proportion of political tweets. The results shed light on whose negativity is most viral, allowing future research to develop interventions aimed at mitigating overexposure to negative content.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.