ArticleAffective science2021
Left- and Right-Leaning News Organizations Use Negative Emotional Content and Elicit User Engagement Similarly.
Article in Affective science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed, 50 citations in OpenAlex.
- Measuring complex constructs in large-scale text with computational social mixed methods.Behavior research methods · 2026Article
- Attention on social media depends more on how you express yourself than on who you are.Nature human behaviour · 2026Article
- Positive sentiment and expertise predict the diffusion of archaeological content on social media.Scientific reports · 2025Article
- Networks of Negativity: Gaining Attention Through Cyberbullying.International journal of environmental research and public health · 2024Article
- News source bias and sentiment on social media.PloS one · 2024Article
- Negative expressions are shared more on Twitter for public figures than for ordinary users.PNAS nexus · 2023Article
- Examining Twitter-Derived Negative Racial Sentiment as Indicators of Cultural Racism: Observational Associations With Preterm Birth and Low Birth Weight Among a Multiracial Sample of Mothers, 2011-2021.Journal of medical Internet research · 2023Article
- Examining Exposure to Messaging, Content, and Hate Speech from Partisan News Social Media Posts on Racial and Ethnic Health Disparities.International journal of environmental research and public health · 2023Article
- Attention-grabbing news coverage: Violent images of the Black Lives Matter movement and how they attract user attention on Reddit.PloS one · 2023Article
- From Storytelling to Facebook : Content Biases When Retelling or Sharing a Story.Human nature (Hawthorne, N.Y.) · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Negativity has historically dominated news content; however, little research has examined how news organizations use affect on social media, where content is generally positive. In the current project we ask a few questions: Do news organizations on Twitter use negative or positive language and which type of affect garners more engagement on social media? Does the political orientation of news organizations impact the affect expressed and engagement tweets receive on social media? The goal of this project is to examine these questions by investigating tweets of 24 left- and 20 right-leaning news organizations (140,358 tweets). Results indicated that negative affect was expressed more than positive affect. Additionally, negativity predicted engagement with news organizations' tweets, but positivity did not. Finally, there were no differences in affect between left- and right-leaning political orientations. Overall, it appears that for news organizations, negativity is more frequent and more impactful than positivity. Supplementary Information: The online version contains supplementary material available at 10.1007/s42761-021-00046-w.
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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.