ArticleScientific reports2022
Validating daily social media macroscopes of emotions.
Article in Scientific reports, 2022. 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.
- Large-scale patterns of self-reported mood and sleep across the day and week on an online cognitive-training platform.PLOS digital health · 2026Article
- A Decade of News Forum Interactions: Threaded Conversations, Signed Votes, and Topical Tags.Scientific data · 2026Article
- Psychological reactance to vaccine mandates on Twitter: a study of sentiments in the United States.Journal of public health policy · 2025Article
- A systematic evaluation of text mining methods for short texts: Mapping individuals' internal states from online posts.Behavior research methods · 2024Article
- Article
- Emotions on Twitter as crisis imprint in high-trust societies: Do ambient affiliations affect emotional expression during the pandemic?PloS one · 2024Article
- Unveiling the dynamics of emotions in society through an analysis of online social network conversations.Scientific reports · 2023Article
- Young people's mental and social distress in times of international crisis: evidence from helpline calls, 2019-2022.Scientific reports · 2023Article
- Computational social science is growing up: why puberty consists of embracing measurement validation, theory development, and open science practices.EPJ data science · 2023Article
- LEIA: Linguistic Embeddings for the Identification of Affect.EPJ data science · 2023Article
- On the development of an information system for monitoring user opinion and its role for the public.Journal of big data · 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
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Measuring sentiment in social media text has become an important practice in studying emotions at the macroscopic level. However, this approach can suffer from methodological issues like sampling biases and measurement errors. To date, it has not been validated if social media sentiment can actually measure the temporal dynamics of mood and emotions aggregated at the level of communities. We ran a large-scale survey at an online newspaper to gather daily mood self-reports from its users, and compare these with aggregated results of sentiment analysis of user discussions. We find strong correlations between text analysis results and levels of self-reported mood, as well as between inter-day changes of both measurements. We replicate these results using sentiment data from Twitter. We show that a combination of supervised text analysis methods based on novel deep learning architectures and unsupervised dictionary-based methods have high agreement with the time series of aggregated mood measured with self-reports. Our findings indicate that macro level dynamics of mood expressed on an online platform can be tracked with social media text, especially in situations of high mood variability.
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.