# Validating daily social media macroscopes of emotions

arXiv: https://arxiv.org/abs/2108.07646 · HTML: https://mlsanity.com/paper/2108.07646/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Aug 2021.

## Built on by (most cited first, 3 of 3)

- [Extracting Affect Aggregates from Longitudinal Social Media Data with Temporal Adapters for Large Language Models](https://mlsanity.com/paper/2409.17990/) (2024)
- [LEIA: Linguistic Embeddings for the Identification of Affect](https://mlsanity.com/paper/2304.10973/) (2023)
- [Is the Platform Part of the Measurement? A Protocol and Simulation Study for Cross-Platform Equivalence of Machine-Labelled Policy Sentiment](https://mlsanity.com/paper/2610.16757/) (2026)

## Abstract (arXiv)

To study emotions at the macroscopic level, affective scientists have made extensive use of sentiment analysis on social media text. However, this approach can suffer from a series of methodological issues with respect to sampling biases and measurement error. To date, it has not been validated if social media sentiment can measure the day to day temporal dynamics of emotions aggregated at the macro level of a whole online community. We ran a large-scale survey at an online newspaper to gather daily self-reports of affective states from its users and compare these with aggregated results of sentiment analysis of user discussions on the same online platform. Additionally, we preregistered a replication of our study using Twitter text as a macroscope of emotions for the same community. For both platforms, we find strong correlations between text analysis results and levels of self-reported emotions, as well as between inter-day changes of both measurements. We further show that a combination of supervised and unsupervised text analysis methods is the most accurate approach to measure emotion aggregates. We illustrate the application of such social media macroscopes when studying the association between the number of new COVID-19 cases and emotions, showing that the strength of associations is comparable when using survey data as when using social media data. Our findings indicate that macro level dynamics of affective states of users of an online platform can be tracked with social media text, complementing surveys when self-reported data is not available or difficult to gather.