# I Stand With You: Using Emojis to Study Solidarity in Crisis Events

arXiv: https://arxiv.org/abs/1907.08326 · HTML: https://mlsanity.com/paper/1907.08326/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jul 2019.

## Built on by (most cited first, 5 of 5)

- [Fine-Grained Detection of Solidarity for Women and Migrants in 155 Years of German Parliamentary Debates](https://mlsanity.com/paper/2210.04359/) (2022)
- [Changes in European Solidarity Before and During COVID-19: Evidence from a Large Crowd- and Expert-Annotated Twitter Dataset](https://mlsanity.com/paper/2108.01042/) (2021)
- [On the Context-Free Ambiguity of Emoji](https://mlsanity.com/paper/2201.06302/) (2022)
- [LLM Analysis of 150+ years of German Parliamentary Debates on Migration Reveals Shift from Post-War Solidarity to Anti-Solidarity in the Last Decade](https://mlsanity.com/paper/2509.07274/) (2025)
- [Digital Skin, Digital Bias: Uncovering Tone-Based Biases in LLMs and Emoji Embeddings](https://mlsanity.com/paper/2604.06863/) (2026)

## Abstract (arXiv)

We study how emojis are used to express solidarity in social media in the context of two major crisis events - a natural disaster, Hurricane Irma in 2017 and terrorist attacks that occurred on November 2015 in Paris. Using annotated corpora, we first train a recurrent neural network model to classify expressions of solidarity in text. Next, we use these expressions of solidarity to characterize human behavior in online social networks, through the temporal and geospatial diffusion of emojis. Our analysis reveals that emojis are a powerful indicator of sociolinguistic behaviors (solidarity) that are exhibited on social media as the crisis events unfold.