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

Samira Shaikh; Sashank Santhanam; Shaina Glass; Vidhushini Srinivasan

arxiv: 1907.08326 · v1 · pith:WDH4QWUWnew · submitted 2019-07-19 · 💻 cs.SI · cs.CL

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

Sashank Santhanam , Vidhushini Srinivasan , Shaina Glass , Samira Shaikh This is my paper

Pith reviewed 2026-05-24 19:19 UTC · model grok-4.3

classification 💻 cs.SI cs.CL

keywords emojissolidaritycrisis eventssocial mediarecurrent neural networkHurricane IrmaParis attackssociolinguistic behaviors

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The pith

Emojis mark expressions of solidarity on social media during major crisis events.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper trains a recurrent neural network on annotated text corpora to detect expressions of solidarity, then tracks the temporal and geospatial spread of emojis in posts about Hurricane Irma and the 2015 Paris attacks. This method is used to characterize how people respond collectively in online networks as crises develop. A reader would care if emojis turn out to function as observable signals of social bonding rather than random decorations. The central finding is that these symbols reliably reflect sociolinguistic behaviors tied to solidarity.

Core claim

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.

What carries the argument

Recurrent neural network classifier trained on annotated corpora to identify solidarity expressions and applied to emoji diffusion patterns.

If this is right

Solidarity responses can be monitored through emoji patterns over time and space.
Online network behavior during crises can be described using emoji diffusion metrics.
Emojis carry measurable sociolinguistic information beyond their visual role.
The same classification approach could separate solidarity from other emotional signals in text.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Real-time dashboards could flag rising solidarity in affected regions using only public emoji data.
The method might extend to studying coordination or conflict signals in non-crisis events.
If emoji patterns hold across cultures, they could serve as low-cost proxies for public sentiment in data-scarce areas.

Load-bearing premise

The annotated corpora used to train the RNN accurately capture expressions of solidarity and the model generalizes beyond the training data to new crisis-related posts.

What would settle it

A hand-labeled sample of posts from a fresh crisis event in which the model's solidarity predictions show no statistical alignment with human judgments of emoji use.

read the original abstract

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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper trains an RNN on annotated tweets to flag solidarity during two crises then tracks emoji spread, but reports no accuracy or validation numbers.

read the letter

The core contribution is a pipeline that takes annotated corpora from Hurricane Irma and the 2015 Paris attacks, trains a recurrent neural network to label solidarity expressions, and then maps the temporal and geospatial diffusion of emojis in those posts. This is a direct application rather than a new theoretical frame, but it does connect the classification step to observable emoji patterns in real events. The approach is clear and uses actual social media data, which gives it some grounding in observable behavior. The main weakness is that the abstract supplies no performance figures, inter-annotator scores, or cross-event checks. Without those numbers it is hard to judge whether the classifier is reliable enough to support claims that emojis reliably mark solidarity. If the labels are noisy or the model overfits the training events, the diffusion results lose their interpretive force. The work sits in computational social science and crisis communication. Readers already working on emoji analysis or online response to disasters could extract the method and the two case studies, but they would need the missing metrics to assess the strength of the evidence. I would send it to peer review; the idea is reasonable and the events are concrete, so referees can check the implementation details and ask for the validation numbers that are currently absent.

Referee Report

3 major / 2 minor

Summary. The paper studies the use of emojis to express solidarity in social media during two crisis events: Hurricane Irma in 2017 and the 2015 Paris terrorist attacks. It trains a recurrent neural network (RNN) on annotated corpora to classify expressions of solidarity in text, then uses these to analyze the temporal and geospatial diffusion of emojis in online social networks. The central claim is that emojis serve as a powerful indicator of sociolinguistic behaviors like solidarity that emerge during crises.

Significance. If the classifier is reliable and the diffusion patterns are robust, this work contributes a novel method for quantifying solidarity through emoji usage in crisis contexts, bridging computational linguistics and social network analysis. The dual-event design allows for comparative insights, and the focus on real-time social media data is timely for understanding online responses to disasters and attacks.

major comments (3)

[Abstract and §3] Abstract and §3 (Methods): No validation metrics such as accuracy, precision, recall, F1-score, or inter-annotator agreement are reported for the RNN classifier or the annotated corpora. This is load-bearing because the temporal and geospatial diffusion analyses in §4–5 depend entirely on the reliability of the solidarity classifications.
[§4] §4 (Results): The diffusion analysis applies the RNN without reported cross-event validation or hold-out testing; it is unclear whether performance on one crisis event transfers to the other, which directly undermines interpreting the emoji patterns as evidence of generalizable solidarity behaviors.
[§5] §5 (Discussion): The assertion that emojis are a 'powerful indicator' lacks any quantitative baseline comparison (e.g., against random emoji selection or non-solidarity classifiers) or statistical significance tests on the diffusion metrics.

minor comments (2)

[Introduction] The motivation section could clarify why these two events were selected as comparable cases for solidarity analysis.
[Figures] Figure captions describing the pipeline would benefit from explicit data-flow arrows or step labels.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address each major comment below and will make the necessary revisions to strengthen the work.

read point-by-point responses

Referee: [Abstract and §3] Abstract and §3 (Methods): No validation metrics such as accuracy, precision, recall, F1-score, or inter-annotator agreement are reported for the RNN classifier or the annotated corpora. This is load-bearing because the temporal and geospatial diffusion analyses in §4–5 depend entirely on the reliability of the solidarity classifications.

Authors: We agree that validation metrics are essential and were omitted from the original manuscript. This is a clear gap. In the revision we will add accuracy, precision, recall, F1-score for the RNN classifier and inter-annotator agreement statistics for the annotated corpora to §3, directly supporting the reliability of the classifications used in later sections. revision: yes
Referee: [§4] §4 (Results): The diffusion analysis applies the RNN without reported cross-event validation or hold-out testing; it is unclear whether performance on one crisis event transfers to the other, which directly undermines interpreting the emoji patterns as evidence of generalizable solidarity behaviors.

Authors: We acknowledge that cross-event validation and hold-out testing were not reported. The revised manuscript will include these experiments, applying the model trained on one event to the other with appropriate performance metrics to demonstrate transferability and support the generalizability of the observed patterns. revision: yes
Referee: [§5] §5 (Discussion): The assertion that emojis are a 'powerful indicator' lacks any quantitative baseline comparison (e.g., against random emoji selection or non-solidarity classifiers) or statistical significance tests on the diffusion metrics.

Authors: We agree that the claim would be strengthened by explicit baselines and statistical tests. The revision will add quantitative comparisons against random emoji selection and non-solidarity classifiers, together with significance testing on the diffusion metrics, to §5. revision: yes

Circularity Check

0 steps flagged

No circularity; analysis rests on external annotations and trained classifier without self-referential reduction.

full rationale

The paper trains an RNN classifier on annotated corpora for solidarity expressions, then applies the resulting labels to study emoji diffusion in crisis events. No equations, fitted parameters renamed as predictions, or self-citation chains appear in the derivation. The central steps rely on independent human annotations and standard supervised learning rather than reducing to the paper's own inputs by construction. This is the expected non-finding for an empirical sociolinguistic study.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Abstract-only review provides no explicit free parameters, axioms, or invented entities; the central claim rests on the unstated assumption that annotated text reliably represents solidarity and that emoji use correlates with the classified expressions.

axioms (1)

domain assumption Annotated corpora accurately represent expressions of solidarity in crisis-related social media text.
Required for training the RNN classifier described in the abstract.

pith-pipeline@v0.9.0 · 5636 in / 1037 out tokens · 30534 ms · 2026-05-24T19:19:26.720071+00:00 · methodology

discussion (0)

Forward citations

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