---
title: War in Ukraine Analysis
url: https://www.ml-quant.com/papers/repec/bla-rgscpp-v-15-y-2023-i-1-p-56-74/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: RePEc:bla:rgscpp:v:15:y:2023:i:1:p:56-74
source_url: https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Fdoi.org%2F10.1111%2Frsp3.12632%3Bh%3Drepec%3Abla%3Argscpp%3Av%3A15%3Ay%3A2023%3Ai%3A1%3Ap%3A56-74
featured: 2024-07-17
citations: unknown
topic: LLMs & Text
---


# War in Ukraine Analysis

The research examines the topics and sentiments of Ukrainian Telegram users during the early stages of the war in Ukraine, emphasizing the importance of social media analytics.

- Source: https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Fdoi.org%2F10.1111%2Frsp3.12632%3Bh%3Drepec%3Abla%3Argscpp%3Av%3A15%3Ay%3A2023%3Ai%3A1%3Ap%3A56-74
- Identifier: RePEc:bla:rgscpp:v:15:y:2023:i:1:p:56-74
- Released: 2023-02-14
- First featured: Quant Letter No. 57 (2024-07-17): https://www.ml-quant.com/issues/2024-07-17/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: LLMs & Text

## Related

- [Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models](https://www.ml-quant.com/papers/ssrn/4412788/): ChatGPT predicts stock market returns using sentiment analysis, outperforming traditional methods.
- [Sentiment trading with large language models](https://www.ml-quant.com/papers/doi/10-1016-j-frl-2024-105227/): The OPT model, a large language model, has proven superior in predicting stock market returns using sentiment analysis of U.S. financial news, outdoing traditional methods like the Loughran-McDonald dictionary model.
- [Designing Heterogeneous LLM Agents for Financial Sentiment Analysis](https://www.ml-quant.com/papers/arxiv/2401.05799/): A study suggests using large language models without fine-tuning for financial sentiment analysis, offering a design framework that enhances accuracy.
- [Instruct-FinGPT: Financial Sentiment Analysis by Instruction Tuning of General-Purpose Large Language Models](https://www.ml-quant.com/papers/arxiv/2306.12659/): A new approach improves financial sentiment analysis by addressing limitations of language models.
- [The Model Arena for Cross-lingual Sentiment Analysis: A Comparative Study in the Era of Large Language Models](https://www.ml-quant.com/papers/arxiv/2406.19358/): The study finds Small Multilingual Language Models (SMLM) excel in zero-shot cross-lingual sentiment analysis, while Large Language Models (LLM) perform better in few-shot scenarios.
- [InkubaLM: A small language model for low-resource African languages](https://www.ml-quant.com/papers/arxiv/2408.17024/): African Language Model: InkubaLM, a language model for African languages, is introduced, performing well in tasks like machine translation and sentiment analysis despite limited resources.
