# Detecting value-expressive text posts in Russian social media

arXiv: https://arxiv.org/abs/2312.08968 · HTML: https://mlsanity.com/paper/2312.08968/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 44 references that are in the map.
- First posted on arXiv 14 Dec 2023.

## Builds on (most cited first, 9 of 9)

- [ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks](https://mlsanity.com/paper/2303.15056/) (2023)
- [Social Chemistry 101: Learning to Reason about Social and Moral Norms](https://mlsanity.com/paper/2011.00620/) (2020)
- [The Rise of Social Bots](https://mlsanity.com/paper/1407.5225/) (2014)
- [Deep Bayesian Active Learning for Natural Language Processing: Results of a Large-Scale Empirical Study](https://mlsanity.com/paper/1808.05697/) (2018)
- [The Touch'e23-ValueEval Dataset for Identifying Human Values behind Arguments](https://mlsanity.com/paper/2301.13771/) (2023)
- [Acquiring Background Knowledge to Improve Moral Value Prediction](https://mlsanity.com/paper/1709.05467/) (2017)
- [What does ChatGPT return about human values? Exploring value bias in ChatGPT using a descriptive value theory](https://mlsanity.com/paper/2304.03612/) (2023)
- [Latent Human Traits in the Language of Social Media: An Open-Vocabulary Approach](https://mlsanity.com/paper/1705.08038/) (2017)
- [On Predicting Personal Values of Social Media Users using Community-Specific Language Features and Personal Value Correlation](https://mlsanity.com/paper/2007.08107/) (2020)

## Built on by (most cited first, 1 of 1)

- [Structure of Basic Human Values in Russian Social Media](https://mlsanity.com/paper/2603.18822/) (2026)

## Abstract (arXiv)

Basic values are concepts or beliefs which pertain to desirable end-states and transcend specific situations. Studying personal values in social media can illuminate how and why societal values evolve especially when the stimuli-based methods, such as surveys, are inefficient, for instance, in hard-to-reach populations. On the other hand, user-generated content is driven by the massive use of stereotyped, culturally defined speech constructions rather than authentic expressions of personal values. We aimed to find a model that can accurately detect value-expressive posts in Russian social media VKontakte. A training dataset of 5,035 posts was annotated by three experts, 304 crowd-workers and ChatGPT. Crowd-workers and experts showed only moderate agreement in categorizing posts. ChatGPT was more consistent but struggled with spam detection. We applied an ensemble of human- and AI-assisted annotation involving active learning approach, subsequently trained several classification models using embeddings from various pre-trained transformer-based language models. The best performance was achieved with embeddings from a fine-tuned rubert-tiny2 model, yielding high value detection quality (F1 = 0.77, F1-macro = 0.83). This model provides a crucial step to a study of values within and between Russian social media users.