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The Economy and Public Diplomacy: An Analysis of RT's Economic Content and Context on Facebook

Ayse D. Lokmanoglu, Carol K. Winkler, Kareem El Damanhoury, Virginia Massignan, Esteban Villa-Turek, Keyu Alexander Chen

TL;DR

The study analyzes RT’s non-Russian Facebook content over five years to map economic topics and their sensitivity to macroeconomic context. It combines multilingual BERTopic-based topic modeling with Vector Autoregression to link topic volumes to currency fluctuations (Ruble) and Urals oil prices, accounting for major shocks such as the COVID-19 pandemic and the Russia–Ukraine war. Findings show RT systematically emphasizes economic topics, with content volumes modulated by currency and oil-price changes in language-specific ways, and with distinct patterns for sanctions, currency narratives, and cryptocurrency topics. The work highlights how state-backed public diplomacy leverages economic context to tailor messaging across global audiences and suggests avenues for cross-country and cross-platform comparisons, as well as methodological enhancements.

Abstract

With globalization's rise, economic interdependence's impacts have become a prominent factor affecting personal lives, as well as national and international dynamics. This study examines RT's public diplomacy efforts on its non-Russian Facebook accounts over the past five years to identify the prominence of economic topics across language accounts. Computational analysis, including word embeddings and statistical methods, investigates how offline economic indicators, like currency values and oil prices, correspond to RT's online economic content changes. The results demonstrate that RT uses message reinforcement associated economic topics as an audience targeting strategy and differentiates their use with changing currency and oil values.

The Economy and Public Diplomacy: An Analysis of RT's Economic Content and Context on Facebook

TL;DR

The study analyzes RT’s non-Russian Facebook content over five years to map economic topics and their sensitivity to macroeconomic context. It combines multilingual BERTopic-based topic modeling with Vector Autoregression to link topic volumes to currency fluctuations (Ruble) and Urals oil prices, accounting for major shocks such as the COVID-19 pandemic and the Russia–Ukraine war. Findings show RT systematically emphasizes economic topics, with content volumes modulated by currency and oil-price changes in language-specific ways, and with distinct patterns for sanctions, currency narratives, and cryptocurrency topics. The work highlights how state-backed public diplomacy leverages economic context to tailor messaging across global audiences and suggests avenues for cross-country and cross-platform comparisons, as well as methodological enhancements.

Abstract

With globalization's rise, economic interdependence's impacts have become a prominent factor affecting personal lives, as well as national and international dynamics. This study examines RT's public diplomacy efforts on its non-Russian Facebook accounts over the past five years to identify the prominence of economic topics across language accounts. Computational analysis, including word embeddings and statistical methods, investigates how offline economic indicators, like currency values and oil prices, correspond to RT's online economic content changes. The results demonstrate that RT uses message reinforcement associated economic topics as an audience targeting strategy and differentiates their use with changing currency and oil values.
Paper Structure (17 sections, 52 figures, 6 tables)

This paper contains 17 sections, 52 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Social Media and Public Diplomacy
  3. Russian Public Diplomacy and the Economy
  4. Methodology
  5. Results
  6. RQ1: Economic Content in RT Posts
  7. RQ2: RT Economic Content and the Value of the Russian Ruble
  8. RQ3: RT Economic Content and Ural Oil Prices
  9. Discussion
  10. Future Areas of Study
  11. Supplementary Material
  12. Supplement A: Economic Search Words
  13. Supplement B: BERTopic Results
  14. Supplement C: Vector Autoregressive Analysis (VAR)
  15. ACF & PACF Post-Differencing Variables
  16. ...and 2 more sections

Figures (52)

  • Figure 1: Research Design. The figure represents a workflow diagram for analyzing the rhetoric of economics across various RT (Russia Today) Facebook pages in different languages, using the BERTopic model.
  • Figure 2: Vector Autoregressive Methodology Steps. The figure outlines the steps of a Vector Autoregressive (VAR) Analysis methodology, beginning with testing for stationarity using the Augmented Dickey-Fuller (ADF) and AutoCorrelation Function (ACF) tests, proceeding to differencing non-stationary variables, selecting appropriate lag lengths using Bayesian Information Criterion (BIC) or Schwarz Criterion (SC), performing VAR Analysis, Toda-Yamamoto two-way Granger Causality tests, Impulse Response Function (IRF) and concluding with post-hoc tests for co-integration such as the Johansen test and the Ljung-Box test to arrive at results.
  • Figure 3: RT Pages Post Volume Economic to Non-Economic Ratio.
  • Figure 4: Independent and Dependent Variables over Time. The figure displays two over time graphs from August 2018 to April 2023, with the top graph showing Russian Ruble (RUB) and Urals oil prices over time, and the bottom stacked bar graph depicting the normalized values of topics.
  • Figure 5: IRF RT on Ruble, lags in the graph correspond to months.
  • ...and 47 more figures