Social media platforms play a central role in shaping political discourse during elections, particularly in geopolitically contested regions. We study how political narratives emerged, aligned, and propagated during the 2024 Moldovan presidential election by analyzing publicly available posts collected from X, Facebook, and Reddit between August and October 2024. Combining multilingual stance detection, coordination analysis, and causal inference, we examine both descriptive and causal mechanisms underlying online political discourse. First, we apply large language models to classify stance toward European integration, Russia, and the two main presidential candidates, revealing clear cross-platform and cross-linguistic differences in how political positions are expressed. Second, we construct retweet-based and semantic alignment networks on X to identify amplification and aligned framing of dominating narratives. Finally, we use a proximal causal inference framework to estimate peer influence effects in stance formation. Our results indicate consistently positive and statistically significant average treatment effects across major stance categories. These findings provide evidence that amplification and peer exposure contribute to the reinforcement of political narratives online. This work offers the first analysis of social media discourse surrounding the 2024 Moldovan presidential election and demonstrates the value of integrating language-based annotation, network analysis, and causal inference for studying political influence in multilingual and geopolitically sensitive contexts.
Figures & tables
Fig. 1: Normalized heatmap of language share within each platform. Only the eight most frequent languages are shown individually; all remaining languages are aggregated into an “Other” category. For Facebook, 80% of posts are in Romanian, while for Reddit and X, more than 75% of posts are in English.
Fig. 2: Distribution of topics accounting for at least 2% of posts on at least one platform. X and Reddit communities display similar attention to geopolitical issues, whereas Facebook users predominantly engage with discussions centered on domestic governance.
Fig. 3: Distribution of macro-topics within each language community across platforms. Across X, Facebook, and Reddit, “Domestic Politics & Governance” accounts for the largest share of posts in all language groups, particularly on Facebook, where it represents 60% - 80% of content. “Geopolitics & EU Integration” is comparatively more prominent among English and Russian posts, especially on Reddit and X. “Society & Civic Life” receives greater attention within Romanian-language discourse across platforms, while “Culture, Religion, & Conflict” consistently represents a small fraction of discussion.
Fig. 4: Aggregate distribution of major political stances expressed in election-related posts. Excluding neutral stance, “anti-Russia”, “anti-Russia & pro-Europe”, and “pro-Europe” stances constitute the majority of observed content ( 5−20% across all platforms), while “pro-Russia” and “anti-Europe” stances appear less frequently ( <5% ).
Fig. 5: Daily volume of stance-labeled posts over time. The vertical line marks the first round of the Moldovan presidential election on October 20, 2024. “pro-Europe”, “anti-Russia”, and “anti-Russia & pro-Europe” stances exhibit the most pronounced post-election surge.
Fig. 6: Rapid retweet subgraph using a 1-hour temporal threshold. Nodes represent users; edges connect users who retweeted another user’s content within the threshold. The node size reflects the number of interacting users.
Fig. 7: Semantic similarity alignment subgraph capturing posts that are both semantically similar and temporally proximate. Edges indicate high cosine similarity. The node size reflects the number of users posting similar tweets. The alignment clusters show a prevalence of “pro-Europe” discourse.
Fig. 8: Individual treatment effect (ITE) of exposure to stance-aligned content on expression of similar stances in subsequent tweets, obtained with ProEmb.
Stance
ATE (ProEmb)
n1/n0
Pro-Europe
0.218±0.034
614/703
Pro-Russia
0.517±0.0002
21/1,296
Anti-Europe
0.251±0.043
160/1,157
Anti-Russia
0.253±0.021
840/477
TABLE I: Average treatment effect (ATE) ProEmb estimates of exposure to stance-aligned content on subsequent stance expression. n1/n0 denotes the treated/control sample sizes.
Query category
Description
Actor-based
Mentions of prominent Moldovan political figures, candidates, and party leaders, including multilingual name variants.
Party-based
References to major political parties and coalitions participating in the election, expressed in multiple languages.
Election keywords
Proximity-based queries capturing elections, voting, referendum-related terms, and electoral procedures across languages.
Interaction-based
Posts engaging with or authored by key political accounts (e.g., replies, retweets, quotes), used to capture amplification and discussion dynamics.
Location-based
Content associated with Moldova or major cities (e.g., Chișinău), combined with political keywords to increase contextual relevance.
Platform-specific
Queries tailored to platform constraints (e.g., site-restricted searches or first-post filters) to improve coverage consistency.
TABLE II: Summary of query categories used for election-related data extraction.
Fig. 9: Sensitivity of the retweet-based synchronized amplification network to the temporal threshold Δt . Panels (a)–(c) show how network density, network size, and retweet coverage change as the threshold increases. Dashed vertical lines mark the representative thresholds of 60 s, 1 hour, and 24 hours.
Account
Description
@SanduMaiaMD
Official account of President Maia Sandu
@vonderleyen
Official account of Ursula von der Leyen, President of the European Commission
@JuliaDavisNews
Journalist and columnist monitoring Russian state media
@wartranslated
Account translating and reposting Russo-Ukrainian war footage
@nexta_tv
Eastern European opposition-aligned news outlet
@IgorSushko
Ukrainian–American political commentator on Eastern Europe
TABLE III: Selected high-degree accounts in the rapid retweet network. Descriptions are based on publicly available account information and observed content.
Fig. 10: Topic prevalence across election-related posts, ordered by frequency and shown on a logarithmic scale. The distribution exhibits a pronounced long tail, with a small number of dominant topics and many low-frequency topics.
Macro-Topic
Mapped Topic Descriptions
Geopolitics & EU Integration
Ukraine, Russia, NATO, and post-Soviet geopolitics
Eastern European countries and regional cooperation
Referendum results and diaspora turnout
EU accession and political stability
Justice reforms and EU cooperation plans
Putin, Trump, and geopolitical propaganda discourse
TABLE IV: Manual aggregation of BERTopic topics into macro-topics based on qualitative inspection of topic content.
Institute of Computer Science, Foundation for Research and Technology, Vassilika Vouton, Heraklion, Crete, Greece · Technical University of Crete, University Campus, Akrotiri, Chania, Greece