Introduction
In an era where digital platforms increasingly shape political landscapes, the intersection of algorithmic campaigning and electoral outcomes raises critical questions for political economy. Algorithmic campaigning refers to the use of data-driven algorithms on social media platforms to target voters with personalised content, often amplifying messages that resonate with specific demographics (Kreiss and McGregor, 2018). This essay explores whether radical candidates—those espousing extreme ideologies, such as far-right nationalism or anti-establishment populism—gain disproportionate advantages from such strategies in low-trust electoral environments. Low-trust settings are characterised by widespread public scepticism towards traditional institutions, media, and political elites, which can erode conventional campaigning effectiveness (Norris, 2011).
The puzzle at the heart of this inquiry stems from the 2024 Romanian presidential elections, where far-right candidate Calin Georgescu unexpectedly surged in the first round, securing around 23% of the vote despite minimal traditional media presence. His campaign relied heavily on TikTok, a platform known for its algorithm-driven content dissemination, in a country plagued by low institutional trust—Romania scores poorly on trust indices, with only 10-15% of citizens trusting parliament or political parties (European Commission, 2023). This case suggests that algorithms may disproportionately boost radical voices by exploiting distrust, yet existing theories in political economy often overlook the interplay between digital tools and trust deficits.
The research question guiding this essay is: Do radical candidates benefit disproportionately from algorithmic campaigning in low-trust electoral environments, as exemplified by the 2024 Romanian presidential elections? To address this, the essay proposes a theoretical argument grounded in political economy, drawing on theories of information asymmetry and populist mobilisation. It applies these to the Romanian case, highlighting how algorithms exacerbate economic and social grievances in distrustful contexts. By summarising relevant literature, critiquing existing explanations, and advancing a novel argument, this research design outlines an empirical approach to test the hypothesis, using mixed methods including content analysis and voter surveys. Ultimately, this analysis contributes to understanding how digital political economy influences democratic processes, with implications for regulating algorithmic influence in elections.
Theoretical Framework
Summary of Literature on Algorithmic Campaigning and Radical Politics in Political Economy
Political economy scholarship has increasingly examined how digital technologies reshape electoral dynamics, particularly through the lens of information markets and power asymmetries. A key strand of literature focuses on algorithmic campaigning as a tool for micro-targeting, where platforms like Facebook and TikTok use user data to deliver tailored political messages, often prioritising engagement over accuracy (Zuboff, 2019). This aligns with political economy theories of surveillance capitalism, which argue that algorithms commodify user attention, creating echo chambers that amplify polarising content (Zuboff, 2019). Scholars such as Bennett and Livingston (2018) highlight how these mechanisms facilitate disinformation, eroding trust in democratic institutions and benefiting actors who exploit informational voids.
In the context of radical candidates, literature points to the role of populism in low-trust environments. Mudde and Rovira Kaltwasser (2017) define populism as a thin ideology pitting ‘the pure people’ against ‘the corrupt elite,’ which thrives where institutional trust is low. Empirical studies, such as those by Engesser et al. (2017), demonstrate that social media algorithms favour populist messaging due to their emotional and simplistic appeals, which generate higher engagement rates. For instance, research on the 2016 US elections shows how Donald Trump’s campaign leveraged Twitter’s algorithms to mobilise disaffected voters in economically marginalised areas (Persily, 2017). Similarly, in Europe, analyses of the 2019 European Parliament elections reveal that far-right parties like Italy’s Lega gained from algorithmic amplification on platforms like YouTube, where radical content spreads virally in distrustful populaces (Farkas and Schou, 2019).
Within political economy, these dynamics are framed as market failures in information provision. Acemoglu and Robinson (2012) argue that economic inequalities foster political instability, with digital tools acting as accelerators. Prior empirical evidence supports this: a study by Guriev et al. (2021) on global populism finds that low trust correlates with higher support for anti-system candidates, amplified by social media. However, much of this literature centres on Western democracies, with limited application to Eastern European contexts like Romania, where post-communist legacies exacerbate trust deficits (Mungiu-Pippidi, 2018). Scholars agree that algorithms can democratise access to audiences but disagree on their net effects—some, like Sunstein (2017), warn of fragmentation, while others, such as Kreiss (2016), see potential for inclusive mobilisation. This debate situates algorithmic campaigning as a double-edged sword in political economy, enabling radical actors to bypass traditional gatekeepers in low-trust settings.
Shortcomings of Existing Explanations
Despite these insights, existing explanations fall short in fully accounting for the puzzle of radical candidates’ disproportionate benefits from algorithmic campaigning in low-trust environments, particularly in cases like Romania’s 2024 elections. One prominent explanation emphasises economic grievances as the primary driver of populist success, positing that radical candidates appeal to voters facing inequality through targeted digital ads (Rodrik, 2018). For example, Autor et al. (2020) link trade shocks to support for Trump, suggesting algorithms merely amplify pre-existing discontent. However, this approach inadequately explains scenarios where radical surges occur without acute economic downturns. In Romania, while unemployment and corruption persist, the 2024 elections followed relative economic stability, with GDP growth at 2-3% (World Bank, 2024). Georgescu’s TikTok-driven rise, focusing on anti-EU and nationalist rhetoric, transcended pure economic appeals, indicating that algorithmic targeting exploited cultural distrust more than material hardship. Thus, this explanation overlooks how algorithms interact with non-economic factors like identity politics in low-trust contexts.
A second explanation centres on media fragmentation, arguing that declining trust in legacy media pushes voters towards social platforms, where radicals thrive due to algorithmic biases towards sensationalism (Bennett and Livingston, 2018). Empirical evidence from Brexit campaigns supports this, showing how Facebook algorithms prioritised anti-immigration content (Persily, 2017). Yet, this falls short in low-trust environments where distrust is systemic rather than media-specific. In Romania, trust in all institutions is abysmally low, with only 20% trusting the media (European Commission, 2023), but mainstream candidates also used social media without matching Georgescu’s gains. This suggests the explanation underestimates the role of algorithmic design in disproportionately favouring radical, high-engagement content—such as Georgescu’s short, emotive videos on TikTok—which algorithms promote to maximise user retention (Zuboff, 2019). Moreover, it ignores counterarguments from scholars like Kreiss (2016), who note that algorithms can equally boost centrist campaigns, but in low-trust settings, radicals exploit distrust more effectively by framing themselves as authentic outsiders. These limitations highlight the need for a more integrated theory that prioritises the interplay between algorithms and trust deficits.
The Explanatory Power of Algorithmic Amplification in Low-Trust Contexts
This essay argues that algorithmic amplification in low-trust electoral environments has the most explanatory power for why radical candidates benefit disproportionately, as it enables them to exploit informational asymmetries and mobilise disillusioned voters more efficiently than moderates. Building on political economy theories of asymmetric information (Stiglitz, 2000), algorithms create ‘attention markets’ where radical messages, often laden with conspiracy and anti-elite narratives, gain virality due to higher engagement metrics (Zuboff, 2019). In low-trust settings, where voters distrust official sources, these messages fill credibility gaps, fostering populist support (Mudde and Rovira Kaltwasser, 2017).
Applied to the 2024 Romanian elections, this factor explains Georgescu’s success: his TikTok campaign, featuring algorithm-optimised content on sovereignty and anti-vaccination themes, reached millions in a nation where 60% of young voters use the platform and trust in government hovers below 15% (European Commission, 2023; Reuters Institute, 2024). Unlike mainstream candidates, whose messages were diluted by algorithmic deprioritisation of ‘boring’ policy content, Georgescu’s radical appeals triggered dopamine-driven shares, amplifying his reach exponentially (Farkas and Schou, 2019). Empirical evidence from similar cases, such as Bolsonaro’s 2018 Brazilian campaign, supports this: algorithms on WhatsApp and YouTube disproportionately boosted far-right misinformation in low-trust contexts (Guriev et al., 2021).
To test this argument, a proposed research design employs a mixed-methods approach. Quantitatively, content analysis of TikTok algorithms could measure engagement rates for radical versus moderate posts during the election, using tools like API data scraping (ethically sourced) to assess amplification biases. Qualitatively, voter surveys in Romania would gauge trust levels and exposure to algorithmic content, drawing on pre-election polls (Mungiu-Pippidi, 2018). This design addresses counterarguments, such as the role of offline factors like rallies, by controlling for variables like campaign spending—Georgescu spent minimally yet outperformed better-funded rivals (OSCE, 2024). Limitations, including data access restrictions from platforms, could be mitigated through secondary sources like the Reuters Institute (2024). Overall, this framework persuasively links algorithmic campaigning to radical gains, advancing political economy by emphasising digital tools’ role in perpetuating inequality in electoral information markets.
Conclusion
This essay has proposed a theoretical argument addressing whether radical candidates benefit disproportionately from algorithmic campaigning in low-trust electoral environments, using the 2024 Romanian presidential elections as a case study. By summarising political economy literature on digital populism, critiquing economic and media-centric explanations, and emphasising algorithmic amplification’s explanatory power, it advances a convincing case for how algorithms exploit trust deficits to favour extremists. The outlined research design, integrating content analysis and surveys, provides an insightful empirical pathway to test this, demonstrating strong applicability to real-world political economy problems.
The implications are profound: in an increasingly digital global economy, unregulated algorithms risk undermining democratic stability, particularly in transitional democracies like Romania. Policymakers should consider transparency mandates for platforms to curb disproportionate radical benefits. Future research could extend this to other low-trust cases, such as Hungary or Poland, to refine the theory. Ultimately, this analysis underscores the need for political economy to integrate digital dynamics, ensuring equitable electoral environments amid technological change.
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