Research

TikTok has rapidly developed from a punchline for jokes about “kids these days” into a formidable force in American politics. The speed of this development is unprecedented, even in the rapidly-changing world of digital politics. Through a combination of hashtag and snowball sampling, we identify 11,546 TikTok accounts who primarily post about politics, allowing us to analyze trends in the posting, viewing and commenting behavior on 1,998,642 tiktoks they have uploaded.

We test a number of theories about how the unique combination of affordances on TikTok shapes how it is used for political communication. Compared to the dominant platform for political videos (YouTube) we find that a higher percentage of TikTok users upload videos, TikTok view counts are more dominated by virality, and viewership of videos are less dependent on a given accounts’ number of followers/subscribers. We discuss how these findings affect the production of content that ultimately determines the experience of TikTok consumers.

In democracies, electoral mandates are meant to shape public policy. But how much leeway do elected representatives actually have to implement it? Influential scholars think that (horizontal and vertical) institutional hurdles, budget constraints and political pressure dilute mandate responsiveness, but empirical evidence for this important claim remains scarce. This article provides a theoretical model and an empirical account of the extent to which different types of constraints limit the capacity of governing parties to set their electoral priorities on the agenda.

Using fixed-effects Poisson regression on German electoral and legislative priorities over a period of over three decades (1983–2016), we conclude that policies reflect electoral priorities to a greater extent than scholarship has acknowledged so far. We do confirm, however, the constraining effects of Europeanization, shrinking budget leeway, intra-coalition disagreement and low executive popularity. We elaborate on the implications for theories of public policy, democratic representation and comparative politics.

Although successful sustainability transitions depend on public support, we still know little about citizens' opinions on climate solutions. Existing research often focuses on the problem perception of climate change rather than analyzing attitudes toward specific climate solutions. Studies also largely use closed questions to assess public opinion, posing a problem of ecological validity. Here, we address these gaps by leveraging data from a large-scale public consultation process, the "Grand Débat National", launched by the French government in response to the Yellow Vest movement in 2019.

Combining structural topic modelling, dictionary-based text analysis and qualitative coding, we map the salience and directionality of public opinion on climate solutions. We find that consultation participants perceive climate change as the most salient environmental problem. Transforming the transport and energy sectors is the most supported solution for addressing climate change. For these two sectors, substitution-based climate solutions - as opposed to sufficiency- or efficiency-based measures - are most salient. For instance, participants stress the need to expand public transport infrastructure and switch to renewable energy technologies for power generation.

Our findings demonstrate a strong public consensus on most substitution-based climate solutions, except for the role of cars and nuclear energy. While most participants do not link climate solutions to specific policy instruments, we find preferences for authority-based instruments in the context of phasing out polluting technologies, and treasury-based instruments for supporting innovation and phasing in low carbon technologies.

Models of political competition, questions of representation and voting behaviour alike require an understanding of what policies political actors prefer, offer and promote. To make positive assessments of these processes, political scientists have developed and refined tools to measure the relative positions of political actors on dimensions of politics.

Where do parties or voters stand on the left-right dimension? What policies define it? Is left-right really the most salient dimension in a political system? To answer these questions, so-called scaling models have been developed to estimate the positions of actors on the basis of their observable behaviours. To this end, many data sources have been explored: from voting behaviour in parliament (Poole and Rosenthal 1985), over party manifestos (Budge 2001) and parliamentary speech (Lauderdale and Herzog 2016), to positions in social media networks (Barbera 2015). This contribution gives an intuition on how this can be achieved, an introduction to the math behind them and provides an overview of their applications, both technical and substantive. We close with criticisms of these methods, which speaks to the development of further validation methods.

Agenda-setting focuses on how political issues emerge within society, enter parliamentary debates and are responded to by government decisions. We introduce a database that traces policy issues in Germany between 1978 and 2017. These political activities include political inputs (public opinion), processes (party manifestos, parliamentary questions and government speeches), as well as outputs (laws). Each activity’s topic is identified using the Comparative Policy Agendas scheme. Collectively, these observations comprise the policy agendas of Germany. We highlight the database potential by describing all German policy issues for the 39-year period and by tracking how immigration became a political issue.

Our understanding of politics often relies on the ideological placement of political actors – ranging from scaling of legislative roll-call voting in the United States to text-based classifications of political parties in Europe. A particularly thorny problem remains estimating individual positions in legislatures with strong partisan discipline. We provide a novel approach for estimating legislators' ideological positions: an expert survey in which respondents compare pairs of representatives on a left-right dimension.

Our approach is innovative for four reasons. First, we rely on political youth leaders who are insightful and easy to recruit. Second, the rating task does not involve numeric scaling and consists of simple pairwise comparisons. Third, we efficiently and automatically detect informative comparisons to reduce the cost and length of the survey without compromising our estimates. Fourth, we use a Bayesian Davidson model with random effects in order to generate an ideological position for each legislator. As an empirical illustration, we estimate the placement of the 709 members of the 19th German Bundestag. Several validity tests show that our model captures variation within and across political parties. Our estimates offer a thorough benchmark to validate alternative measurement strategies. The presented measurement strategy is flexible and easily extendable to diverse political settings because it is able to capture comparisons among political actors across time and space.

Increasingly, political campaigns use digital ads to seek out potential voters, and companies like Meta offer advertisers detailed targeting options such as criteria based on user demographics, behaviors, and interests. An underexplored feature of digital advertising on social media platforms is the use of so-called ad delivery algorithms. These algorithms set prices via ad auctions and deliver ads to "relevant" audiences without the explicit knowledge or intention of advertisers, prompting the question of whether this process may constitute an algorithmic form of microtargeting.

To investigate the delivery and pricing of political ads, we collaborated with three Dutch political parties to place on their Facebook and Instagram accounts a total of 135 identical ads targeting nine different audiences before the country's nationwide 2022 municipal elections. Ads ran with the same settings, at the same time, using the same daily budgets, texts, and images to ensure any differences in pricing and delivery are due to the advertiser and target audiences.

Our pre-registered hypotheses expect that political parties reach more people and pay lower prices when ads are targeted at "relevant" audiences. We find pricing and delivery differences between parties and audiences, but not always as expected. We find evidence that some parties are charged more than others, with one party paying 9.24% to 10.74% less to reach 1000 users. Furthermore, lower-educated citizens, women, and younger people (18-24 year olds) are more expensive for political parties to reach.

These findings have significant implications for political parties and democracy. The fact that parties are charged different prices for the same reach creates unfair competition and an uneven playing field. Our finding that certain groups are less likely to receive political ads means they are potentially more isolated from receiving election-related information on Meta platforms. Finally, our findings suggest that simply banning or severely limiting targeting capabilities, without addressing the potential for algorithmic microtargeting, would mean that ad delivery algorithms still deliver ads to specific groups without oversight or transparency.

Although social media only recently emerged, the accumulation of evidence undermining the ‘echo chamber’ hypothesis is striking. While self-selective exposure to congruent content - the echo chamber - is not as salient as expected, the ideological bias induced primarily by algorithmic selection - the filter bubble - has been less scrutinized in the literature. In this study, we propose a new experimental research design to investigate recommender systems. To avoid any behavioral confounder, we rely on automated agents, which 'treat' the algorithm with ideological and behavioral cues. For each agent, we compare the ideological slant of the recommended timeline with the ideological slant of an artificially reconstructed chronological timeline and, hence, isolate the ideological bias of the recommender system. This allows us to investigate two main questions : (1) how much bias is induced by the recommender system? (2) what role is played by implicit and explicit cues, when triggering ideological recommendations?

The pre-registered experiment features 170 automated agents, which were active for three weeks before and three weeks after the 2020 American presidential election. We find that, after three weeks of delivering ideological cues (following accounts and interacting with the content), the average algorithmic bias is about 5%. In other words, the timeline as structured by the algorithm entails 5% less cross-cutting content than it does when it is structured chronologically. While the algorithm relies on both implicit and explicit cues to formulate recommendations, the effect of implicit cues is significantly stronger. This study is, up to our knowledge, the first experimental assessment of the ideological bias induced by the recommender system of a major social media platform. Recommendations rely above all on behavioral cues unwarily and passively shared by the user. As affective polarization becomes a greater contemporary challenge, our results raise important normative questions about the possibility of opting-out from the ideological bias of recommender systems. In addition, it points out that more transparency is urgently needed around the recommendation questions: How are algorithms trained? What cues or features do they use? Against which biases have they been tested? In parallel, the results demonstrate the failure of ‘in-house bias correction’ and calls for an external auditing framework, that would facilitate this kind of research and crowd-source the scrutiny of recommender systems.

All representatives are not equally powerful. This paper proposes a model of intra-partisan coordination, that incorporates the political capital held by representatives. Building upon traditional spatial models, it represents policy-making as a modified Tullock contest and outlines how party leaders strategically allocate resources within their party.

A crucial insight is that resource allocation is determined not only by ideological differences but also by the political power of individual MPs. To prevent the potentially damaging rebellion of influential extremists, leaders must allocate resources to these MPs despite their ideological divergence. Party leaders face a trade-off between promoting converging MPs and maintaining extremists within an acceptable distance from the party line.

Furthermore, the models underscore that leaders must strategically calibrate their tolerance for policy divergence. They strive to avert fragmentation at all costs and, depending on the joint distribution of preferences and political power within their party, they expand or restrict the political latitude afforded to MPs. This approach highlights the delicate balancing act leaders must perform to maintain unity while navigating various political factors.

Accounting for the intra-partisan distribution of political capital is not only important to better understand the dynamics of partisan unity but also to improve our understanding of representation. Capital inequality can indeed distort representation and contribute to reinforcing the over or under-representation of certain groups.

Electoral programs aggregate policy proposals targeting diversified social groups. How do voters trade-off between these targeted policy proposals in their prospective evaluation of manifestos? We develop and test three hypotheses, i.e. whether citizens judge programs primarily in view of the proposals targeting the groups (1) they belong to, (2) that are majoritarily perceived as deserving or (3) through the lens of ideological representations of groups. We designed a conjoint experiment in which respondents had to choose between hypothetical sets of party manifestos addressing different social groups. The presented manifestos are based on real-world electoral pledges taken from the German 2021 legislative and the French 2022 presidential electoral campaign. Our analyses reveal that voters’ program choice varies depending on which groups are targeted in a beneficial or detrimental way, reflecting for instance the finding that senior citizens are largely perceived as “deserving” while immigrants are not (2). However, our study also reveals considerable heterogeneity in program choice, less in terms of respondents’ group membership (1) than depending on ideology (3).

This paper revisits the established connection between electoral systems and representation, specifically spotlighting mixed-member electoral systems. MMPs are popular electoral rules expected, combining the advantages of both proportional and majoritarian electoral systems. In line with the seminal literature on this system, I extend the mandate divide hypothesis and underscore its conditional nature: MPs elected under different rules only behave differently when they face competing demands from their constituents and party. Utilizing the 19th German parliament as a case study and employing an observational design that integrates two innovative measurement strategies for MPs' and citizens' ideological positions, the findings reveal that district MPs hold preferences more aligned with their constituents than do list-elected MPs. Additionally, when they face competing demands, the two groups of MPs exhibit distinct roll-call behaviors, consistent with the conditional mandate divide hypothesis.

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This paper investigates the actual allocation of intra-party resources within the German and British parliaments. Drawing on the formal model's empirical implications, the chapter provides an in-depth analysis of the nature and importance of resources distributed by party leaders. Two primary findings emerge from this chapter. Firstly, the study corroborates the notion that both ideological distance —assessed through pairwise comparisons—and the relative power of MPs —gauged by Wikipedia traffic— play significant roles in intra-party resource allocation. Consequently, extremist MPs can achieve to secure party resources, if they are powerful enough. Secondly, the findings indicate that the electoral system significantly affects the value associated with a given resource. Floor speeches hold greater importance in the UK than in Germany, while key nomination positions are more central within the German parliamentary system.

Estimating ideological position has always been challenging for political scientists. The technical progress of the last decades -digitalization, computational improvements- opened new opportunities to measure ideological position. While classical approaches relied on surveys, new strategies relying on other sources of data such as social networks or text have been explored. This paper develops a strategy to estimate the ideological position of representatives on the basis of their floor speeches.

One big challenge when scaling from text is to ensure that the captured dimension matches the left-right axis. Existing models deal differently with this issue: Wordscores requires the identification of two documents meant to represent the extreme points of the dimension, Wordfish assumes the principal component maps the left-right and Wordshoal require to filter the document to build a homogeneous corpus structured around the dimension to measure. We propose another approach, which relies on a two-stage model. First, we train a neural network to predict the party of each speaker. Thus, we obtain for each document a vector of probabilities, which informs on the affiliation to each party. In other words, we represent the speeches in a low-dimensional space, where each dimension corresponds to one party. In the second stage, we further reduce the dimensions to obtain one linear scale, expected to map the left-right dimension.

To test the measure, we use each floor speech pronounced in the German, British, Spanish and French Parliaments in the last two decades (~2000000 Speeches). The speech-level ideological point estimation allows representing each representative as a distribution of ideological position, which offers more flexibility than single-point estimation. After validating the measure using more classical measure of political ideology, we use it to substantially investigate how the ideological position of representatives responds to electoral incentives.

Legislative studies have extensively benetted from the recent improvement of textual analysis

and scholars have learned to take advantage of the large amount of text produced by legislatures.

Among others, they have used text to estimate the ideological position of individual actors;

they have expanded the scope of their previous analyses by automating the coding processes of

textual documents and they also have improved our understanding of the strategic behaviours of

legislative actors, by looking in detail at their verbal interactions.

Following those developments, this paper proposes to take advantage of the textual modications

adopted during the legislative review, to estimate the amount of inuence, that a parliament exerts.

In doing so, it addresses an old -but widely accepted- idea, that, in parliamentary democracies,

most policies are written by the government all alone and the parliament is restricted to a mere

adoption role. Despite the large consensus supporting this expectation, there is only limited

empirical evidence.

Using data from the British House of Common and the German Bundestag and following an idea

rst proposed by Martin and Vanberg (2011), I compare the introduced and the nal versions of

each bill adopted during the last decade. Then, I compute the number of adopted modications

and obtain a Parliamentary Inuence Score (PIS). This PIS is expected to capture the extent to

which a bill is inuenced by members of Parliaments. The rest of the paper is dedicated to the

validation of the measure.

The Comparative Agenda Project (CAP) has been an instrumental tool for the academic community, offering insights into the policy agendas of various countries. This study investigates the potential of leveraging advanced machine learning methodologies, specifically transformers and transfer learning, to enhance the scalability and efficiency of CAP's classification tasks. Using a curated dataset of political documents, we first demonstrate the efficacy of transformer-based models in supervised topic classification, yielding significantly improved accuracy over traditional machine learning algorithms. Additionally, we explore the potential of transfer learning as a mechanism to adapt models trained on documents from one domain or language to another. Preliminary results show promising generalization across both domains and languages, paving the way for a more robust and scalable approach to document classification within CAP. This research not only advances the state-of-the-art in supervised topic classification but also holds implications for a broader range of applications in political science and computational linguistics.