Survey research is entering a new era which centres on its linkage with other forms of digitally generated data such as social media. Many suggest that this can help to address existing weaknesses in self-report surveys such as non-response and measurement bias. However, to link a participant's survey responses to their social media data, consent from the participant is required. Previous studies have shown that consent to linkage is typically low and selective. This paper expands on the existing literature by comparing Twitter (now X) usage and consent to survey linkage across five national contexts. Testing the effects of several sociodemographic and attitudinal predictors in the US, the UK, France, Germany and Poland, our study finds that overall consent rates vary significantly by age, political attention, privacy concern, trust in social media companies and frequency of political posting on Twitter/X. However, our results also confirm that variable effects differ significantly between nations, suggesting a moderating cultural influence. Within-country variation in the US between 2020 and 2024 is also present, indicating that effects are not necessarily fixed over time. These findings dictate the need for caution when conducting substantive comparisons across countries and time when using social media data.

Introduction & Background Over the last two decades, the digital revolution has led to an explosion of new data sources commonly referred to as digital footprint or trace data (DTD). This rapid expansion in digital data sources has pushed survey research into a new era of development that now centres on its linkage with various participant DTD. This culture shift has unlocked a range of novel opportunities for social scientists to access rich new sources of insight into human behaviour which can be used to augment, validate or even replace conventional self-reported survey data. However, when it comes to making such data open access, there remains a critical gap about maintaining respondent anonymity when it comes to openly releasing DTD. Objectives & Approach This paper will focus on demonstrating the conceptual and methodological value and challenges in producing anonymised and standardised variables from survey respondents’ digital trace data (DTD). We will do this using existing YouGov datasets collected over two time periods in the US 2020 and 2024, and a third collected in the UK 2022. The US datasets link individual survey responses to their Twitter/X feeds and the UK to their browsing history. All three datasets were designed to address research questions about the effects of digital media consumption and exposure on citizen attitudes and behaviours. This paper aims to establish a standardised and automated process for variable generation which is replicable and can produce anonymised variables from the DTD which can be safely linked to respondent survey data and openly shared with the wider research community. Relevance to Digital Footprints The aim of this work is to encourage other researchers working with digital footprint data to consider the ethical and legal implications they face when looking to make their DTD open access. Our work aims to resolve the conflict between open access and data protection, bridging the gap by establishing a process for deriving anonymous unit-level variables which can be released in lieu of the raw DTD. While not designed to be an entirely prescriptive method, this paper strives to inform strategies for making DTD open access and to start the process of creating better standardised practices within the discipline. Conclusions & Implications While this paper is still a work in progress, work is underway for variable generation and will result in the creation and release of a standardised procedure for the anonymisation of DTD. These variables will be created for two specific types of DTD: social media and web-browsing data. However, these variables will be translatable to various other types of DTD and this paper will be accompanied by step-by-step code and codebook which can be used by other researchers. This paper will have significant ethical and methodological implications for how researchers working with DTD make their data open access and will hopefully improve transparency and collaboration within the discipline.

This paper analyses the adoption of data-driven campaigning (DDC) by German and French parties in recent national elections using data from an original post-election survey of 27 parties (12 German, 15 French) and a new purpose-built DDC campaign index. Specifically, we investigate two main research questions: (1) Do countries and parties vary in the extent to which DDC is practised? (2) If so, what explains those differences? We find that while in both countries DDC adoption is limited in comparison to other campaign modes, differences exist across countries and parties based on a range of macro (systemic) and meso (organisation-level) factors. Most notably, contrary to normalisation theory, we find that minor parties with a 'netroots' base and newcomers who are digital 'natives' engage more in DDC than the 'legacy' major parties. Diese Studie untersucht den Einsatz datengesteuerter Kampagnen (DDC) durch deutsche und franz & ouml;sische Parteien bei den vorangegangenen nationalen Wahlen. Aus Daten aus einer Nachwahlbefragung von 27 Parteien (12 deutsche, 15 franz & ouml;sische) wird ein neuer DDC-Kampagnenindex entwickelt. Die Studie widmet sich zwei Hauptforschungsfragen: (1) Gibt es L & auml;nder- und Parteienunterschiede in der Anwendung von DDC? (2) Wenn ja, welche Faktoren erkl & auml;ren diese Unterschiede? In beiden L & auml;ndern zeigt sich der Einsatz von DDC im Vergleich zu anderen Wahlkampfmitteln als begrenzt. Es zeigen sich jedoch Unterschiede zwischen L & auml;ndern und Parteien, die auf einer Reihe von Makro- (systemischen) und Mesofaktoren (auf Organisationsebene) beruhen. Entgegen der Normalisierungsthese nutzen kleinere Parteien mit einer << Netroots >> -Basis und Newcomer, die digitale << Natives >> sind, DDC intensiver als die etablierten << Altparteien >>. Cet article analyse l'adoption des campagnes guid & eacute;es par les donn & eacute;es (CGD) par les partis allemands et fran & ccedil;ais lors des derni & egrave;res & eacute;lections nationales, en utilisant des donn & eacute;es provenant d'une enqu & ecirc;te post-& eacute;lectorale originale, men & eacute;e aupr & egrave;s de 27 partis (12 allemands, 15 fran & ccedil;ais). Il s'appuie sur la cr & eacute;ation d'un indice CGD con & ccedil;u & agrave; cet effet. Nous & eacute;tudions deux questions de recherche principales: (1) Existe-t-il des variations dans l'usage des campagnes guid & eacute;es par les donn & eacute;es (CGD) selon les pays et les partis? (2) Si oui, qu'est-ce qui explique ces diff & eacute;rences? Nous constatons que si, dans les deux pays, l'adoption des CGD reste limit & eacute;e comparativement & agrave; d'autres modes de campagne, il existe des diff & eacute;rences entre les pays et les partis en fonction d'une s & eacute;rie de facteurs macro (syst & eacute;miques) et m & eacute;so (au niveau de l'organisation). En particulier, et contrairement & agrave; la th & eacute;orie de la normalisation, nous constatons que les petits partis disposant d'une base internaute, ainsi que les organisations << nativement num & eacute;riques >> s'engagent davantage dans les CGD que les grands partis << traditionnels >>. L'articolo analizza l'adozione del data-driven campaigning (DDC) da parte dei partiti tedeschi e francesi nelle recenti elezioni nazionali, utilizzando i dati di un'indagine post-elettorale originale su 27 partiti (12 tedeschi, 15 francesi) e un nuovo indice DDC appositamente costruito. Due le principali domande di ricerca: (1) esistono delle differenze fra questi due paesi Paesi e i rispettivi partiti per quanto riguarda l'uso di DDC? (2) Se s & igrave;, cosa spiega queste differenze? L'analisi dimostra che, mentre in entrambi i Paesi l'adozione di DDC & egrave; limitata rispetto ad altre modalit & agrave; di campagna, esistono differenze basate su una serie di fattori macro (sistemici) e meso (a livello di organizzazione). In particolare, e contrariamente alla teoria della normalizzazione, troviamo che i partiti minori con una base "netroots" e i "nativi" digitali usano maggiormente DDC rispetto ai partiti tradizionali.

This paper investigates how the acceptance of data-driven political campaigning depends on four different message characteristics. A vignette study was conducted in 25 countries with a total of 14,390 respondents who all evaluated multiple descriptions of political advertisements. Relying on multi-level models, we find that in particular the source and the issue of the message matters. Messages that are sent by a party the respondent likes and deal with a political issue the respondent considers important are rated more acceptable. Furthermore, targeting based on general characteristics instead of individual ones is considered more acceptable, as is a general call to participate in the upcoming elections instead of a specific call to vote for a certain party. Effects differ across regulatory contexts, with the negative impact of both individual targeting and a specific call to vote for a certain party being in countries that have higher levels of legislative regulation.

Much of the research on political microtargeting has focused on growing public concerns about its use in elections, fuelling calls for greater regulation or even a ban on the practice. We contend that a more nuanced understanding of public attitudes toward microtargeting is required before further regulation is considered. Drawing on advertising psychology research and the results of academic analyses into microtargeting, we argue that individual concern, and by corollary, acceptance of microtargeting will vary based on socio-demographic characteristics and political orientations, and the type of personal data used. We hypothesise that microtargeting that relies on observable or publicly accessible personal information will be more accepted by voters than that which uses unobserved and inferred traits. We test these expectations and the expected variance of public acceptance by individual characteristics using comparative survey data from the US, Germany, and the Netherlands. We find that across countries and socio-demographic groups, not all microtargeting is considered equally problematic. For example, whereas the use of age and gender is generally deemed acceptable, the use of sexual orientation is not, and right-leaning individuals are more accepting than those who lean left. Additionally, overall, the US is more accepting of microtargeting than Germany or the Netherlands. Thus, we find that not all microtargeting is considered equally problematic across countries and socio-demographic groups. We conclude by calling for a more contextualised debate about the benefits and costs of political microtargeting and its use of “sensitive” data before the expansion of current regulation.

Technological advancements create new ways of informing and persuading citizens in the political advertising context. Insights are limited regarding how citizens deal with data-driven political advertising (DDPA). This is problematic because the collection and combination of large amounts of data render them vulnerable to information and power asymmetries. Using multidisciplinary perspectives, this article discusses the digital campaign competence of voters. We look at the interplay of literacy components, offer a typology, and predict campaign behavior, such as ad engagement and ad avoidance. We use data from a multiple-wave panel survey (NW1 = 1914, NW3 = 1303) conducted during the 2021 German federal elections. A latent profile analysis reveals five voter profiles with varying levels of DDPA literacy (i.e. conceptual understanding and evaluative perceptions). People mostly evaluate DDPA as neutral or negative, highly differ in their level of objective conceptual understanding, and underestimate the effectiveness of DDPA. We find no differences between the five profiles in their ad engagement but find differences concerning ad avoidance. The results deepen our understanding of a digitally campaign-competent electorate and highlight areas in which citizen empowerment is needed in light of the inequalities that DDPA has produced.

Discussions of data-driven campaigning have gained increased prominence in recent years. Often associated with the practices of Cambridge Analytica and linked to debates about the health of modern democracy, scholars have devoted considerable attention to the rise of data-driven politics. However, most studies to date have focused solely on practice in the US, and few scholars have made efforts to define the precise meaning of 'data-driven campaigning'. With growing recognition that data-driven campaigning can take different forms dependent on context and available resource, new questions have emerged as to exactly what features are indicative of this phenomena. In this piece we systematically review existing discussions of data-driven campaigning to unpack the components of this idea. Identifying areas of convergence and divergence in existing discussions of 'data', 'driven', and 'campaigning', we classify existing debate to highlight integral features and variable practices. This article accordingly provides the first comprehensive definition of data-driven campaigning, and aims to facilitate international study of this activity.

Contemporary political campaigning takes place both online and offline, and can be data-driven. In this piece, we review existing knowledge around data-driven campaigning (DDC) and introduce the new contributions made by the pieces within this thematic issue. We reveal how the studies included in this thematic issue of Media and Communication contribute to this existing knowledge by providing an up-to-date account of how DDC in general, and political microtargeting in specific, have been employed in election campaigns between 2021 and 2023, in a range of countries: France, Germany, the Netherlands, Sweden, and the US. As a collection, these studies highlight the variance that exists in the degree to which DDC is practiced, the range of DDC tools used, and attitudes toward DDC. In recent election campaigns, DDC takes many forms, and disapproval of DDC varies depending on how it is implemented.

Since the Cambridge Analytica scandal, governments are increasingly concerned about the way in which citizens' personal data are collected, processed and used during election campaigns To develop the appropriate tools for monitoring and controlling this new mode of "data-driven campaigning" (DDC) regulators require a clear understanding of the practices involved. This paper provides a first step toward that goal by proposing a new organizational and process-centred operational definition of DDC from which we derive a set of empirical indicators. The indicators are applied to the policy environment of a leading government in this domain - the European Union (EU) - to generate a descriptive "heat map" of current regulatory activity toward DDC. Based on the results of this exercise, we argue that regulation is likely to intensify on existing practices and extend to cover current "cold spots". Drawing on models of internet governance, we argue that this expansion is likely to occur in one of two ways. A "kaleidoscopic" approach, in which current legislation extends to absorb DDC practices and a more "designed" approach that involves more active intervention by elites, and ultimately the generation of a new regulatory regime.

Studies of online campaigning have consistently demonstrated a positive impact on electoral success, but it remains unclear how this occurs. Some find the content and style of post matter, while others have pointed to overall activity as the key driver, promoting a "broadcast" effect model. Still, others have argued for indirect effects whereby candidates rely on followers to share content within their networks. This paper develops a "joined-up" model to test these arguments that includes new measures to capture the responsiveness of candidate tweets and the extent of user engagement. We apply the model to the 2017 UK General Election Twitter campaign. Our findings confirm a digital campaign effect, but in a two-step rather than direct manner. Specifically, candidates that attract more engagement with their tweets (likes and retweets) enjoy more electoral success. We expand on our findings to argue for a "network" rather than "broadcast" model of digital campaign effects.

has important implications about the habit formation thesis. It suggests that early electoral experiences are critical in establishing long-term voting patterns. Anticipating this idea, Plutzer (2002

The effectiveness of approaches to bot detection varies, with real-time detection being almost impossible. As a result, this article argues that the general Twitter using public cannot be expected to judge which accounts are bots with certainty and therefore do not know to what extent they are being manipulated online. In this article, the challenge of detecting bots and fake accounts is demonstrated by constructing two distinct methods to bot detection. The first method takes a fixed criteria-based approach, by building on commonly cited identifiers for bots. The second method takes a more flexible, investigative approach in order to uncover bots involved in coordinated efforts to influence online debates. As well as profiling the specific mechanics of how each one operates, we argue that they can be compared against an evaluative framework that specifies a set of key criteria that bot detection methods should meet in order to perform. Here, we identify four key criteria on which these methods can be evaluated and then examine how they perform in terms of the key criteria of accuracy. The results of these methods are then compared and cross-checked against an existing and widely used bot detection service. The findings show that different bot detection methods can present significantly different results and that only confirmation from Twitter, through suspensions or announcements, can truly allow users to know whether an account is a bot or not. We argue that this development could have a significant effect on the level of trust that social media users have both in the information they receive through social media and also in the political process.

Concern about whether contemporary societies face a “crisis of democracy” has grown in recent years (Kreisi, 2020). While the severity of the malaise may be disputed, there is growing suspicion that the increasing reliance of political actors on digital technology and particularly new “data driven” campaign techniques may be contributing to growth in citizen disengagement and discontent (Bennett & Lyon, 2019). The grounds for this claim are essentially three-fold. First, data-driven campaigns promote a more individualized form of political targeting that allows parties to narrow their appeals to the most persuadable and “perceived” sections of the electorate (Hersh, 2015), and thereby effectively bypass those harder to reach groups of under-mobilized voters, i.e. the young, the disinterested, and the marginalized. Furthermore, through these microtargeting techniques, campaigners can more accurately target demobilizing messages at opposition supporters to dissuade them from turning out. Second, social media platforms provide powerful new channels for the release of automated, anonymized, false information or “computational propaganda” by rogue actors, both foreign and domestic. These disinformation campaigns are explicitly designed to mislead and confuse voters and are escalating in scale and sophistication (Woolley & Howard, 2018). Finally, campaigns themselves are now increasingly reliant on the “wisdom” of AI and computer modeling for basic tasks such as resource allocation and message construction. This shift creates a new technological elite at the heart of campaigns that operate in an opaque and unaccountable manner (Tufekci, 2014). The combined impact of these developments is a further shrinking of the public sphere and decline in the representativeness and accountability of democratic institutions. Voters who do actually make it the polls face the increasingly difficult task of making an informed choice, as they struggle to discern both the accuracy and source of the political content they encounter online. Given the potentially serious harms that DDC presents to democracy, systematic investigation of its adoption and usage across countries is now a priority for academic research. This is precisely the goal of a new ERC funded project, Digital Campaigning and Electoral Democracy (DiCED). In this short essay we highlight in brief, the key questions the project will pursue and that we urge the wider literature to explore.

One of the central questions in electoral research

Social media campaigning is increasingly linked with anti-democratic outcomes, with concerns to date centring on paid adverts, rather than organic content produced by a new set of online political influencers. This study systematically compares voter exposure to these new campaign actors with candidate-sponsored ads, as well as established and alternative news sources during the US 2020 presidential election. Specifically, we examine how far higher exposure to these sources is linked with key trends identified in the democratic deconsolidation thesis. We use data from a national YouGov survey designed to measure digital campaign exposure to test our hypotheses. Findings show that while higher exposure to online political influencers is linked to more extremist opinions, followers are not disengaging from conventional politics. Exposure to paid political ads, however, is confirmed as a potential source of growing distrust in political institutions.

Political campaigns are increasingly described as data-driven, as parties collect and analyse large quantities of voter data to target their campaign messages in ever more granular ways, particularly online. These practices have increasingly been facing calls for greater regulation due to the range of harms they are seen to pose for citizens and democracy more generally. Such harms include the intrusions on voter privacy, reduced transparency in how messages are constructed and targeted at voters and exposure to increasingly divisive and polarizing political content. Given that data-driven campaigning (DDC) encompasses a range of different practices that are likely to fall under the remit of multiple agencies, it is not evident how suitable current regulatory frameworks are for addressing the harms associated with the growth of DDC. This paper takes a first step toward addressing that question by mapping an emergent regulatory “ecosystem” for DDC in the particular case of the UK. Specifically, we collect and analyse interview data from a range of regulators working directly or indirectly in the election campaigns and communication arena. Our analysis shows that while privacy violations associated with DDC are seen by regulators to be largely well covered by current legislation, other potential harms are given lesser to no priority. These gaps appear to be due to regulators lacking either the powers or the incentives to intervene.

Abstract This chapter reviews the growing body of literature that has emerged on the subject of digital campaigns since they first emerged as a web- and email-based activity in the mid-1990s. It does so chronologically and shows how, when viewed in the aggregate and from a historical perspective, these studies form a narrative that maps onto the four distinct phases of development outlined in Chapter 1. Thus, early studies largely describe a period of experimentation in most countries, while subsequent work in the late 1990s and the first decade of the 2000s reveals a shift toward standardization in practice across parties and countries. Analyses from the middle of the first decade of the twenty-first century focus largely on online political community-building efforts by web campaigns, while the most recent work examines how digital tools are increasingly being used to intensify micro-targeting of voters