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From the 2016 and 2020 US elections to the 2024 European and US elections, digital tools in election campaigns have made it possible to broaden the audience of candidates’ messages, reduce the cost of spreading information and communicate more effectively.
The 2008 U.S. presidential election is widely regarded as the first to make extensive and systematic use of big data and digital tools in political campaigning (Bimber, 2014). President Obama’s campaign strategy relied on social media such as Facebook, MySpace and Twitter, and on the creation of the well-known website MyBarackObama.com. The website was a key element of the digital strategy because it allowed to collect and analyse a large amount of data on the leader’s supporters. Volunteers could create personal profiles, organise local events, communicate with other activists and donate to the campaign. This allowed Obama’s team to identify the most active and influential supporters and send them targeted, personalised messages.
The growing use of political data in modern campaigns has raised significant concerns about the spread of fake news and electoral manipulation, culminating in the Cambridge Analytica scandal. The company had illegally obtained the data of millions of Facebook users to build detailed psychographic profiles and send targeted political messages, thereby influencing the outcome of elections.
This episode highlighted the need to establish clear guidelines and rules on the use of big data in election campaigns to protect privacy and the integrity of the democratic process.
Alongside election campaigns, data is increasingly used in other areas of political life, such as public affairs, advocacy campaigns and the evaluation of public policies. Indeed, a growing trend towards data-driven policy decisions has been recorded worldwide. The concept of data-informed decision-making has shaped the practices of private actors and public decision makers, and has also become a subject of academic study (Jetzek et al., 2014; Duncan et al., 2021).
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The picture becomes even more complex when we consider the role that artificial intelligence is gaining in decision-making. US political campaigns have already used these tools to create advertisements and optimise fundraising. Generative AI is reshaping modern campaigns, although the exact nature of its influence is still being assessed (LaChapelle and Tucker, 2024).
The concept of Data-Driven Campaigning
In this context, academic literature has extensively explored the concept of data-driven campaigning (DDC), a reference model for today’s election campaigns based on two types of practice: targeting and testing. Targeting means using data to decide which messages to send to which potential voters and at what point in the campaign, while testing refers to empirically measuring how well messages perform against one another (Baldwin-Philippi, 2019).
Munroe and Munroe (2022) identify three essential elements of an election campaign based on DDC principles:
Although the literature on the use of data in election campaigns is mainly American, Europe has seen growing interest in reinterpreting this concept from a continental perspective. Recently, Dommett, Barclay and Gibson (2024) developed a definition of data-driven campaigning (DDC) that seeks to avoid being shaped by the US political system:
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In Italy, the most interesting analysis comes from De Rosa (2018), who summarises the latest developments in the use of big data in election campaigns, focusing on the opportunities and limits of data-driven analysis and strategies. On the other hand, Luigi Di Gregorio (2024) examines the current political context and describes it as a “permanent political campaign”, stressing the importance of data analysis in decision-making processes. In his model for defining the strategy of political communication campaigns, the “Control Room“, data-based analysis and the related digital communication strategies carry significant weight. Alongside traditional figures such as the spin doctor or political communication agencies, Professor Di Gregorio suggests, there is a need for new professionals able to monitor and interpret a growing volume of data and to guide the decisions taken to build consensus. Hence the emergence of permanent structures for data-driven decision making.
What types of data are found in a Data-Driven Campaign?
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What impact is Data-Driven Campaigning having on elections?
The 2024 European elections and the November 2024 US presidential election confirmed the growing presence of data-driven campaigns. Concerns about the misuse of digital tools and social networks are at the heart of public debate, while regulators are trying to keep pace with the latest innovations.
In the United States, the 2020 elections, shaped by the pandemic, marked a further shift towards digital innovation in campaigning. Joe Biden skilfully used online platforms to engage with voters, raise funds and analyse data in real time. As reported by Reuters, ahead of the 2024 elections the Republican and Democratic parties were investing millions of dollars in their respective data analytics companies, Data Trust and Democratic Data Exchange. The aim was to build national databases, analyse voters in detail, identify undecided voters and carry out predictive analysis of positions on the most pressing issues.
In Europe, the use of data in political campaigns is still limited compared with the United States, mainly because of the rules on data and digital platforms (such as the GDPR and the DSA) and the small number of companies specialising in political data analytics. However, the use of data and digital tools for political and electoral purposes is growing steadily. An analysis by Barclay et al. (2024) of parties in Austria and the United Kingdom showed that parties use different types of data (voter data, public data, digital trace data, etc.) for purposes that are not solely electoral. For example, parties such as the British Labour Party and Conservative Party have divided the electorate into large-scale segments to tailor candidates’ messages, signals and behaviour to each audience. Moreover, although partly prohibited, micro-targeted political advertising is common practice in Europe. Micro-differentiated ads on social networks were already used in Italy in 2018 (Cepernich, 2019). Finally, in Germany, the privacy protection organisation NOYB (None of Your Business) filed a series of complaints against several German political parties that engaged in these practices.
At European level, a briefing published by the European Parliamentary Research Service in May 2024 examined the use of data-driven campaigning ahead of elections in several European countries (Italy, Germany, Spain, France, Hungary, the Netherlands, Estonia and Norway). The document identified risks and benefits relating to e-voting, data management, online political microtargeting and AI-augmented politics. It also analysed the relevant rules, with particular reference to the GDPR and the Digital Services Act (DSA).
On the regulatory front, in March 2024 the European Union adopted a regulation on the transparency and targeting of political advertising (TTPA), aimed at countering information manipulation and foreign interference in elections. Key measures include labelling political ads, defining the contexts in which targeting is allowed, and banning the provision of advertising services to sponsors from third countries in the three months before an election or referendum. With some exceptions, the regulation has applied since 10 October 2025. The complexity and cost of the new obligations led some of the main operators, including Adform, Google, Microsoft and Meta, to suspend political advertising in the European Union from October 2025. Political content can still be published organically, but it can no longer be amplified through paid ads, a change that is reshaping how data-driven campaigns are run in Europe.
Finally, in April 2024 the European Commission published guidelines for large technology companies, known as VLOPs and VLOSEs, to mitigate risks to electoral processes. The most important measures include:
References
Aldrich, J. H., Gibson, R. K., Cantijoch, M., & Konitzer, T. (2016). Getting out the vote in the social media era: Are digital tools changing the extent, nature and impact of party contacting in elections? Party Politics, 22(2), pp. 165-178.
Baldwin-Philippi, J. (2019). Data campaigning: Between empirics and assumptions. Internet Policy Review, 8(4), pp. 1-18.
Barclay, A., Dommett, K., & Russmann, U. (2024). Data Driven-Campaign Infrastructures in Europe: Evidence from Austria and the UK. Journal of Political Marketing, pp. 1-20.
Bimber, B. (2014). Digital media in the Obama campaigns of 2008 and 2012: Adaptation to the personalized political communication environment. Journal of information technology & politics, 11(2), pp. 130-150.
Cepernich, C. (2019). Digital Campaigning: The Communication Strategies of the Leaders on Facebook. In The Italian General Election of 2018: Italy in Uncharted Territory, pp. 217-243.
De Rosa, R. (2018). L’uso dei big data nella comunicazione politico-elettorale. Comunicazione politica, 2, pp. 199-224.
Di Gregorio, L. (2024). War Room: attori, strutture e processi della politica in campagna permanente. Rubbettino Editore, pp. 355-359.
Dommett, K., Barclay, A., & Gibson, R. (2024). Just what is data-driven campaigning? A systematic review. Information, Communication & Society, 27(1), pp. 1-22.
Duncan, P. B., Edgar, D. A., Magnaghi, Okwechime, E., & Veglianti, E. (2021). Chapter: Big data: An introduction to data-driven decision making. In Organizing Smart Buildings and Cities: Promoting Innovation and Participation, pp. 35-46.
Jetzek, T., Avital, M., & Bjorn-Andersen, N. (2014). Data-driven innovation through open government data. Journal of theoretical and applied electronic commerce research, 9(2), pp. 100-120.
LaChapelle, C. and Tucker, C. (2024). Generative AI in Political Advertising, Brennan Center for Justice. Available at: https://www.brennancenter.org/our-work/research-reports/generative-ai-political-advertising (Accessed: 30 May 2024).
Munroe, K. B., & Munroe, H. D. (2022). “Chapter 11: Local Data-Driven Campaigning” in Inside the Local Campaign: Constituency Elections in Canada, pp. 245-264.
Nguyen, B. (2024). 2024 candidates are using AI in their campaigns – here’s how to spot it. Forbes Business. Available at: https://www.forbes.com/sites/britneynguyen/2023/12/13/2024-candidates-are-using-ai-in-their-campaigns-heres-how-to-spot-it/ (Accessed: 19 June 2024).
Walker, D., & Nowlin, E. L. (2021). Data-driven precision and selectiveness in political campaign fundraising. Journal of Political Marketing, 20(2), pp. 73-92.
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