Gibson, R., Bon, E., & Dommett, K. (2024). “I always feel like somebody’s watching me”: What do the U.S. electorate know about political micro-targeting and how much do they care?. Journal of Quantitative Description: Digital Media, 4. https://doi.org/10.51685/jqd.2024.001

Abstract

The practice of political micro-targeting (PMT) – tailoring messages for voters based on their personal data – has increased over the past two decades, particularly in the U.S. Studies of PMT have to date concentrated largely on its effects on voters, or its implications for democracy more broadly. Less attention has been given to answering basic descriptive questions about how people perceive, feel and care about this new mode of political communication. This paper fills that gap by reporting findings from an online survey (weighted to be nationally representative on age, gender, ethnicity, region and past vote) that measured public attitudes toward PMT during the 2020 U.S. Presidential campaign. Specifically, we measure voter orientations toward PMT in four key dimensions – awareness, aversion, knowledge, and acceptability at the aggregate level – and explore how these vary according to a range of individual characteristics. Key findings are that public understanding and acceptance of PMT may be higher than current studies indicate, particularly among certain sectors of the population. Such insights are important for academic research to cognize and also policy-makers, as they move toward greater regulation of voter targeting.

Gibson, R., Bon, E., Darius, P., & Smyth, P. (2023). Are Online Political Influencers Accelerating Democratic Deconsolidation?. Media and Communication, 11(3), 175-186. https://doi.org/10.17645/mac.v11i3.6813

Abstract

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.

Gibson, R., Bon, E., & Römmele, A. (2023). Operationalizing data-driven campaigning: designing a new tool for mapping and guiding regulatory intervention. Policy Studies, 1–17. https://doi.org/10.1080/01442872.2023.2259333

Abstract

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.

Abstract

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 of 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 their 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.

Bon, Esmeralda; Darius, Philipp; Gibson, Rachel, Gibson; Greffet, Fabienne and Andrea Rommele

Abstract

This paper analyses the adoption of data-driven campaigning (DDC) by German and French parties in recent national elections by investigating three main research questions: (1) do countries and parties vary in the extent to which DDC is practised? (2) if so, what explains those differences? and (3) are certain DDC techniques more or less prominent due to EU and national regulatory frameworks? We hypothesise the impact of a range of institutional (macro-level) and organisational (meso-level) factors on the take-up of DDC across parties and party systems and test these with original post-election survey data from 27 parties (12 German, 15 French) and a new purpose-built DDC campaign index. We find that although DDC use is limited in both systems, uptake is responsive to a combination of the regulatory environment and parties’ prior campaign practices. Contrary to normalisation theory, minor parties with a ‘netroots’ base and newer digital ‘natives’ engage more in DDC than the ‘legacy’ major parties.

Esmeralda Bon

Abstract

Debates about the value of social media versus survey data for measuring public opinion have intensified as usage of the former has spread and increasing problems with the latter have surfaced. To measure the salience of policy issues during an election campaign, surveys which include MII or MIP questions are typically seen as the gold standard. However, in contrast to the survey, social media data can provide a dynamic impression of the issues which gain and lose attention over time. In this paper, we compare Twitter and survey data as issue salience measures in the 2020 US presidential election campaign. We ask three questions: (1) To what extent do these data produce similar findings about issue salience? (2) How does this (dis)similarity affect our conclusions about what issues mattered? and (3) what do these findings say about the value of these data for measuring issue salience? We find that while there is some overlap in the range of salient issues identified by both data sets, the two sources lead to different conclusions about the decisiveness of these issues. The survey data suggests that the election outcome centered on the issues of healthcare, the economy and the coronavirus pandemic and that issues like social security and immigration were also important. In contrast, the latter two issues are not salient in the Twitter data. The Twitter data suggests that law and order and education were more important, instead, and shows that postal voting became a highly salient issue later in the campaign. Our evidence does not permit us to rule definitively in favor of one data source over the other. Instead, we conclude that one should focus on the relative value of each for measuring particular aspects of public opinion.

Rachel Gibson and Esmeralda Bon

Abstract

The practice of political micro-targeting (PMT) – tailoring messages for voters based on their personal data – has increased significantly over the past two decades, particularly in the U.S. While studies consistently show that publics are very concerned about the use of PMT in elections, the reasons for that opposition have not been subject to detailed theoretical or empirical analysis. Most studies cite privacy preference as a core motivation behind voter concerns but analyses typically focus on their linkage to standard socio-demographic correlates and partisanship. This paper seeks to advance this research by developing and testing a new explanatory model of attitudes toward PMT that examines the extent to which privacy concerns are driving peoples’ fears about PMT across three established democracies, vis a vis other psychological, socioeconomic and demographic factors. In particular, we examine the idea that privacy concerns, while important in determining attitudes toward PMT may be offset or moderated by the perceived benefits of personalised ad content. This so-called ‘privacy calculus’ has been demonstrated for commercial advertising, however, whether it applies to political campaigns has not yet been explored. We hypothesise it will have more limited impact in the political context, but where it is detected, it will be most influential in the US. We further argue that its impact will vary according to the type of data used for PMT. We test our expectations using survey data from a representative YouGov sample of U.S. German and French voters during recent election campaigns. Our findings show that controlling for socio-demographic and other psychological traits, the privacy calculus plays a strong and significant role in moderating concerns about PMT regardless of national context, but that its effect does vary slightly according to the type of personal data used. Our findings are important in that they suggest we need to adopt a more nuanced understanding of current worries about PMT, and particularly that there it may hold value for some voters. It also calls into question whether moves to ban the practice entirely are necessary or appropriate.

Esmeralda Bon, Rachel Gibson, Fabienne Greffet, Mickael Temporão and Peter Smyth

Abstract

Online political influencers (OPIs) are increasingly important actors during election campaigns, however, there is as yet little agreement on what defines them. This lack of conceptual clarity is reflected in the diversity of methods that have been used to identify them. In this paper, we seek to advance this debate by applying three distinct approaches to identifying OPIs on Twitter during the 2022 French Presidential and Parliamentary elections and assessing the pros and cons of each. Specifically, we compare the outcomes from using expert judgement versus more inductive methods that rely either on digital trace data supplied by survey respondents, or automated identification strategies that use aggregated tweets for identifying political influencers. We evaluate the performance of each approach post-identification by comparing the influencers they identify on a range of indicators including their overall number, degree of overlap, variance in ideological outlook and type of actor, as well as follower numbers and network reach. Based on our findings we argue there is no one ideal method for identifying OPIs and choice of approach is largely dependent on the research question posed.  

Philipp Darius, Rachel Gibson, Esmeralda Bon and Peter Smyth

Abstract

Recent elections have seen a growing role for a new type of online political actor – political influencers. Most studies have focused on ‘top-down’ questions that focus on detection and content analysis. Analyses of their follower base have been limited and primarily relied on self-report survey data. Part of the reason for this gap is a lack of relevant data that differentiates or categorizes types of influencers, and that can link influencers to their audience. Our study addresses this gap by combining survey and observational data to measure the attitudes and behaviours of those following different ideological types of political influencers on Twitter during the US 2020 campaign. We divide influencers into four ideological groups – right, left, independent, non- partisan – and compare their followerships’ based on levels of variance in critical political attitudes (trust, interest, ideology, conspiracy belief, vote intention) and vote decisions. We identify influencers using our survey data and manual coding of accounts. We further contrast them with followerships’ of mainstream and alternative news media organizations and candidates or parties, classified into the same ideological categories. We expect ideological congruence between influencers and followers and homogeneity in attitudes (i.e. they are sheep and not cats). We test our expectations using a linked dataset that combines content from individuals’ Twitter feeds (n= 697) with their responses to a two- wave pre- and post-election online panel survey fielded by YouGov. We conclude by reflecting on the findings’ implications concerning media fragmentation, partisan polarisation, and distrust in democratic elections.