Publications
Below you will find some of our recent publications. Happy readings and do get in touch with any thoughts, questions or ideas for collaborations.
Communicating Europe: a computational analysis of the evolution of the European Commission’s communication on Twitter
Authors: Rocca, R., Lawall, K., Tsakiris, M. et al.
Social media is an important means of communication for political agencies, which makes it possible to engage with large sectors of the public. For institutions which are not directly elected by voters, such as the European Commission (EC), social media can be a strategic tool for increasing perceived legitimacy and citizen engagement, especially in contexts of high politicization. In this paper, we use natural language processing techniques to provide a comprehensive overview of how EC communication on Twitter has evolved between 2010 and 2022, with respect to both its topics and its style. Our analyses show that, over time, the focus of EC communication has shifted substantially from economy-, finance- and governance-related topics, towards social policy, digital and environmental policy, and identity. These changes have progressively differentiated the EC’s profile from that of other institutions (especially more technocratic ones) and contributed to better alignment with engagement patterns of its social media audience. In addition, EC communication has become less neutral (in favor of more positive sentiment), simpler, and more readable, all features which are associated with more accessible and engaging messaging. Yet, while the EC currently scores better than most other reference agencies on several descriptors of accessibility, its style is still lexically more complex, less concrete and less action-oriented than that of other institutions. Alongside providing novel insights on how the EC’s online communication and projected political identity have changed over time, this study lays the foundations for future experimental and hypothesis-driven work combining social media data with external data sources.
Rocca, R., Lawall, K., Tsakiris, M., & Cram, L. (2024). Communicating Europe: a computational analysis of the evolution of the European Commission’s communication on Twitter. Journal of Computational Social Science, 1-52. https://doi.org/10.1007/s42001-024-00271-w
Interventions to counter misinformation are often less effective for polarizing content on social media platforms. We sought to overcome this limitation by testing an identity-based intervention, which aims to promote accuracy by incorporating normative cues directly into the social media user interface. Across three pre-registered experiments in the US (N = 1709) and UK (N = 804), we found that crowdsourcing accuracy judgements by adding a Misleading count (next to the Like count) reduced participants' reported likelihood to share inaccurate information about partisan issues by 25% (compared with a control condition). The Misleading count was also more effective when it reflected in-group norms (from fellow Democrats/Republicans) compared with the norms of general users, though this effect was absent in a less politically polarized context (UK). Moreover, the normative intervention was roughly five times as effective as another popular misinformation intervention (i.e. the accuracy nudge reduced sharing misinformation by 5%). Extreme partisanship did not undermine the effectiveness of the intervention. Our results suggest that identity-based interventions based on the science of social norms can be more effective than identity-neutral alternatives to counter partisan misinformation in politically polarized contexts (e.g. the US).
Pretus C, Javeed AM, Hughes D, Hackenburg K, Tsakiris M, Vilarroya O, Van Bavel JJ. The Misleading count: an identity-based intervention to counter partisan misinformation sharing. Philos Trans R Soc Lond B Biol Sci. 2024 Mar 11;379(1897):20230040. doi: 10.1098/rstb.2023.0040. Epub 2024 Jan 22. PMID: 38244594; PMCID: PMC10799730.
Large language models can now generate political messages as persuasive as those written by humans, raising concerns about how far this persuasiveness may continue to increase with model size. Here, we generate 720 persuasive messages on 10 U.S. political issues from 24 language models spanning several orders of magnitude in size. We then deploy these messages in a large-scale randomized survey experiment (N = 25,982) to estimate the persuasive capability of each model. Our findings are twofold. First, we find evidence of a log scaling law: model persuasiveness is characterized by sharply diminishing returns, such that current frontier models are barely more persuasive than models smaller in size by an order of magnitude or more. Second, mere task completion (coherence, staying on topic) appears to account for larger models’ persuasive advantage. These findings suggest that further scaling model size will not much increase the persuasiveness of static LLM-generated messages.
Political campaigns increasingly conduct experiments to learn how to persuade voters. Little research has considered the implications of this trend for elections or democracy. To probe these implications, we analyze a unique archive of 146 advertising experiments conducted by US campaigns in 2018 and 2020 using the platform Swayable. This archive includes 617 advertisements produced by 51 campaigns and tested with over 500,000 respondents. Importantly, we analyze the complete archive, avoiding publication bias. We find small but meaningful variation in the persuasive effects of advertisements. In addition, we find that common theories about what makes advertising persuasive have limited and context-dependent power to predict persuasiveness. These findings indicate that experiments can compound money’s influence in elections: it is difficult to predict ex ante which ads persuade, experiments help campaigns do so, but the gains from these findings principally accrue to campaigns well-financed enough to deploy these ads at scale.
Hewitt, L., Broockman, D., Coppock, A., Tappin, B. M., Slezak, J., Coffman, V., ... & Hamidian, M. (2024). How experiments help campaigns persuade voters: Evidence from a large archive of campaigns’ own experiments. American Political Science Review, 1-19.
According to various sources the world is likely to witness another pandemic on the scale of COVID-19 in the future. How can the social and behavioral sciences contribute to a successful response? Here we conduct a cost-effectiveness analysis of an under-evaluated yet promising tool from modern social and behavioral science: the randomized controlled trial conducted in an online survey environment (“in-survey RCT”). Specifically, we analyze whether, in a pandemic context, a public health campaign that uses an in-survey RCT to pre-test two or more different message interventions — and then selects the top-performing one for their public outreach — has greater impact in expectation than a campaign which does not use this strategy. Our results are threefold. First, in-survey RCT pre-testing is plausibly cost-effective for public health campaigns with typical resources. Second, in-survey RCT pre-testing has potentially powerful returns to scale: for well-resourced campaigns, it looks highly cost-effective. Third, additional evidence for several key parameters could both confirm these patterns and further increase the cost-effectiveness of in-survey RCT pre-testing for public health campaigns. Together our results suggest in-survey RCT pre-testing can plausibly increase the impact of public health campaigns in a pandemic context and identify a research agenda to inform pandemic preparedness.
Elite and mass level politics in many Western democracies is increasingly charac-terised by the expression of negative feelings towards political out-groups. While theexistence of these feelings is well-documented, there is little evidence on the conse-quences of activating political identities during election campaigns. We test whetherfundraising emails containing negative or positive political identity cues lead party sup-porters to donate money via a large pre-registered digital field experiment conductedin collaboration with a British political party. We find that emails containing negativeas opposed to positive identity cues lead to a higher number and frequency of dona-tions. We also find that negative identity cues were only effective when paired with anissue identity rather than a traditional party identity cue, resulting in a 15% increasein the probability of donating over the untreated control. Our results provide novelexperimental evidence on the behavioural effects of activating identities in real-worldpolitical campaigns.
Negative Political Identities and Costly Political Action
Katharina Lawall, Stuart Turnbull-Dugarte, Florian Foos, and Joshua Townsley. Ahead of Print at The Journal of Politics. 2024.
The Polarizing Effect of Partisan Echo Chambers
Authors: Sara B Hobolt, Katharina Lawall, James Tilley
We are witnessing increasing partisan polarization across the world. It is often argued that partisan “echo chambers” are one of the drivers of both policy and affective polarization. In this article, we develop and test the argument that the political homogeneity of people’s social environment shapes polarization. Using an innovative, large-scale pre-registered “lab-in-the-field” experiment in the United Kingdom, we examine how polarization is influenced by partisan group homogeneity. We recruit nationally representative partisans and assign them to discuss a salient policy issue, either with like-minded partisans (an echo chamber) or in a mixed-partisan group. This allows us to examine how group composition affects polarization. In line with our expectations, we find that partisan echo chambers increase both policy and affective polarization compared to mixed discussion groups. This has important implications for our understanding of the drivers of polarization and for how out-group animosity might be ameliorated in the mass public.
Hobolt S. B., Lawall K., Tilley J. The Polarizing Effect of Partisan Echo Chambers. American Political Science Review. Published online 2023:1-16. doi:10.1017/S0003055423001211
During political campaigns, candidates use rhetoric to advance competing visions and assessments of their country. Research reveals that the moral language used in this rhetoric can significantly influence citizens’ political attitudes and behaviors; however, the moral language actually used in the rhetoric of elites during political campaigns remains understudied. Using a data set of every tweet (N=139,412�=139,412) published by 39 US presidential candidates during the 2016 and 2020 primary elections, we extracted moral language and constructed network models illustrating how candidates’ rhetoric is semantically connected. These network models yielded two key discoveries. First, we find that party affiliation clusters can be reconstructed solely based on the moral words used in candidates’ rhetoric. Within each party, popular moral values are expressed in highly similar ways, with Democrats emphasizing careful and just treatment of individuals and Republicans emphasizing in-group loyalty and respect for social hierarchies. Second, we illustrate the ways in which outsider candidates like Donald Trump can separate themselves during primaries by using moral rhetoric that differs from their parties’ common language. Our findings demonstrate the functional use of strategic moral rhetoric in a campaign context and show that unique methods of text network analysis are broadly applicable to the study of campaigns and social movements.
Updating one’s beliefs about the causes and effects of climate change is crucial for altering attitudes and behaviours. Importantly, metacognitive abilities - insight into the (in)correctness of one’s beliefs- play a key role in the formation of polarised beliefs. We here aimed at investigated the role of metacognition in changing beliefs about climate change. To that end, we focused on the role of domain-general and domain-specific metacognition in updating prior beliefs about climate change across the spectrum of climate change scepticism. We also considered the role of how climate science is communicated in the form of textual or visuo-textual presentations. We asked two large US samples to perform a perceptual decision-making task (to assess domain-general decision-making and metacognitive abilities. They next performed a belief-updating task, where they were exposed to good and bad news about climate change and we asked them about their beliefs and their updating. Lastly, they completed a series of questionnaires probing their attitudes to climate change. We show that climate change scepticism is associated with differences in domain-general as well as domain-specific metacognitive abilities. Moreover, domain-general metacognitive sensitivity influenced belief updating in an asymmetric way: lower domain-general metacognition decreased the updating of prior beliefs, especially in the face of negative evidence. Our findings highlight the role of metacognitive failures in revising erroneous beliefs about climate change and point to their adverse social effects.
We review findings and hypotheses at the intersections of life sciences, social sciences and humanities to shed light on how and why people come to experience such emotions in politics and what if any are their behavioural consequences. To answer these questions, we provide insights from predictive coding accounts of interoception and emotion and a proof of concept experiment to highlight the role of visceral states in political behaviour.
On the realness of people who do not exist: the social processing of artificial faces
Today more than ever, we are asked to judge the realness, truthfulness and trustworthiness of our social world. We here focus on how people perceive artificially-generated faces. Generative adversarial networks (GANs) faces are realistic-looking faces of non-existing people, increasingly used in marketing, journalism, social media, and political propaganda. Across three studies, we investigated if and how participants can distinguish between GAN and Real faces and the social consequences of exposure to artificial faces. GAN faces were more likely to be perceived as real than Real faces, a pattern partly explained by certain intrinsic stimuli characteristics. Moreover, participants’ realness judgments influenced their behaviour, displaying increased social conformity towards faces perceived as real, independently of their actual realness. Lastly, knowledge about the existence of GAN faces eroded social trust. Our findings point to the potentially far-reaching consequences of the ubiquitous use of GAN faces in a culture powered by images at unprecedented levels.
Computational and neurocognitive approaches to the political brain: key insights and future avenues for political neuroscience
Although the study of political behaviour has been traditionally restricted to the social sciences, new advances in political neuroscience and computational cognitive science highlight that the biological sciences can offer crucial insights into the roots of ideological thought and action. Echoing the dazzling diversity of human ideologies, this theme issue seeks to reflect the multiplicity of theoretical and methodological approaches to understanding the nature of the political brain. Cutting-edge research along three thematic strands is presented, including (i) computational approaches that zoom in on fine-grained mechanisms underlying political behaviour, (ii) neurocognitive perspectives that harness neuroimaging and psychophysiological techniques to study ideological processes, and (iii) behavioural studies and policy-minded analyses of such understandings across cultures and across ideological domains. Synthesizing these findings together, the issue elucidates core questions regarding the nature of uncertainty in political cognition, the mechanisms of social influence and the cognitive structure of ideological beliefs. This offers key directions for future biologically grounded research as well as a guiding map for citizens, psychologists and policymakers traversing the uneven landscape of modern polarization, misinformation, intolerance and dogmatism.
Angry Politics: How experienced anger shifts political leader choices
Past research has shown that anger is associated with support for confrontational and punitive responses during crises, and with endorsement of authoritarian ideologies. One important question is whether it is the political origin of the feeling of anger that explains the association between anger and authoritarianism or whether any feeling of anger would be associated with changes in political attitudes. Here, we tested the effect of non-politically motivated incidental anger on the preference for strong leaders. In line with past research, we predicted that anger would increase preferences for authoritarian leaders. Across three experiments, we exposed participants to an anger manipulation. Before and after this manipulation, we measured participants’ political leader preferences by asking them to choose between the faces of two leaders they would vote for in a hypothetical election. The level of self-reported anger predicted the probability of choosing more dominant and less trustworthy leaders after the manipulation, suggesting that even non-political incidental anger increases preferences for authoritarian leaders. Importantly, this change was absent when participants had to indicate which individuals were perceived as most successful, documenting the specificity of our results in the context of political leaders.
How should the political animals of the 21st century feel?: Comment on “The sense of should: A biologically-based framework for modelling social pressure” by J.E. Theriault et al.
A commentary on the seminal article by Theriault, Young and Feldman Barrett [1] that puts put forward a wide-ranging model that accounts for a fundamental building block of our sociality, namely the felt sense that we must conform to other people's expectations, what they aptly call ‘the sense of should’.
[1] J.E. Theriault, L. Young, L.F. Barrett (2021) The sense of should: a biologically-based framework for modeling social pressure. Phys Life Rev, 36 (2021), pp. 100-136, 10.1016/j.plrev.2020.01.004