The DSI hosts, facilitates and promotes research in social and economic data science and AI. Our core staff and network of Affiliates have delivered excellence in data science by combining technical aspects (statistics, machine learning and computer programming) with applications in the social world, encompassing political, economic, social, legal, policy and philosophical questions.
Selected publications by DSI Affiliates and staff
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Milena Tsvetkova, Taha Yasseri, Niccolo Pescetelli & Tobias Werner(2024). A new sociology of humans and machines. Nature Human Behaviour volume 8, pages1864–1876.
Abstract
From fake social media accounts and generative artificial intelligence chatbots to trading algorithms and self-driving vehicles, robots, bots and algorithms are proliferating and permeating our communication channels, social interactions, economic transactions and transportation arteries. Networks of multiple interdependent and interacting humans and intelligent machines constitute complex social systems for which the collective outcomes cannot be deduced from either human or machine behaviour alone. Under this paradigm, we review recent research and identify general dynamics and patterns in situations of competition, coordination, cooperation, contagion and collective decision-making, with context-rich examples from high-frequency trading markets, a social media platform, an open collaboration community and a discussion forum. To ensure more robust and resilient human–machine communities, we require a new sociology of humans and machines. Researchers should study these communities using complex system methods; engineers should explicitly design artificial intelligence for human–machine and machine–machine interactions; and regulators should govern the ecological diversity and social co-development of humans and machines.
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Aurélien Saussay, Misato Sato (2024). The impact of energy prices on industrial investment location: Evidence from global firm level data. Journal of Environmental Economics and Management, Volume 127, ISSN 0095-0696.
Abstract
This study examines the influence of relative energy prices on the geographical distribution of industrial investments across 41 countries. Employing a gravity model framework to analyse firms’ investment location decisions, we estimate the model using global bilateral investment flows derived from firm-level M&A data. Our findings reveal that a 10% increase in the energy price differential between two countries results in a 3.2% rise in cross-border acquisitions. This effect is most pronounced in energy-intensive industries and transactions targeting emerging economies. Furthermore, policy simulations suggest that the impact of unilateral carbon pricing on cross-border investments is modest.
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Dickson, Z. P., & Hobolt, S. B. (2024). Going Against the Grain: Climate Change as a Wedge Issue for the Radical Right.Comparative Political Studies, 0(0).
Abstract
Political parties often mobilise issues that can improve their electoral fortunes by splitting existing coalitions. We argue that by adopting a distinctively adversarial stance, radical right-wing parties have increasingly politicised climate change policies as a wedge issue. This strategy challenges the mainstream party consensus and seeks to mobilise voter concerns over green initiatives. Relying on state-of-the-art multilingual large language models, we empirically examine nearly half a million press releases from 76 political parties across nine European democracies to support this argument. Our findings demonstrate that the radical right’s oppositional climate policy rhetoric diverges significantly from the mainstream consensus. Survey data further reveal climate policy scepticism among voters across the political spectrum, highlighting the mobilising potential of climate policies as a wedge issue. This research advances our understanding of issue competition and the politicisation of climate change.
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Dickson, Z. P., & Yildirim, T. M. (2024). The Effects of COVID-19 Infection on Opposition to COVID-19 Policies: Evidence from the U.S. Congress. Political Communication, 1–24.
Abstract
Elites’ skepticism of scientific consensus presents a formidable challenge in addressing critical issues like climate change and global pandemics. While extensive research has explored the capacity of events related to these challenges to act as “exogenous shocks,” motivating the general public to reassess their risk perceptions, our understanding of how elites similarly respond to such shocks remains limited. In this article, we investigate whether COVID-19 infections influenced US lawmakers’ support for COVID-19 containment measures, focusing on expressed opposition to COVID-19 policies on social media and in press releases throughout the first two years of the pandemic. Employing a staggered difference-in-differences design and matrix completion methods, our analysis reveals that COVID-19 infections caused a reduction of approximately 30% in legislators’ expressions of opposition to COVID-19 policies on social media. These findings underscore that elites are indeed responsive to policy shocks – even in highly polarized contexts – when they are personally affected by an issue.
- Pulkkinen, Undorf, Bender, Wikman-Svahn, Doblas-Reyes, Flynn, Hegerl, Jönsson, Leung, Roussos, Shepherd & Thompson (2022) The value of values in climate science. Nature Climate Change.
https://doi.org/10.1038/s41558-021-01238-9
This article describes how values shape choices at all levels from methodology to communication. The authors call for wider reflection on management of social values in climate science.
- Katzav, Thompson, Risbey, Stainforth, Bradley, and Frisch (2021). On the appropriate and inappropriate uses of probability distributions in climate projections, and some alternatives. Climatic Change 169 (15).
https://doi.org/10.1007/s10584-021-03267-x
This article argues that probability distributions of future climate change do not accurately represent genuine levels of uncertainty. The authors present some considerations for the use of probabilistic representations, and discuss alternatives.
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Andrade Junior, Cardoso-Silva, Bezerra (2021). Comparing Contextual Embeddings for Semantic Textual Similarity in Portuguese. BRACIS 2021: Intelligent Systems, 389-404, Springer.
http://dx.doi.org/10.1007/978-3-030-91699-2_27
This paper uses several deep learning architectures to analyse text written in the Portuguese language. It compares pre-trained deep learning models (transfer learning) and provide examples in which fine-tuning such models have made predictions worse or better.
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De Bacco, Contisciani, Cardoso-Silva, Safdari, Baptista, Sweet, Young, Koster, Ross, McElreath, and Redhead (2021). Latent Network Models to Account for Noisy, Multiply-Reported Social Network Data.
arXiv preprint arXiv:2112.11396. (under review)
This article proposes a new Bayesian statistical model to infer a network of social support from collected data. The paper shows that the model can recover the “ground truth” of social ties much more accurately than common naïve approaches in simulation experiments, and it demonstrates its applicability to two real data sets.
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Costa Avelar, Lamb, Tsoka, Cardoso-Silva (2021). Weekly Bayesian modelling strategy to predict deaths by COVID-19: a model and case study for the state of Santa Catarina, Brazil. (under review)
Preprint: https://arxiv.org/abs/2104.01133
This article proposes an extension to a Bayesian statistical model used to forecast deaths caused by COVID-19. In the paper, new equations are incorporated to take reported cases into account and a strategy to re-calibrate the model every week
- Hendrickx, Arcucci, Amador Díaz López, Guo, Kennedy (2021) Correcting public opinion trends through Bayesian data assimilation. CoRR abs/2105.14276.
This paper aims to merge data from traditional survey polling and Twitter opinion mining techniques using Bayesian data assimilation to arrive at a more accurate estimate of true public opinion for the Brexit referendum.
- Wilson, Guivarch, Kriegler, van Ruijven, van Vuuren, Krey, Schwanitz and Thompson (2021). Evaluating Process-Based Integrated Assessment Models of Climate Change Mitigation, Climatic Change, 166(1), 1-22.
https://doi.org/10.1007/s10584-021-03099-9
This article is the first comprehensive synthesis of research on the evaluation of process-based Integrated Assessment Models (IAMs) of climate change mitigation pathways. The authors propose a systematic evaluation framework to establish the appropriate-ness, interpretability, credibility, and relevance of process-based IAMs as useful scientific tools for informing climate policy.
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Amador Díaz López, Madhyastha (2021), A Focused Analysis of Twitter-based Disinformation from Foreign Influence Operations, Proceedings of the 1st International Workshop on Knowledge Graphs for Online Discourse Αnalysis (KnOD 2021), 30th The Web Conference (WWW 2021)
This paper presents a focused study on disinformation from a foreign influence campaign over Twitter during the 2016 US presidential election. The authors introduce a new dataset of political disinformation related to a foreign influence operation.
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Buizza, Quilodrán Casas, Nadler, Mack, Marrone, Titus, Le Cornec, Heylen, Dur, Baca Ruiz, Heaney, Amador Díaz Lopez, Kumar, Arcucci, Data Learning: Integrating Data Assimilation and Machine Learning, Journal of Computational Science, Volume 58, 2022, 101525, ISSN 1877-7503,
https://doi.org/10.1016/j.jocs.2021.101525
This paper provides an introduction to Data Learning, a field that integrates Data Assimilation and Machine Learning to overcome limitations in applying these fields to real-world data.