Negative campaigning is a central feature of political competition, yet empirical research has been limited by the high cost and limited scalability of existing classification methods. This study makes two key contributions. First, it introduces zero-shot Large Language Models (LLMs) as a novel approach for cross-lingual classification of negative campaigning. Using benchmark datasets in ten languages, we demonstrate that LLMs achieve performance on par with native-speaking human coders and outperform conventional supervised machine learning approaches. Second, we leverage this novel method to conduct the largest cross-national study of negative campaigning to date, analyzing 18 million tweets posted by parliamentarians in 19 European countries between 2017 and 2022. The results reveal consistent cross-national patterns: governing parties are less likely to use negative messaging, while ideologically extreme and populist parties -- particularly those on the radical right -- engage in significantly higher levels of negativity. These findings advance our understanding of how party-level characteristics shape strategic communication in multiparty systems. More broadly, the study demonstrates the potential of LLMs to enable scalable, transparent, and replicable research in political communication across linguistic and cultural contexts.

Cambridge, United Kingdom – The fate of Ukraine and the future of European security hangs in the balance as United States and Russian diplomats prepared to discuss an accelerated peace plan this week. The uncertainty and dreadful possibilities of this historical moment, with Russia occupying a fifth of Ukrainian soil, dominated the atmosphere of Firewalling the Future, a conference on the future of Ukraine held at Cambridge University on Monday. Organised by programme leader Victoria Vdovychenko and professor of Ukrainian studies Rory Finnin under the auspices of the Centre for Geopolitics, it brought together Ukrainian, European and British diplomats, soldiers and academics. Dominant among the Ukrainians and Eastern Europeans present was the sentiment that with Trump's re-election, the international order is irrecoverably lost and needs to be rebuilt. Some spoke openly of a post-NATO reality in which Europe must form new structures and alliances to fend for itself.

The work covers the development and explainability of machine learning models for predicting political leanings through parliamentary transcriptions. We concentrate on the Slovenian parliament and the heated debate on the European migrant crisis, with transcriptions from 2014 to 2020. We develop both classical machine learning and transformer language models to predict the left- or right-leaning of parliamentarians based on their given speeches on the topic of migrants. With both types of models showing great predictive success, we continue with explaining their decisions. Using explainability techniques, we identify keywords and phrases that have the strongest influence in predicting political leanings on the topic, with left-leaning parliamentarians using concepts such as people and unity and speak about refugees, and right-leaning parliamentarians using concepts such as nationality and focus more on illegal migrants. This research is an example that understanding the reasoning behind predictions can not just be beneficial for AI engineers to improve their models, but it can also be helpful as a tool in the qualitative analysis steps in interdisciplinary research.

Each political group of the European Parliament has submitted several hundred amendments, bringing the total to several thousand. The deluge has come equally from the left and the right – and will now have to be reconciled in a summer of negotiations. One of the most controversial topics is on definitions. Left-of-center parliamentarians are pushing for a broad general definition of artificial intelligence (AI) rather than accepting a narrow list of AI techniques. Their goal is to make the regulation future-proof.

A study has found that most Europeans would like to see some of their members of parliament replaced by algorithms. Researchers at IE University's Center for the Governance of Change asked 2,769 people from 11 countries worldwide how they would feel about reducing the number of national parliamentarians in their country and giving those seats to an AI that would have access to their data. The results, published Thursday, showed that despite AI's clear and obvious limitations, 51% of Europeans said they were in favor of such a move. Oscar Jonsson, academic director at IE University's Center for the Governance of Change and one of the report's main researchers, told CNBC that there's been a "decades long decline of belief in democracy as a form of governance." The reasons are likely linked to increased political polarization, filter bubbles and information splintering, he said.

Microsoft has updated it's facial recognition technology in an attempt to make it less'racist'. It follows a study published in March that criticised the technology for being able to more accurately recognise the gender of people with lighter skin tones. The system was found to perform best on males with lighter skin and worst on females with darker skin. The problem largely comes down to the data being used to train the AI system not containing enough images of people with darker skin tones. Experts from the computing firm say their tweaks have significantly reduced these errors, by up to 20 times for people with darker faces.

Tech giants have made some major strides in advancing facial recognition technology. But a new study, called'Gender Shades,' has found that it may not be working for all users, especially those who aren't white males. A researcher from the MIT Media Lab discovered that popular facial recognition services from Microsoft, IBM and Face vary in accuracy based on gender and race. A researcher from MIT tested popular facial recognition services and found that they experienced more errors when the used was a dark-skinned female. To illustrate this, researcher Joy Buolamwini created a data set using 1,270 photos of parliamentarians from three African nations and three Nordic countries. The faces were selected to represent a broad range of human skin tones, using a labeling system developed by dermatologists, called the Fitzpatrick scale.