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Advances in large language models have raised concerns about their potential use in generating compelling election disinformation at scale. This study presents a two-part investigation into the capabilities of LLMs to automate stages of an election disinformation operation. First, we introduce DisElect, a novel evaluation dataset designed to measure LLM compliance with instructions to generate content for an election disinformation operation in localised UK context, containing 2,200 malicious prompts and 50 benign prompts. Using DisElect, we test 13 LLMs and find that most models broadly comply with these requests; we also find that the few models which refuse malicious prompts also refuse benign election-related prompts, and are more likely to refuse to generate content from a right-wing perspective. Secondly, we conduct a series of experiments ( N  = 2 , 340) to assess the “humanness” of LLMs: the extent to which disinformation operation content generated by an LLM is able to pass as human-written. Our experiments suggest that almost all LLMs tested released since 2022 produce election disinformation operation content indiscernible by human evaluators over 50% of the time. Notably, we observe that multiple models achieve above-human levels of humanness . Taken together, these findings suggest that current LLMs can be used to generate high-quality content for election disinformation operations, even in hyperlocalised scenarios, at far lower costs than traditional methods, and offer researchers and policymakers an empirical benchmark for the measurement and evaluation of these capabilities in current and future models.

Aerial image data are becoming more widely available, and analysis techniques based on supervised learning are advancing their use in a wide variety of remote sensing contexts. However, supervised learning requires training datasets which are not always available or easy to construct with aerial imagery. In this respect, unsupervised machine learning techniques present important advantages. This work presents a novel pipeline to demonstrate how available aerial imagery can be used to better the provision of services related to the built environment, using the case study of road traffic collisions (RTCs) across three cities in the UK. In this paper, we show how aerial imagery can be leveraged to extract latent features of the built environment from the purely visual representation of top-down images. With these latent image features in hand to represent the urban structure, this work then demonstrates how hazardous road segments can be clustered to provide a data-augmented aid for road safety experts to enhance their nuanced understanding of how and where different types of RTCs occur.

Engaging with online social media platforms is an important part of life as a public figure in modern society, enabling connection with broad audiences and providing a platform for spreading ideas. However, public figures are often disproportionate recipients of hate and abuse on these platforms, degrading public discourse. While significant research on abuse received by groups such as politicians and journalists exists, little has been done to understand the differences in the dynamics of abuse across different groups of public figures, systematically and at scale. To address this, we present analysis of a novel dataset of 45.5M tweets targeted at 4602 UK public figures across 3 domains (members of parliament, footballers, journalists), labelled using fine-tuned transformer-based language models. We find that MPs receive more abuse in absolute terms, but that journalists are most likely to receive abuse after controlling for other factors. We show that abuse is unevenly distributed in all groups, with a small number of individuals receiving the majority of abuse, and that for some groups, abuse is more temporally uneven, being driven by specific events, particularly for footballers. We also find that a more prominent online presence and being male are indicative of higher levels of abuse across all 3 domains.

This aim of this article is to explore the potential use of Wikipedia page view data for predicting electoral results. Responding to previous critiques of work using socially generated data to predict elections, which have argued that these predictions take place without any understanding of the mechanism which enables them, we first develop a theoretical model which highlights why people might seek information online at election time, and how this activity might relate to overall electoral outcomes, focussing especially on information seeking incentives related to swing voters and new parties. We test this model on a novel dataset drawn from a variety of countries in the 2009 and 2014 European Parliament elections. We show that while Wikipedia offers little insight into absolute vote outcomes, it does offer good information about changes in overall turnout at elections and about changes in vote share for particular parties. These results are used to enhance existing theories about the drivers of aggregate patterns in online information seeking, by suggesting that voters are cognitive misers who seek information only when considering changing their vote.

The emergence of large stores of transactional data generated by increasing use of digital devices presents a huge opportunity for policymakers to improve their knowledge of the local environment and thus make more informed and better decisions. A research frontier is hence emerging which involves exploring the type of measures that can be drawn from data stores such as mobile phone logs, Internet searches and contributions to social media platforms and the extent to which these measures are accurate reflections of the wider population. This paper contributes to this research frontier, by exploring the extent to which local commuting patterns can be estimated from data drawn from Twitter. It makes three contributions in particular. First, it shows that heuristics applied to geolocated Twitter data offer a good proxy for local commuting patterns; one which outperforms the current best method for estimating these patterns (the radiation model). This finding is of particular significance because we make use of relatively coarse geolocation data (at the city level) and use simple heuristics based on frequency counts. Second, it investigates sources of error in the proxy measure, showing that the model performs better on short trips with higher volumes of commuters; it also looks at demographic biases but finds that, surprisingly, measurements are not significantly affected by the fact that the demographic makeup of Twitter users differs significantly from the population as a whole. Finally, it looks at potential ways of going beyond simple frequency heuristics by incorporating temporal information into models.

This research presents key findings from a project exploring UK doctors’ perspectives on artificial intelligence (AI) in their work. Despite a growing interest in the use of AI in medicine, studies have yet to explore a representative sample of doctors’ perspectives on, and experiences with, making use of different types of AI. Our research seeks to fill this gap by presenting findings from a survey exploring doctors’ perceptions and experiences of using a variety of AI systems in their work. A sample of 929 doctors on the UK medical register participated in a survey between December 2023 and January 2024 which asked a range of questions about their understanding and use of AI systems.Overall, 29% of respondents reported using some form of AI in their practice within the last 12 months, with diagnostic-decision-support (16%) and generative-AI (16%) being the most prevalently used AI systems.We found that the majority of generative-AI users (62%) reported that these systems increase their productivity, and most diagnostic- decision-support users (62%) reported that the systems improve their clinical decision-making. More than half of doctors (52%) were optimistic about the integration of AI in healthcare, rising to 63% for AI users. Only 15% stated that advances in AI make them worried about their job security, with no significant difference between AI and non-AI users. However, there were relatively low reported levels of training, as well as understandings of risks and professional responsibilities, especially among generative-AI users. Just 12% of respondents agreed they have received sufficient training to understand their professional responsibilities when using AI, with this number decreasing to 8% for generative-AI users. We hope this work adds to the evidence base for policy-makers looking to support the integration of AI in healthcare.

User-facing ‘platform safety technology’ encompasses an array of tools offered by social media platforms to help people protect themselves from harm, for example allowing people to report content or block other users. These tools are an increasingly important part of online safety; however, little is known about how users engage with them. We present findings from a nationally representative survey of UK adults examining their experiences with online harms and safety technologies. The results show that online harm is widespread: 67% of respondents report having encountered harmful content online. Among those who are aware of safety tools, over 80% have used at least one, indicating high uptake when knowledge of the tools is present. Awareness of specific tools is varied, with people more aware of ‘post hoc’ safety tools, taken in response to harm exposure (such as reporting or blocking), than preventive measures (such as altering feed algorithms). However, satisfaction with safety technologies is generally low. People who have previously seen online harms are more likely to use safety tools, implying a ‘learning the hard way’ route to engagement. Those higher in digital literacy are also more likely to use some of these tools, raising concerns about the accessibility of these technologies. In addition, women are more likely to engage in particular types of online ‘safety work’. These findings have significant implications for platform designers, regulators, researchers and policymakers seeking to create a safer and more equitable online environment.

Securitisations permit the breaking of rules: but which rules? This article argues that any given security situation could be handled by a variety of different ‘rule breaking’ procedures, and that securitisations themselves, whilst permitting rule breaking in general, do not necessarily specify in advance which rules in particular have to be broken. This begs the question: how do specific threats result in specific rule breaking measures? This article explores this question through reference to ‘control orders’, an unusual legal procedure developed in the UK during the course of the war on terrorism. Once applied to an individual, a control order gives the government a meticulous control over every aspect of their life, up to and including deciding on which educational qualifications they can take. Despite this control, individuals under the regime remain technically ‘free’: and have frequently used this freedom to abscond from the police who are supposed to be watching them. How did a security policy which controls a suspect's educational future, but not their physical movements, develop? This article aims to answer this question, and in so doing present a reevaluation of the mechanisms through which the effects of securitisation manifest themselves.