Academics have traditionally played a vital role in both the generation and dissemination of knowledge, ideas and narratives. Social media, relative to traditional media, provides for new and more direct ways of science communication. Yet, since not all academics may engage with social media, the sample that does so may have an outsize influence on shaping public perceptions of academia more broadly through at least two channels: the set topics they engage with and through the particular style and tone of communication. This paper describes patterns in academics' expression online found in a newly constructed global dataset covering over 100,000 scholars linking their social media content to academic record. We document large and systematic variation in politically salient academic expression concerning climate action, cultural, and economic concepts. We show that these appear to often diverge from general public opinion in both topic focus and style. 

Support for right-wing populist parties is characterised by considerable regional heterogeneity and especially concentrated in regions that have experienced economic decline. It remains unclear, however, whether the spatial externalities of local decline, including homelessness and crime, boost support for populist parties, even among those not directly affected by such decline. In this paper, we contribute to filling this gap in two ways. First, we gather novel data on a particularly visible form of local decline, high-street vacancies, that comprise 83,000 premises in England and Wales. Second, we investigate the influence of local decline on support for the right-wing populist UK Independence Party (UKIP) between 2009 and 2019. We find a significant positive association between high-street vacancy rates and UKIP support. These results enhance our understanding of how changes in the lived environment shape political preferences and behaviour, particularly in relation to right-wing populism. 

For six decades, Bob Dylan has challenged listeners with songs that reward interpretation. Critics and fans have long pored over his words, treating them as literary texts worthy of a slow, devotional reading, line by line, image by image. In 2016, Dylan even won the Nobel Prize in Literature. As the Swedish Academy put it, the prize honoured him for ‘having created new poetic expressions within the great American song tradition’. But what more might we discover if, instead of a human scholar, we asked an artificial intelligence to sift through every word Dylan ever wrote? What patterns, connections or evolution in Dylan’s massive body of lyrics might reveal themselves to a machine’s analysis, and what could that tell us about the man and his music?

Climate change is increasing the frequency and severity of natural disasters worldwide. Media coverage of these events may be vital to generate empathy and mobilize global populations to address the common threat posed by climate change. Using a dataset of 466 news sources from 123 countries, covering 135 million news articles since 2016, we apply an event study framework to measure cross-border media activity following natural disasters. Our results shows that while media attention rises after disasters, it is heavily skewed towards certain events, notably earthquakes, accidents, and wildfires. In contrast, climatologically salient events such as floods, droughts, or extreme temperatures receive less coverage. This cross-border disaster reporting is strongly related to the number of deaths associated with the event, especially when the affected populations share strong social ties or genetic similarities with those in the reporting country. Achieving more balanced media coverage across different types of natural disasters may be essential to counteract skewed perceptions. Further, fostering closer social connections between countries may enhance empathy and mobilize the resources necessary to confront the global threat of climate change.

As social media becomes prominent within academia, we examine its reputational costs for academics. Analyzing Twitter posts from 98,000 scientists (2016–22), we uncover substantial political expression. Online experiments with 6,000 U.S. respondents and 135 journalists, rating synthetic academic profiles with different political affiliations, reveal that politically neutral scientists are seen as the most credible. Strikingly, political expressions result in monotonic penalties: Stronger posts reduce perceived credibility of scientists and their research and audience engagement more, particularly among oppositely aligned respondents. Two surveys with scientists highlight their awareness to penalties, their perceived benefits, and a consensus on limiting political expression outside their expertise.

We analyse the migration of 300,000 academic users from Twitter/X to Bluesky between 2023 and early 2025, combining rich bibliometric data, longitudinal social-media activity, and a novel cross-platform identity-matching pipeline. We show that 18% of scholars in our sample transitioned, with transition rates varying sharply by discipline, political expression, and Twitter engagement but not by traditional academic metrics. Using time-varying Cox models and a matched-pairs design, we isolate genuine peer influence from homophily. We uncover a striking asymmetry whereby information sources drive migration far more powerfully than audience, with this influence decaying exponentially within a week. We further develop an ego-level contagion classifier, revealing that simple contagion drives two-thirds of all exits, shock-driven bursts account for 16%, and complex contagion plays a marginal role. Finally, we show that scholars who rebuild a higher fraction of their former Twitter networks on Bluesky remain significantly more active and engaged. Our findings provide new insights onto theories of network externalities, directional influence, and platform migration, highlighting information sources’ central role in overcoming switching costs.

This paper leverages generative AI to build a network structure over 5,000 product nodes, where directed edges represent input-output relationships in production. We layout a two-step ‘build-prune’ approach using an ensemble of prompt-tuned generative AI classifications. The ’build’ step provides an initial distribution of edge predictions, the ‘prune’ step then re-evaluates all edges. With our AI-generated Production Network (AIPNET) in toe, we document a host of shifts in the network position of products and countries during the 21st century. Finally, we study production network spillovers using the natural experiment presented by the 2017 blockade of Qatar. We find strong evidence of such spill-overs, suggestive of on-shoring of critical production. This descriptive and causal evidence demonstrates some of the many research possibilities opened up by our granular measurement of product linkages, including studies of on-shoring, industrial policy, and other recent shifts in global trade.

<Preliminary>

Medical research remains concentrated in high-income settings, risking misalignment with global health needs. We build a geography-aware knowledge graph linking articles in the 524 leading medical journals to the diseases they study, the countries or territories whose data they analyse, author institutions and funders. We use large-language-model extraction to compare research output with disease burden across 204 countries and 15 major disease groups from 1990 to 2021. Research output has become twice as responsive to domestic disease burden since 1990, yet lower-income regions remain underrepresented in authorship despite serving as frequent research contexts. Maternal-neonatal, nutritional, and many infectious diseases are still under-studied relative to their burden. Philanthropic funders targets neglected burdens, corporations focus on profitable chronic diseases, and governments fall in between. Analyzing WHO disease outbreak news alerts in an event-study design, we show that health shocks trigger rapid, durable increases in both domestic and global research attention, strongest for high-lethality threats. The system is becoming more needs-driven yet remains uneven. Our scalable framework enables near-real-time tracking of convergence.

Using basic health statements authorized by UK and EU registers and ~9,100 journalist-vetted public-health assertions on topics such as abortion, COVID-19 and politics from sources ranging from peer-reviewed journals and government advisories to social media and news across the political spectrum, we benchmark seven leading large language models in 21 languages. We find that, despite high accuracy on English-centric textbook claims, performance falls in multiple non-European languages and fluctuates by topic and source. This highlights the urgency of comprehensive multilingual, domain-aware validation before deploying AI in global health communication. 

This open‑source notebook collection and slides demonstrate two complementary LLM paradigms, retrieval and generation, for turning raw text into structured, research‑ready data.

1. Retrieval notebooks show how to mine large document corpora to extract causal edges, stance labels, demographic attributes and other key fields (e.g., the pipeline powering www.causal.claims).

2. Generation notebooks start from minimal seed prompts and leverage the model’s prior to build production networks, innovation profiles and context‑aware keyword dictionaries (see aipnet.io and www.academicexpression.online).

Across both strands you will find hands‑on modules for prompt engineering, JSON‑schema enforcement, cost‑efficient batch calling, embedding‑based code mapping (HS6 / JEL) and validation routines such as modal voting and cosine sanity checks. By the end, users can scale or adapt each workflow—whether analysing messy policy PDFs or constructing supply‑chain graphs—while keeping costs predictable and outputs auditable.