Ongoing debates among historians of early modern philosophy are concerned with how to best understand the context of historical works and authors. Current methods usually rely on qualitative assessments made by the historians themselves and do not define constraints that can be used to profile a given context in more quantitative terms. In this paper, we present a computational method that can be used to parse a large corpus of works based on their linguistic features, alongside some preliminary information that can be retrieved from the associated metadata. The goal of the method is to use the available information about the corpus to create broad groups that can work as sub-contexts for better understanding different sorts of works and authors. In turn, this makes it possible to better profile each group and identify its most distinguishing linguistic features. Once these features are clarified, it will eventually become possible to also identify what the most representative works and authors in each group are and which of them may be worth exploring in greater detail. This classification method thus allows historians to integrate their qualitative assessments with quantitative studies in order to better define the relevant context for any given work.

Protest event analysis (PEA) is the core method to understand spatial patterns and temporal dynamics of protest. We show how Large Language Models (LLM) can be used to automate the classification of protest events and of political event data more broadly with levels of accuracy comparable to humans, while reducing necessary annotation time by several orders of magnitude. We propose a modular pipeline for the automation of PEA (PAPEA) based on fine-tuned LLMs and provide publicly available models and tools which can be easily adapted and extended. PAPEA enables getting from newspaper articles to PEA datasets with high levels of precision without human intervention. A use case based on a large German news-corpus illustrates the potential of PAPEA.

The transformation in the purposes, instruments, and conditions for the deployment of coercion was a central aspect of the modernization of Western European states during the long nineteenth century. Nowhere is this transformation as evident as in the emergence and diffusion of public, specialized, and professional police forces at the time. In this article, we employ automated text analysis to explore legislative debates on policing in the United Kingdom from 1803 to 1945. We identify three distinct periods in which policing was highly salient in Parliament, each of them related to more general processes driving the modernization of the British state. The first period (1830s–1850s) was marked by the institutionalization of modern police forces and their spread across Great Britain. The second period (1880s–1890s) was dominated by Irish MPs denouncing police abuses against their constituents. The third period (1900s–1940s) was characterized by discussions around working conditions for the police in the context of mounting social pressures and war-related police activities. Whereas the first and third periods have attracted much scholarly interest as they culminated in concrete police reforms, the second period has not been as central to historical research on the British police. We show, however, that policing became a major issue in the legislative agenda of the 1880s and 1890s, as it highlighted the tensions within a modernizing British state, torn between the professionalization of domestic police forces under control of local authorities and the persistence of imperial practices in its colonial territories.

One of the most significant challenges in research related to nutritional epidemiology is the achievement of high accuracy and validity of dietary data to establish an adequate link between dietary exposure and health outcomes. Recently, the emergence of artificial intelligence (AI) in various fields has filled this gap with advanced statistical models and techniques for nutrient and food analysis. We aimed to systematically review available evidence regarding the validity and accuracy of AI-based dietary intake assessment methods (AI-DIA). In accordance with PRISMA guidelines, an exhaustive search of the EMBASE, PubMed, Scopus and Web of Science databases was conducted to identify relevant publications from their inception to 1 December 2024. Thirteen studies that met the inclusion criteria were included in this analysis. Of the studies identified, 61·5 % were conducted in preclinical settings. Likewise, 46·2 % used AI techniques based on deep learning and 15·3 % on machine learning. Correlation coefficients of over 0·7 were reported in six articles concerning the estimation of calories between the AI and traditional assessment methods. Similarly, six studies obtained a correlation above 0·7 for macronutrients. In the case of micronutrients, four studies achieved the correlation mentioned above. A moderate risk of bias was observed in 61·5 % (n 8) of the articles analysed, with confounding bias being the most frequently observed. AI-DIA methods are promising, reliable and valid alternatives for nutrient and food estimations. However, more research comparing different populations is needed, as well as larger sample sizes, to ensure the validity of the experimental designs.

This study examines the impact of temperature on human well-being using approximately 80 million geo-tagged tweets from Argentina spanning 2017–2022. Employing text mining techniques, we derive two quantitative estimators: sentiments and a social media aggression index. The Hedonometer Index measures overall sentiment, distinguishing positive and negative ones, while social media aggressive behavior is assessed through profanity frequency. Non-linear fixed effects panel regressions reveal a notable negative causal association between extreme heat and the overall sentiment index, with a weaker relationship found for extreme cold. Our results highlight that, while heat strongly influences negative sentiments, it has no significant effect on positive ones. Consequently, the overall impact of extremely high temperatures on sentiment is predominantly driven by heightened negative feelings in hot conditions. Moreover, our profanity index exhibits a similar pattern to that observed for negative sentiments.

Machine learning has exhibited substantial success in the field of natural language processing (NLP). For example, large language models have empirically proven to be capable of producing text of high complexity and cohesion. However, at the same time, they are prone to inaccuracies and hallucinations. As these systems are increasingly integrated into real-world applications, ensuring their safety and reliability becomes a primary concern. There are safety critical contexts where such models must be robust to variability or attack and give guarantees over their output. Computer vision had pioneered the use of formal verification of neural networks for such scenarios and developed common verification standards and pipelines, leveraging precise formal reasoning about geometric properties of data manifolds. In contrast, NLP verification methods have only recently appeared in the literature. While presenting sophisticated algorithms in their own right, these papers have not yet crystallised into a common methodology. They are often light on the pragmatical issues of NLP verification, and the area remains fragmented. In this paper, we attempt to distil and evaluate general components of an NLP verification pipeline that emerges from the progress in the field to date. Our contributions are twofold. First, we propose a general methodology to analyse the effect of the embedding gap – a problem that refers to the discrepancy between verification of geometric subspaces, and the semantic meaning of sentences which the geometric subspaces are supposed to represent. We propose a number of practical NLP methods that can help to quantify the effects of the embedding gap. Second, we give a general method for training and verification of neural networks that leverages a more precise geometric estimation of semantic similarity of sentences in the embedding space and helps to overcome the effects of the embedding gap in practice.

Understanding and tracking societal discourse around essential governance challenges of our times is crucial. One possible heuristic is to conceptualize discourse as a network of actors and policy beliefs.

Here, we present an exemplary and widely applicable automated approach to extract discourse networks from large volumes of media data, as a bipartite graph of organizations and beliefs connected by stance edges. Our approach leverages various natural language processing techniques, alongside qualitative content analysis. We combine named entity recognition, named entity linking, supervised text classification informed by close reading, and a novel stance detection procedure based on large language models.

We demonstrate our approach in an empirical application tracing urban sustainable transport discourse networks in the Swiss urban area of Zürich over 12 years, based on more than one million paragraphs extracted from slightly less than two million newspaper articles.

We test the internal validity of our approach. Based on evaluations against manually automated data, we find support for what we call the window validity hypothesis of automated discourse network data gathering. The internal validity of automated discourse network data gathering increases if inferences are combined over sliding time windows.

Our results show that when leveraging data redundancy and stance inertia through windowed aggregation, automated methods can recover basic structure and higher-level structurally descriptive metrics of discourse networks well. Our results also demonstrate the necessity of creating high-quality test sets and close reading and that efforts invested in automation should be carefully considered.

Natural language processing (NLP) has significantly advanced our ability to model and interact with human language through technology. However, these advancements have disproportionately benefited high-resource languages with abundant data for training complex models. Low-resource languages, often spoken by smaller or marginalized communities, need help realizing the full potential of NLP applications. The primary challenges in developing NLP applications for low-resource languages stem from the need for large, well-annotated datasets, standardized tools, and linguistic resources. This scarcity of resources hinders the performance of data-driven approaches that have excelled in high-resource settings. Further, low-resource languages frequently exhibit complex grammatical structures, diverse vocabularies, and unique social contexts, which pose additional challenges for standard NLP techniques. Innovative strategies are emerging to address these challenges. Researchers are actively collecting and curating datasets, even utilizing community engagement platforms to expand data resources. Transfer learning, where models pre-trained on high-resource languages are adapted to low-resource settings, has shown significant promise. Multilingual models like Multilingual Bidirectional Encoder Representations from Transformers (mBERT) and Cross Lingual Models (XLM-R), trained on vast quantities of multilingual data, offer a powerful avenue for cross-lingual knowledge transfer. Additionally, researchers are exploring integrating multimodal approaches, combining textual data with images, audio, or video, to enhance NLP performance in low-resource language scenarios. This survey covers applications like part-of-speech tagging, morphological analysis, sentiment analysis, hate speech detection, dependency parsing, language identification, discourse annotation guidelines, question answering, machine translation, information retrieval, and predictive authoring for augmentative and alternative communication systems. The review also highlights machine learning approaches, deep learning approaches, Transformers, and cross-lingual transfer learning as practical techniques. Developing practical NLP applications for low-resource languages is crucial for preserving linguistic diversity, fostering inclusion within the digital world, and expanding our understanding of human language. While challenges remain, the strategies outlined in this survey demonstrate the ongoing progress and highlight the potential for NLP to empower communities that speak low-resource languages and contribute to a more equitable landscape within language technology.

Public opinion surveys are vital for informing democratic decision-making, but responding to rapidly changing information environments and measuring beliefs within hard-to-reach communities can be challenging for traditional survey methods. This paper introduces a crowdsourced adaptive survey methodology (CSAS) that unites advances in natural language processing and adaptive algorithms to produce surveys that evolve with participant input. The CSAS method converts open-ended text provided by participants into survey items and applies a multi-armed bandit algorithm to determine which questions should be prioritized in the survey. The method’s adaptive nature allows new survey questions to be explored and imposes minimal costs in survey length. Applications in the domains of misinformation, issue salience, and local politics showcase CSAS’s ability to identify topics that might otherwise escape the notice of survey researchers. I conclude by highlighting CSAS’s potential to bridge conceptual gaps between researchers and participants in survey research.

Algorithmic automatic item generation can be used to obtain large quantities of cognitive items in the domains of knowledge and aptitude testing. However, conventional item models used by template-based automatic item generation techniques are not ideal for the creation of items for non-cognitive constructs. Progress in this area has been made recently by employing long short-term memory recurrent neural networks to produce word sequences that syntactically resemble items typically found in personality questionnaires. To date, such items have been produced unconditionally, without the possibility of selectively targeting personality domains. In this article, we offer a brief synopsis on past developments in natural language processing and explain why the automatic generation of construct-specific items has become attainable only due to recent technological progress. We propose that pre-trained causal transformer models can be fine-tuned to achieve this task using implicit parameterization in conjunction with conditional generation. We demonstrate this method in a tutorial-like fashion and finally compare aspects of validity in human- and machine-authored items using empirical data. Our study finds that approximately two-thirds of the automatically generated items show good psychometric properties (factor loadings above .40) and that one-third even have properties equivalent to established and highly curated human-authored items. Our work thus demonstrates the practical use of deep neural networks for non-cognitive automatic item generation.

Word embeddings are now a vital resource for social science research. However, obtaining high-quality training data for non-English languages can be difficult, and fitting embeddings therein may be computationally expensive. In addition, social scientists typically want to make statistical comparisons and do hypothesis tests on embeddings, yet this is nontrivial with current approaches. We provide three new data resources designed to ameliorate the union of these issues: (1) a new version of fastText model embeddings, (2) a multilanguage “a la carte” (ALC) embedding version of the fastText model, and (3) a multilanguage ALC embedding version of the well-known GloVe model. All three are fit to Wikipedia corpora. These materials are aimed at “low-resource” settings where the analysts lack access to large corpora in their language of interest or to the computational resources required to produce high-quality vector representations. We make these resources available for 40 languages, along with a code pipeline for another 117 languages available from Wikipedia corpora. We extensively validate the materials via reconstruction tests and other proofs-of-concept. We also conduct human crowdworker tests for our embeddings for Arabic, French, (traditional Mandarin) Chinese, Japanese, Korean, Russian, and Spanish. Finally, we offer some advice to practitioners using our resources.

Due to their significant role in creative design ideation, databases of causal ontology-based models for biological and technical systems have been developed. However, creating structured database entries through system models using a causal ontology requires the time and effort of experts. Researchers have worked toward developing methods that can automatically generate representations of systems from documents using causal ontologies by leveraging machine learning (ML) techniques. However, these methods use limited, hand-annotated data for building the ML models and have manual touchpoints that are not documented. While opportunities exist to improve the accuracy of these ML models, more importantly, it is required to understand the complete process of generating structured representations using causal ontology. This research proposes a new method and a set of rules to extract information relevant to the constructs of the SAPPhIRE model of causality from descriptions of technical systems in natural language and report the performance of this process. This process aims to understand the information in the context of the entire description. The method starts by identifying the system interactions involving material, energy and information and then builds the causal description of each system interaction using the SAPPhIRE ontology. This method was developed iteratively, verifying the improvements through user trials in every cycle. The user trials of this new method and rules with specialists and novice users of the SAPPhIRE modeling showed that the method helps in accurately and consistently extracting the information relevant to the constructs of the SAPPhIRE model from a given natural language description.

We analyze the disclosures of sustainable investing by Dutch pension funds in their annual reports by introducing a novel textual analysis approach using state-of-the-art natural language processing techniques to measure the awareness and implementation of sustainable investing. We find that a pension fund's size increases both the awareness and implementation of sustainable investing. Moreover, we analyze the role of signing a sustainable investment initiative. Although signing this initiative increases the specificity of pension fund statements about sustainable investing, we do not find an effect on the implementation of sustainable investing.