Why we think this paper is great for you:
This paper directly addresses the "Political Economy" of corporate influence and misinformation, which is crucial for understanding contemporary "Democratic Processes" and challenges to "Social Movements." It offers insights into how economic power shapes public discourse, a key area of your interest.

Abstract
In this paper, we provide a novel measure for greenwashing -- i.e., climate-related misinformation -- that shows how polluting companies can use social media advertising related to climate change to redirect criticism. To do so, we identify greenwashing content in 11 million social-political ads in Meta's Ad Targeting Datset with a measurement technique that combines large language models, human coders, and advances in Bayesian item response theory. We show that what is called greenwashing has diverse actors and components, but we also identify a very pernicious form, which we call political greenwashing, that appears to be promoted by fossil fuel companies and related interest groups. Based on ad targeting data, we show that much of this advertising happens via organizations with undisclosed links to the fossil fuel industry. Furthermore, we show that greenwashing ad content is being micro-targeted at left-leaning communities with fossil fuel assets, though we also find comparatively little evidence of ad targeting aimed at influencing public opinion at the national level.

AI Summary

• A novel, triangulated measurement technique combining large language models (LLMs), human coding, and Bayesian item response theory (IRT) effectively identifies political greenwashing in 11 million social-political ads on Meta platforms. [3]
• The study differentiates between "political greenwashing" (aimed at policy outcomes) and "consumer greenwashing" (aimed at attracting green consumers), emphasizing the former's greater significance for climate change policy. [3]
• Greenwashing: The act of making false or misleading statements about the climate impact of a product or practice, often as a means for companies to maintain or increase their greenhouse gas emissions. [3]
• Consumer Greenwashing: A form of greenwashing aimed at attracting climate-conscious consumers by bolstering a company's climate change bona fides, which is theoretically distinct from political greenwashing's corporate political aims. [3]
• Fossil fuel companies and their undisclosed affiliates engage in a pernicious form of "political greenwashing" by micro-targeting specific left-leaning communities with fossil fuel assets. [2]
• This political greenwashing primarily serves as a micro-level political strategy to protect investments and preempt adverse regulatory decisions, rather than influencing national-level public opinion. [2]
• Social media platforms enable corporations to advertise with plausible deniability through third-party accounts, making the detection of politically motivated greenwashing more challenging but crucial. [2]
• Political Greenwashing: A specific and pernicious form of greenwashing aimed at avoiding adverse regulatory action and undermining costly efforts to transition away from fossil fuels, typically promoted by fossil fuel companies and related interest groups. [2]
• Micro-targeting: The ability of social media advertisers to show ads to very specific subsets of users based on geographic and other demographic criteria. [2]
• The developed Bayesian IRT model effectively incorporates and mitigates the noise and "hallucinations" inherent in LLM outputs, enhancing measurement validity for complex latent constructs like greenwashing. [1]

Why we think this paper is great for you:
This paper offers valuable methodological insights for "Political Science" by reviewing techniques to measure "Political Positions" from textual data. This will be highly relevant for your work on understanding "Political Movements" and public opinion within "Democratic Systems."

Abstract
This article presents the first systematic review of unsupervised and semi-supervised computational text-based ideal point estimation (CT-IPE) algorithms, methods designed to infer latent political positions from textual data. These algorithms are widely used in political science, communication, computational social science, and computer science to estimate ideological preferences from parliamentary speeches, party manifestos, and social media. Over the past two decades, their development has closely followed broader NLP trends -- beginning with word-frequency models and most recently turning to large language models (LLMs). While this trajectory has greatly expanded the methodological toolkit, it has also produced a fragmented field that lacks systematic comparison and clear guidance for applied use. To address this gap, we identified 25 CT-IPE algorithms through a systematic literature review and conducted a manual content analysis of their modeling assumptions and development contexts. To compare them meaningfully, we introduce a conceptual framework that distinguishes how algorithms generate, capture, and aggregate textual variance. On this basis, we identify four methodological families -- word-frequency, topic modeling, word embedding, and LLM-based approaches -- and critically assess their assumptions, interpretability, scalability, and limitations. Our review offers three contributions. First, it provides a structured synthesis of two decades of algorithm development, clarifying how diverse methods relate to one another. Second, it translates these insights into practical guidance for applied researchers, highlighting trade-offs in transparency, technical requirements, and validation strategies that shape algorithm choice. Third, it emphasizes that differences in estimation outcomes across algorithms are themselves informative, underscoring the need for systematic benchmarking.

Why we think this paper is great for you:
You will find this paper highly relevant as it explores the detection of "Political Bias" in news media, a critical aspect of maintaining informed "Democratic Processes." Understanding media influence is essential for studying "Democracy" and "Democratic Systems."

Abstract
Detecting political bias in news media is a complex task that requires interpreting subtle linguistic and contextual cues. Although recent advances in Natural Language Processing (NLP) have enabled automatic bias classification, the extent to which large language models (LLMs) align with human judgment still remains relatively underexplored and not yet well understood. This study aims to present a comparative framework for evaluating the detection of political bias across human annotations and multiple LLMs, including GPT, BERT, RoBERTa, and FLAN. We construct a manually annotated dataset of news articles and assess annotation consistency, bias polarity, and inter-model agreement to quantify divergence between human and model perceptions of bias. Experimental results show that among traditional transformer-based models, RoBERTa achieves the highest alignment with human labels, whereas generative models such as GPT demonstrate the strongest overall agreement with human annotations in a zero-shot setting. Among all transformer-based baselines, our fine-tuned RoBERTa model acquired the highest accuracy and the strongest alignment with human-annotated labels. Our findings highlight systematic differences in how humans and LLMs perceive political slant, underscoring the need for hybrid evaluation frameworks that combine human interpretability with model scalability in automated media bias detection.

Why we think this paper is great for you:
This paper directly relates to "Human Rights" and the principle of "Good Governance" within "Democratic Institutions," particularly concerning vulnerable populations. It provides important insights into the practical application of these principles in legal contexts.

Abstract
In the YS. and M. and S. judgment, the Court of Justice of the European Union ruled on three procedures in which Dutch judges asked for clarification on the right of asylum seekers to have access to the documents regarding the decision on asylum applications. The judgment is relevant for interpreting the concept of personal data and the scope of the right of access under the Data Protection Directive, and the right to good administration in the EU Charter of Fundamental Rights. At first glance, the judgment seems disappointing from the viewpoint of individual rights. Nevertheless, in our view the judgment provides sufficient grounds for effective access rights to the minutes in future asylum cases.

Why we think this paper is great for you:
This paper delves into "Freedom of Expression" and the "Right to be Forgotten," fundamental "Human Rights" that are central to the functioning of "Democratic Systems." It offers a specific case study on how these rights are applied in practice.

Abstract
Since the Google Spain judgment of the Court of Justice of the European Union, Europeans have, under certain conditions, the right to have search results for their name delisted. This paper examines how the Google Spain judgment has been applied in the Netherlands. Since the Google Spain judgment, Dutch courts have decided on two cases regarding delisting requests. In both cases, the Dutch courts considered freedom of expression aspects of delisting more thoroughly than the Court of Justice. However, the effect of the Google Spain judgment on freedom of expression is difficult to assess, as search engine operators decide about most delisting requests without disclosing much about their decisions.

Why we think this paper is great for you:
This paper explores the concept of "Public Goods" through "Game Theory," a foundational topic in "Political Economy" and "Political Theory." It provides a theoretical lens for understanding collective action problems relevant to "Democratic Processes."

Abstract
This paper examines public goods and evaluates the mechanism through the game theory. Public goods are characterized by nonexclusivity and nonrivalry and this creates fundamental challenges for allocation. We analyze why competitive markets undersupply public goods by deriving the inefficiency formally through Nash equilibrium. The paper evaluates theoretical solutions including Lindahl pricing, Clarke-Groves mechanisms, and voting schemes. The paper will cover their efficiency properties and practical limitations. We show how strategic interaction leads to free-riding behavior using roommates dilemma and other examples. We also cover why a large household lives in messy conditions not because individuals are lazy, but because they are rational players in a Nash equilibrium. We also examine voting mechanisms, the median voter theorem, and recent developments in truth-revealing mechanisms.

Why we think this paper is great for you:
This paper contributes to the broader "Social Sciences" by exploring epistemological questions around reality and interpretation, which can inform discussions within "Political Philosophy" about the nature of social knowledge. It offers a unique perspective on how scientific paradigms intersect with social understanding.

Abstract
The text points out that one of the main contradictions of quantum realism, which is particularly relevant to the social sciences, is the tension between the existence of an observer-independent reality and the idea that this reality is mediated by the cognitive and interpretative processes of the subject. This contradiction arises from the central role of the observer in phenomena such as the collapse of the wave function and their influence on the construction of reality, which challenges the classical notion of an objective, non-perceptual nature. Moreover, the encounter between social realism and social interpretivism lies in the fact that, in the social sciences, it is also recognized that human interpretations and actions shape social reality, creating a scenario in which absolute objectivity becomes difficult to sustain. These tensions, therefore enrich the epistemological and ontological debate, showing that the contradictions of quantum realism transcend physics and connect deeply with the difficulties and dynamics of the social construction of reality.