While media bias is widely studied, the epistemic strategies behind factual reporting remain computationally underexplored. This paper analyzes these strategies through a large-scale comparison of CNN and Fox News. To isolate reporting style from topic selection, we employ an article matching strategy to compare reports on the same events and apply the FactAppeal framework to a corpus of over 470K articles covering two highly politicized periods: the COVID-19 pandemic and the Israel-Hamas war. We find that CNN's reporting contains more factual statements and is more likely to ground them in external sources. The outlets also exhibit sharply divergent sourcing patterns: CNN builds credibility by citing Experts} and Expert Documents, constructing an appeal to formal authority, whereas Fox News favors News Reports and direct quotations. This work quantifies how partisan outlets use systematically different epistemic strategies to construct reality, adding a new dimension to the study of media bias.

This study employs machine learning, historical analysis, and natural language processing (NLP) to examine recurring lethal stampedes at Indias mass religious gatherings, focusing on the 2025 Mahakumbh tragedy in Prayagraj (48+ deaths) and its 1954 predecessor (700+ casualties). Through computational modeling of crowd dynamics and administrative records, it investigates how systemic vulnerabilities contribute to these disasters. Temporal trend analysis identifies persistent choke points, with narrow riverbank access routes linked to 92% of past stampede sites and lethal crowd densities (eight or more persons per square meter) recurring during spiritually significant moments like Mauni Amavasya. NLP analysis of seven decades of inquiry reports reveals cyclical administrative failures, where VIP route prioritization diverted safety resources in both 1954 and 2025, exacerbating fatalities. Statistical modeling demonstrates how ritual urgency overrides risk perception, leading to panic propagation patterns that mirror historical incidents. Findings support the Institutional Amnesia Theory, highlighting how disaster responses remain reactionary rather than preventive. By correlating archival patterns with computational crowd behavior analysis, this study frames stampedes as a collision of infrastructure limitations, socio spiritual urgency, and governance inertia, challenging disaster discourse to address how spiritual economies normalize preventable mortality.

Markets and policymakers around the world hang on the consequential monetary policy decisions made by the Federal Open Market Committee (FOMC). Publicly available textual documentation of their meetings provides insight into members' attitudes about the economy. We use GPT-4 to quantify dissent among members on the topic of inflation. We find that transcripts and minutes reflect the diversity of member views about the macroeconomic outlook in a way that is lost or omitted from the public statements. In fact, diverging opinions that shed light upon the committee's "true" attitudes are almost entirely omitted from the final statements. Hence, we argue that forecasting FOMC sentiment based solely on statements will not sufficiently reflect dissent among the hawks and doves.

The current prevalence of conspiracy theories on the internet is a significant issue, tackled by many computational approaches. However, these approaches fail to recognize the relevance of distinguishing between texts which contain a conspiracy theory and texts which are simply critical and oppose mainstream narratives. Furthermore, little attention is usually paid to the role of inter-group conflict in oppositional narratives. We contribute by proposing a novel topic-agnostic annotation scheme that differentiates between conspiracies and critical texts, and that defines span-level categories of inter-group conflict. We also contribute with the multilingual XAI-DisInfodemics corpus (English and Spanish), which contains a high-quality annotation of Telegram messages related to COVID-19 (5,000 messages per language). We also demonstrate the feasibility of an NLP-based automatization by performing a range of experiments that yield strong baseline solutions. Finally, we perform an analysis which demonstrates that the promotion of intergroup conflict and the presence of violence and anger are key aspects to distinguish between the two types of oppositional narratives, i.e., conspiracy vs. critical.

DOI and link will be added once available. What can we learn from the classics of Finnish literature by using computational emotion analysis? This article tries to answer this question by examining how computational methods of sentiment analysis can be used in the study of literary works in conjunction with a qualitative or more'traditional' approach to literature and affect. We present and develop a simple but robust computational approach of affect analysis that uses a carefully curated emotion lexicon adapted to Finnish turn-of-the-century literary texts combined with word embeddings to map out the semantic emotional spaces of seminal works of Finnish literature. We focus our qualitative analysis on selected case studies: four works by Juhani Aho, Minna Canth, Maria Jotuni and F. E. Sillanpää, but provide emotion arcs for a total of 975 Finnish novels. We argue that a computational analysis of a text's lexicon can be valuable in evaluating the large distribution of the emotional valence in a text and provide guidelines to help other researchers replicate our findings. We show that computational approaches have a place in traditional studies on affect in literature as a support tool for close-reading-based analyses, but also allowing for large-scale comparison between for example, genres or national canons. Introduction The study of literature provides interesting insights for an interdisciplinary study of emotion since literature can be considered a genre in which the affective functions of language are of principal importance (Hogan 2011, 1).

A community needs assessment is a tool used by non-profits and government agencies to quantify the strengths and issues of a community, allowing them to allocate their resources better. Such approaches are transitioning towards leveraging social media conversations to analyze the needs of communities and the assets already present within them. However, manual analysis of exponentially increasing social media conversations is challenging. There is a gap in the present literature in computationally analyzing how community members discuss the strengths and needs of the community. To address this gap, we introduce the task of identifying, extracting, and categorizing community needs and assets from conversational data using sophisticated natural language processing methods. To facilitate this task, we introduce the first dataset about community needs and assets consisting of 3,511 conversations from Reddit, annotated using crowdsourced workers. Using this dataset, we evaluate an utterance-level classification model compared to sentiment classification and a popular large language model (in a zero-shot setting), where we find that our model outperforms both baselines at an F1 score of 94% compared to 49% and 61% respectively. Furthermore, we observe through our study that conversations about needs have negative sentiments and emotions, while conversations about assets focus on location and entities. The dataset is available at https://github.com/towhidabsar/CommunityNeeds.

The paper presents a lifted generalization of decomposition trees used to analyze and effective specify a computation tree for the analysis of lifted graphical models. The paper is exceptionally well-written -- it uses a clean notation and is full of examples to guide the reader's understanding. From a quality perspective, this paper is among the top rank of papers I've reviewed for NIPS. The idea is also novel and seemingly worthy of publication -- first-order decision trees (FODTs) strike me as a useful internal data structure for guiding the application of lifted inference. Yet, on the other hand, it should be pointed out that FODTs don't permit novel lifted computations, they only help one organize them and analyze a notion of width which bounds the computational complexity of evaluation w.r.t. an FODT. In some sense the ability to do this computational analysis would have been implicit in any prior implementation that had to recursively break down a model and apply operations, namely the cited work of Milch et al, but again this paper formalizes the recursive structure and analysis in a way that I'm not aware has been done before.

[See full abstract in the pdf] Formal Thought Disorder (FTD), which is a group of symptoms in cognition that affects language and thought, can be observed through language. FTD is seen across such developmental or psychiatric disorders as Autism Spectrum Disorder (ASD) or Schizophrenia, and its related Schizotypal Personality Disorder (SPD). This paper collected a Japanese audio-report dataset with score labels related to ASD and SPD through a crowd-sourcing service from the general population. We measured language characteristics with the 2nd edition of the Social Responsiveness Scale (SRS2) and the Schizotypal Personality Questionnaire (SPQ), including an odd speech subscale from SPQ to quantify the FTD symptoms. We investigated the following four research questions through machine-learning-based score predictions: (RQ1) How are schizotypal and autistic measures correlated? (RQ2) What is the most suitable task to elicit FTD symptoms? (RQ3) Does the length of speech affect the elicitation of FTD symptoms? (RQ4) Which features are critical for capturing FTD symptoms? We confirmed that an FTD-related subscale, odd speech, was significantly correlated with both the total SPQ and SRS scores, although they themselves were not correlated significantly. Our regression analysis indicated that longer speech about a negative memory elicited more FTD symptoms. The ablation study confirmed the importance of function words and both the abstract and temporal features for FTD-related odd speech estimation. In contrast, content words were effective only in the SRS predictions, and content words were effective only in the SPQ predictions, a result that implies the differences between SPD-like and ASD-like symptoms. Data and programs used in this paper can be found here: https://sites.google.com/view/sagatake/resource.

WikiHow is an open-domain repository of instructional articles for a variety of tasks, which can be revised by users. In this paper, we extract pairwise versions of an instruction before and after a revision was made. Starting from a noisy dataset of revision histories, we specifically extract and analyze edits that involve cases of vagueness in instructions. We further investigate the ability of a neural model to distinguish between two versions of an instruction in our data by adopting a pairwise ranking task from previous work and showing improvements over existing baselines.

Suspense is an important tool in storytelling to keep readers engaged and wanting to read more. However, it has so far not been studied extensively in Computational Literary Studies. In this paper, we focus on one of the elements authors can use to build up suspense: dangerous situations. We introduce a corpus of texts annotated with dangerous situations, distinguishing between 7 types of danger. Additionally, we annotate parts of the text that describe fear experienced by a character, regardless of the actual presence of danger. We present experiments towards the automatic detection of these situations, finding that unsupervised baseline methods can provide valuable signals for the detection, but more complex methods are necessary for further analysis. Not unexpectedly, the description of danger and fear often relies heavily on the context, both local (e.g., situations where danger is only mentioned, but not actually present) and global (e.g., "storm" being used in a literal sense in an adventure novel, but metaphorically in a romance novel).