Morocco

Understanding emotion responses relies on reconstructing how individuals appraise events. While prior work has studied emotion trajectories and inherent correlations with appraisals, it has considered appraisals only in a snapshot analysis. However, because appraisal is a complex, sequential process, we argue that it should be analyzed based on how it unfolds throughout a narrative. In this study, we investigate whether trajectories of appraisals are distinctive for different emotions in five-event stories -- narratives where each of five sentences describes an event. We employ zero-shot prompting with a large language model to predict appraisals on sub-sequences of a narrative. We find that this approach is effective in identifying relevant appraisals in narratives, without prior knowledge of the evoked emotion, enabling a comprehensive analysis of appraisal trajectories. Furthermore, we are the first to quantitatively identify typical patterns of appraisal trajectories that distinguish emotions. For example, a rising trajectory for self-responsibility indicates trust, while a falling trajectory suggests anger.

EACL 2026

Appraisal Trajectories in Narratives Reveal Distinct Patterns of Emotion Evocation

appraisals

data generation

emotions

trajectories

## Welcome to EMNLP 2022!
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workshop paper

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I’m delighted and honoured to welcome you to the 19th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2026), taking place in the beautiful city of Rabat, in Morocco, in March 24-29, 2026. EACL is the flagship European conference of the Association and EACL 2026 proudly continues our field’s tradition of excellence in scholarship, innovation, and inclusivity. I am deeply grateful to the many volunteers whose dedication, generosity, and tireless efforts have made this conference possible.
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Welcome to the 19th Conference of the European Chapter of the Association for Computational Linguistics (EACL). EACL 2026 will be held in Rabat, Morocco, from March 24–29, 2026. 

Given Farsi's speaker base of over 127 million people and the growing availability of digital text, including more than 1.3 million articles on Wikipedia, it is considered a middle-resource language. However, this label quickly crumbles when the situation is examined more closely. We focus on three subjective tasks (Sentiment Analysis, Emotion Analysis, and Toxicity Detection) and identify significant challenges in data availability and quality, despite overall increases in data availability. We review 110 publications on subjective tasks in Farsi and observe a lack of publicly available datasets. Furthermore, existing datasets often lack essential demographic factors, such as age and gender, that are crucial for accurately modeling subjectivity in language. When evaluating prediction models using the few available datasets, the results are highly unstable across both datasets and models. Our findings show that the volume of data alone is insufficient to improve a language's standing in NLP.

Exploring Subjective Tasks in Farsi: A Survey Analysis and Evaluation of Language Model

Digital inclusion increasingly supports adults with intellectual disabilities (ID) to participate online, yet social media posts can be difficult to understand, particularly when they contain strong emotions, slang, or non-standard writing. This paper investigates whether large language models (LLMs) can simplify social media texts to improve cognitive accessibility and preserve emotional meaning. Using an accessibility-oriented prompt based on existing guidance, posts are simplified and emotion preservation is assessed. The results suggest that many simplified posts retain the same emotions, though changes occur, especially when emotions are weakly expressed or ambiguous. Qualitative analysis shows that simplification improves fluency and structure but can also shift perceived emotion through changes to tone, formatting, and other affective cues common in social media text. The research has also revealed that different LLMs produce very different outputs.

Emotion-aware text simplification of user generated content using LLMs

Machine translation (MT) systems perform well on standard benchmarks, yet their ability to preserve emotional meaning in informal user-generated content—particularly for low-resource languages—remains underexplored. We investigate the preservation of emotion intensity in Spanish–Basque tweet translation, focusing on Basque, an under-represented language in MT research. We compile a small, controlled corpus of Spanish reaction tweets and evaluate Basque translations from three publicly available systems through a crowd-based study. While all systems achieve comparable and above mid-range accuracy and fluency, emotion intensity is systematically attenuated in the translations, with greater loss for more emotionally intense inputs. A follow-up on highly emotional tweets shows that LLM prompting reduces emotion loss, yet substantial attenuation remains, highlighting emotion preservation as a persistent challenge in Spanish--Basque MT.

Crowd-Based Evaluation of Emotion Preservation in Spanish--Basque Tweet Machine Translation

Automatic detection of toxic language has the potential to considerably improve engagement with online spaces. Previous work has characterized toxic language detection as a classification problem, often using fine-grained classes for increased explainability. In this position paper, we argue for a particular way of operationalizing categories of toxic language. Our approach focuses on what is expressed or implied, and breaks down implications based on two traits: (i) the core content of what was expressed, and (ii) relevant stakeholders' attitudes towards that content. We argue for an approach, which we call toxic reasoning, where such distinctions are made explicit. We point out the benefits for such an approach, and develop a toxic reasoning schema, which can explain categories of toxic language from diverse sources. We demonstrate this by mapping the classes of existing toxic language datasets to the schema. Toxic reasoning promises to provide improved understanding of implicit toxicity while increasing explainability.

A Position Paper on Toxic Reasoning: Grounding Categories of Toxic Language in Implications and Attitudes

Use cases of sentiment analysis in the humanities often require contextualized, continuous scores. Concept Vector Projections (CVP) offer a recent solution: by modeling sentiment as a direction in embedding space, they produce continuous, multilingual scores that align closely with human judgments. Yet the method’s portability across domains and underlying assumptions remain underexplored. We evaluate CVP across genres, historical periods, languages, and affective dimensions, finding that concept vectors trained on one corpus transfer well to others with minimal performance loss. To understand the patterns of generalization, we further examine the linearity assumption underlying CVP. Our findings suggest that while CVP is a portable approach that effectively captures generalizable patterns, its linearity assumption is approximate, pointing to potential for further development. Code available at: github.com/lauritswl/representation-transfer

Is Sentiment Banana-Shaped? Exploring the Geometry and Portability of Sentiment Concept Vectors

Large language models (LLMs) have demonstrated strong performance on emotion understanding tasks, yet their ability to faithfully generate emotionally aligned text remains less well understood. We propose a semantic evaluation framework that jointly assesses emotion understanding, emotion generation, and internal consistency, using a VAE-based emotion cost matrix that captures graded semantic similarity between emotion categories. Our framework introduces four complementary metrics that disentangle baseline understanding, human-perceived emotion in generated text, generation quality, and model consistency. Experimental results show that while understanding and consistency scores are highly correlated, emotion generation exhibits substantially weaker correlations with these metrics. These findings motivate the development of specialized evaluation protocols that independently measure emotional understanding and generation, enabling more reliable assessments of LLM emotional intelligence.

Disentangling Emotion Understanding and Generation in Large Language Models

In an era of rapid misinformation spread, LLMs have emerged as tools for assessing news credibility at scale. However, the assessments are influenced by social and cultural biases. Studies investigating political bias, compare model credibility ratings with expert credibility ratings. Comparing LLMs to the perceptions of political camps extends this approach to detecting similarities in their biases. We compare LLM-generated credibility and bias ratings of news outlets with expert assessments and stratified political opinions collected through surveys. We analyse three models (Llama 3.3 70B, Mixtral 8x7B, and GPT-OSS 120B) across 47 news outlets from two countries (U.S. and Germany). We found that models demonstrated consistently high alignment with expert ratings, while showing weaker and more variable alignment with public opinions. For US-American news outlets all models showed stronger alignment with center-left perceptions, while for German news outlets the alignment is more diverse.

News Credibility Assessment by LLMs and Humans: Implications for Political Bias

The transition of Large Language Models (LLMs) from exploratory tools to active "silicon subjects" in social science lacks extensive validation of operational validity. This study introduces Conditioned Comment Prediction (CCP), a task in which a model predicts how a user would comment on a given stimulus by comparing generated outputs with authentic digital traces. This framework enables a rigorous evaluation of current LLM capabilities with respect to the simulation of social media user behavior. We evaluated open-weight 8B models (Llama-3.1, Qwen3, Ministral) in English, German, and Luxembourgish language scenarios. By systematically comparing prompting strategies (explicit vs. implicit) and the impact of Supervised Fine-Tuning (SFT), we identify a critical form vs. content decoupling in low-resource settings: while SFT aligns the surface structure of the text output (length and syntax), it degrades semantic grounding. Furthermore, we demonstrate that explicit conditioning (generated biographies) becomes redundant under fine-tuning, as models successfully perform latent inference directly from behavioral histories. Our findings challenge current "naive prompting" paradigms and offer operational guidelines prioritizing authentic behavioral traces over descriptive personas for high-fidelity simulation.

Towards Simulating Social Media Users with LLMs: Evaluating the Operational Validity of Conditioned Comment Prediction

We propose an agentic data augmentation method for Aspect-Based Sentiment Analysis (ABSA) that uses iterative generation and verification to produce high-quality synthetic training examples. To isolate the effect of agentic structure, we also develop a closely matched prompting-based baseline using the same model and instructions. Both methods are evaluated across three ABSA subtasks—Aspect Term Extraction (ATE), Aspect Sentiment Classification (ATSC), and Aspect Sentiment Pair Extraction (ASPE)—four SemEval datasets, and two encoder–decoder models: T5-Base and Tk-Instruct. Our results show that the agentic augmentation outperforms raw prompting in label preservation of the augmented data, especially when the tasks require aspect term generation. In addition, when combined with real data, agentic augmentation provides higher gains, consistently outperforming prompting-based generation. These benefits are most pronounced for T5-Base, while the more heavily pretrained Tk-Instruct exhibits smaller improvements. As a result, augmented data helps T5-Base achieve comparable performance with its counterpart.

Label-Consistent Data Generation for Aspect-Based Sentiment Analysis Using LLM Agents

Antisocial behavior (ASB) on social media encompasses online behaviors that harm individuals, groups, or platform ecosystems, including hate speech, harassment, cyberbullying, trolling, and coordinated abuse. While most prior work has focused on detecting harm after it occurs, a growing body of research on ASB prediction seeks to forecast future harmful outcomes before they materialize, including—but not limited to—hate-speech diffusion, conversational derailment, and user recidivism. However, this emerging field remains fragmented, with limited conceptual grounding and few integrative frameworks. This paper establishes a foundation for ASB prediction by introducing a structured taxonomy spanning temporal, structural, and behavioral dimensions. Drawing on 49 machine learning studies identified through a literature review, we map predictive goals to datasets, modeling choices, and evaluation practices, and identify key challenges, including the lack of standardized benchmarks, the dominance of text-centric representations, and trade-offs between accuracy and interpretability. We conclude by outlining actionable directions toward more robust, generalizable, and responsible ASB prediction systems.

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