Morocco

Emotional tone plays a central role in persuasion, yet its impact on computational assessments of political argument quality in real world election campaign speeches remains understudied. In this work, we investigate whether positive emotional framing correlates with higher perceived convincingness in political arguments. We fine-tune language models on argument quality datasets and test their ability to transfer convincingness predictions to real-world campaign speeches. Using a corpus of U.S. presidential campaign speeches, we analyze emotional polarity in relation to predicted persuasive strength to test whether positively framed arguments are judged more convincing than neutral or negative ones. Our empirical analysis shows that political parties rely heavily on argumentation during their election campaigns. Also, we found the evidence that politicians strategically employ emotional cues within their arguments during these campaign speeches, with positive emotions being more strongly associated with persuasive strength, for example in topics such as USMCA’s Effect on American Jobs and Agriculture, Border Control Policies, Progressive Tax Reforms. At the same time, we find that negative emotions have a weaker yet still non-negligible influence on voter persuasion in topics such as City Crime and Civil Unrest and White Supremacist Violence (Charlottesville Incident).

EACL 2026

Predicting Convincingness in Political Speech: How Emotional Tone Shapes Persuasive Strength

convincingness

argument quality assessment

argument detection

persuasiveness

topic modelling

emotion analysis

argument mining

### Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

Message from the General Chair: 
*It is my great pleasure and honor to welcome you to the 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025), held in beautiful Vienna, Austria, from July 27 to August 1, 2025. ACL2025continues our field’s tradition of excellence in scholarship, innovation, and inclusivity, and I am deeply grateful to the many volunteers who have worked tirelessly to bring this event to life.* 
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ACL 2025

Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

workshop paper

#### *Message from the General Chair, Aline Villavicencio*
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.
For the first time EACL is being hosted in the African continent. This is an important milestone for our community, and we are grateful to our Moroccan hosts for enabling this historic moment by bringing this edition of EACL to Rabat. We are also delighted that the Second Arabic NLP School is co-located with EACL. We hope attendees enjoy this wonderful opportunity to strengthen ties with the Computational Linguistics communities across the African continent. *[Read full message](https://drive.google.com/file/d/14NlmHvwM6fPJuMmOvVh7K0vtQbEyv3SZ/view?usp=sharing)* 

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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. 

This study examines the capability of LLMs to predict emotional ratings of Russian words by comparing their assessments with both native speakers' ratings and expert evaluations. The research utilises two datasets: the ENRuN database containing associative emotional ratings of Russian nouns by native speakers, and RusEmoLex, an expert-compiled lexicon. Various open-source LLMs were evaluated, including international models (Llama-3, Qwen 2.5), Russian-developed models, and Russian-adapted variants, representing three parameter scales. The findings reveal distinct patterns in model performance: Russian-adapted models demonstrated superior alignment with native speakers' ratings, whilst model size was not a decisive factor. Conversely, larger models showed better performance in matching expert assessments, with language adaptation having minimal impact. Emotional or sensitive lexis with strong connotations produce a more substantial human-model gap.

Emotional Lexicons: How Large Language Models Predict Emotional Ratings of Russian Words

Data annotation is essential for supervised natural language processing tasks but remains labor-intensive and expensive. Large language models (LLMs) have emerged as promising alternatives, capable of generating high-quality annotations either autonomously or in collaboration with human annotators. However their use in autonomous annotations is often questioned for their ethical take on subjective matters. This study investigates the effectiveness of LLMs in a autonomous, and hybrid annotation setups in propaganda detection. We evaluate GPT and open-source models on two datasets from different domains, namely, Propaganda Techniques Corpus (PTC) for news articles and the Journalist Media Bias on X (JMBX) for social media. Our results show that LLMs, in general, exhibit high recall but lower precision in detecting propaganda, often over-predicting persuasive content. Multi-annotator setups did not outperform the best models in single-annotator setting although it helped reasoning models boost their performance. Hybrid annotation, combining LLMs and human input, achieved the highest overall accuracy than LLM-only settings. We further analyze misclassifications and found that LLM have higher sensitivity towards certain propaganda techniques like loaded language, name calling, and doubt. Finally, using error typology analysis, we explore the reasoning provided on misclassifications by the LLM. Our result shows that although some studies report LLM outperforming manual annotations and it could prove useful in hybrid annotation, its incorporation in the human annotation pipeline must be implemented with caution.

Council of LLMs: Evaluating Capability of Large Language Models to Annotate Propaganda

This paper presents a domain-specific transformer pipeline for quantifying social atmosphere in hostel reviews, an experiential dimension that travelers consistently prioritize but that existing NLP methods and booking platforms fail to capture. We train a cross-encoder on 4,994 manually annotated reviews and use it to pseudo-label 162,840 additional reviews; these labels are then distilled into a sentence-transformer bi-encoder, producing embeddings where proximity reflects social interaction level rather than generic sentiment. On held-out human-labeled data, the domain-adapted embeddings achieve F1 = 0.826, outperforming generic sentence embeddings (0.671) and zero-shot GPT-4o (0.774), with a 40-fold improvement in intra-class versus inter-class similarity. Aggregating predictions to the property level reveals that hostel socialness follows an approximate exponential distribution, confirming that highly social hostels are rare. This work formalizes socialness as a measurable semantic construct and provides a general template for extracting implicit experiential attributes from text at scale.

Quantifying Social Sentiment in Hostels Using A Domain-Specific Transformer Pipeline

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.

Appraisal Trajectories in Narratives Reveal Distinct Patterns of Emotion Evocation

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.

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