Sentiment analysis is a fundamental problem in Natural Language Processing (NLP). Standard sentiment classification for the Arabic language remains challenging due to the high volume of dialectal Arabic. To advance research in this area, this paper proposes the Shared Task on Sentiment Analysis and Swapping in Arabic (AraSentEval), organized as part of the OSACT7 Workshop at LREC 2026. This shared task consists of two subtasks: Subtask 1 focuses on multi-class and multi-dialect sentiment analysis, requiring models to identify sentiment polarity across various Arabic dialects. Subtask 2 introduces a generative task for Arabic sentiment swap, challenging models to invert sentiment polarity while preserving core semantics. In this overview paper, we present the motivation, dataset creation, and summarize the main findings from participating models.

The proliferation of social media platforms has significantly reshaped how conflicts are documented, generating large-scale visual records that must be structured to enable meaningful analysis. In this paper, we present the NakbaArchiveClassifier shared task, which focuses on binary classification of infrastructure damage in images from Gaza. This task formed part of the Nakba-NLP Workshop at LREC 2026 and is grounded in an ongoing initiative focused on humanitarian archiving. It utilizes a carefully curated dataset of 2,001 images sourced from Palestinian journalists and content creators on Instagram, spanning the period from October 7, 2023 to December 15, 2025. The objective for participants was to classify whether an image depicts damaged or destroyed infrastructure versus intact structures. This task poses multiple challenges, such as the complexity of real-world conflict imagery, imbalance between classes, and the inherent ambiguity present in many visual scenes. The NakbaArchiveClassifier shared task introduces a new benchmark for analyzing conflict-related visual data and provides valuable resources for advancing research in humanitarian AI, crisis analytics, and Arabic digital humanities.

Social media virality significantly shapes public discourse during geopolitical conflicts, where emotionally charged and multimodal content can rapidly gain widespread attention. However, most prior approaches rely on retrospective engagement signals, limiting their usefulness for early prediction. Multimodal virality modeling in high-stakes Arabic discourse remains largely unexplored. We introduce NakbaVirality, a shared task on multimodal virality classification in conflict-related social media posts, organized as part of the Nakba-NLP workshop at LREC 2026. The dataset consists of 2,600 anonymized posts from X and Reddit collected after October 7, 2023, each including text, an associated image, and normalized engagement labels. Participants must classify posts into low, medium, or high virality categories using only textual and visual inputs. The task provides standardized splits, baseline systems, and evaluation using macro-F1 and accuracy. NakbaVirality establishes the first benchmark for multimodal virality prediction in Arabic high-stakes discourse and promotes research on contextual and multimodal modeling for early impact prediction. The shared task attracted 18 participants, who contributed a total of 4 official test phase submissions.

Authorship style transfer aims to rewrite a given text so that it reflects the distinctive style of a target author while preserving the original meaning. Despite growing interest in text style transfer, most existing work has focused on English and other high-resource languages, with limited attention to languages written in the Arabic script. In this paper, we present an overview of AbjadStyleTransfer, a shared task organised as part of the AbjadNLP workshop at EACL 2026, which targets authorship style transfer for Arabic-script languages with a strong focus on literary text. The shared task covers Modern Standard Arabic and Urdu, and is designed to encourage research on controllable text generation in morphologically rich and stylistically diverse languages. Participants are required to generate text that conforms to the writing style of a specified author, given a semantically equivalent formal input. We describe the task motivation, dataset construction, evaluation protocol, and participation statistics, and provide an initial discussion of the challenges associated with authorship style transfer in Arabic-script languages. AbjadStyleTransfer establishes a new benchmark for literary style transfer beyond Latin-script settings and supports future research on culturally grounded and linguistically informed text generation.

Authorship identification is a core problem in Natural Language Processing and computational linguistics, with applications spanning digital humanities, literary analysis, and forensic linguistics. While substantial progress has been made for English and other high-resource languages, authorship attribution for languages written in the Arabic (Abjad) script remains underexplored. In this paper, we present an overview of AbjadAuthorID, a shared task organised as part of the AbjadNLP workshop at EACL 2026, which focuses on multiclass authorship identification across Arabic-script languages. The shared task covers Modern Standard Arabic, Urdu, and Kurdish, and is formulated as a closed-set multiclass classification problem over literary text spanning multiple authors and historical periods. We describe the task motivation, dataset construction, evaluation protocol, and participation statistics, and report official results for the Arabic track. The findings highlight both the effectiveness of current approaches in controlled settings and the challenges posed by lower participation and resource availability in some language tracks. AbjadAuthorID establishes a new benchmark for multilingual authorship attribution in morphologically rich, underrepresented languages.

We present the findings of the AbjadGenEval shared task, organized as part of the AbjadNLP workshop at EACL 2026, which benchmarks AI-generated text detection for Arabic-script languages. Extending beyond Arabic to include Urdu, the task serves as a binary classification platform distinguishing human-written from AI-generated news articles produced by varied LLMs (e.g., GPT, Gemini). Twenty teams par- ticipated, with top systems achieving F1 scores of 0.93 for Arabic and 0.89 for Urdu. The re- sults highlight the dominance of multilingual transformers-specifically XLM-RoBERTa and DeBERTa-v3-and reveal significant challenges in cross-domain generalization, where naive data augmentation often yielded diminishing returns. This shared task establishes a robust baseline for authenticating content in the Abjad ecosystem.

We present an overview of the AraGenEval shared task, organized as part of the ArabicNLP 2025 conference. This task introduced the first benchmark suite for Arabic authorship analysis, featuring three subtasks: Authorship Style Transfer, Authorship Identification, and AI-Generated Text Detection. We curated high-quality datasets, including over 47,000 paragraphs from 21 authors and a balanced corpus of human- and AI-generated texts. The task attracted significant global participation, with 72 registered teams from 16 countries. The results highlight the effectiveness of transformer-based models, with top systems leveraging prompt engineering for style transfer, model ensembling for authorship identification, and a mix of multilingual and Arabic-specific models for AI text detection. This paper details the task design, datasets, participant systems, and key findings, establishing a foundation for future research in Arabic stylistics and trustworthy NLP.