We present our system for Subtask 1 (three-class sentiment classification) of the DialectSentEval 2026 shared task, which evaluates five Arabic dialects using the Multi-Dialect-Sent (MDS-3) hotel-review corpus. Our approach fine-tunes MARBERT with a stratified train/validation split and class-weighted cross-entropy loss. On the official test set, our system achieves an F1 score of 0.8175 and an accuracy of 0.8419, ranking 3rd on the official leaderboard. We identify a substantial dialect mismatch in the dataset: the training set contains no Lebanese reviews, while approximately 20% of the test set is Lebanese. Adding additional Lebanese reviews from OCLAR partially reduces this gap, but does not eliminate it. The error analysis also shows that misclassifications are concentrated in the neutral class and in reviews that express mixed feelings, often with high confidence. These findings highlight the challenges of cross-dialect sentiment classification under unseen-dialect conditions.

This paper presents the PTUK-NLP submission to KnowledgeGraphEval 2026 Subtask 1 (AdaptNER), evaluating cross-domain and cross-dialect nested Arabic Named Entity Recognition (NER). Models trained on Wojood are evaluated in a zero-shot setting on Konooz, spanning ten unseen domains and sixteen Arabic dialects. Our system fine-tunes ArBERT with targeted augmentation for rare entity types and lightweight inference-time post-processing, using Konooz strictly for evaluation and never for training or adaptation. The submitted system achieves 91.07% micro-F1 and 83.11% macro-F1 on Wojood, and 71.84% precision, 66.59% recall, 69.11% micro-F1, and 60.70% macro-F1 on the official Konooz blind test, ranking fourth on the final leaderboard and achieving a micro-F1 score 6.11 points above the organizers’ baseline. A post-submission ensemble of ArBERT and AraBERTv2 does not improve over the best single model. Overall, targeted data-centric modifications provide useful gains for rare entity types, while a standard MSA-pretrained encoder remains competitive under substantial domain and dialect shift.

The proliferation of bias and propaganda onsocial media is an increasingly significant concern,leading to the development of techniquesfor automatic detection. This article presents amultilingual corpus of 12, 000 Facebook postsfully annotated for bias and propaganda. Thecorpus was created as part of the FigNews2024 Shared Task on News Media Narrativesfor framing the Israeli War on Gaza. It coversvarious events during the War from October7, 2023 to January 31, 2024. The corpuscomprises 12, 000 posts in five languages (Arabic,Hebrew, English, French, and Hindi), with2, 400 posts for each language. The annotationprocess involved 10 graduate students specializingin Law. The Inter-Annotator Agreement(IAA) was used to evaluate the annotationsof the corpus, with an average IAA of 80.8%for bias and 70.15% for propaganda annotations.Our team was ranked among the bestperformingteams in both Bias and Propagandasubtasks. The corpus is open-source and availableat https://sina.birzeit.edu/fada

Disinformation involves the dissemination of incomplete, inaccurate, or misleading information; it has the objective, goal, or purpose of deliberately or intentionally lying to others aboutthe truth. The spread of disinformative information on social media has serious implications, and it causes concern among internet users in different aspects. Automatic classification models are required to detect disinformative posts on social media, especially on Twitter. In this article, DistilBERT multilingual model was fine-tuned to classify tweets either as dis-informative or not dis-informative in Subtask 2A of the ArAIEval shared task. The system outperformed the baseline and achieved F1 micro 87% and F1 macro 80%. Our system ranked 11 compared with all participants.