African Twitter users are active shapers of the Palestine-Israel conversation but their contribution remains relatively understudied. Using 132.5K geo-located tweets from 2020 to 2023 and 451-term list of keywords in 33 languages, we identify three patterns in this context: (1) broad participation (Egypt supplies 43% of posts, yet Nigeria, South Africa, Kenya and Ghana contribute more than a third); (2) multilingual predominantly pro-Palestine amplification across Arabic, English, French, Swahili and other tongues, with 8% of tweets left “undetermined” by Twitter’s language detector; and (3) a humanitarian framing that centers civilian harm through hashtags such as #GazaUnderAttack and #PalestenianLivesMatter. We outline design implications for language-agnostic interfaces, low-friction source verification and cross-movement recommendation tools that foreground African epistemologies in global civic-tech systems.

Stance detection continues to be an important task sitting at the intersection of Natural Language Processing (NLP) and Computational Social Science (CSS). In this work, we evaluate how different variations of BERT models perform on the cross-topic form of the task. In particular, we inspect their performance on the second subtask of the shared task StanceNakba 2026, where two topics are included, namely Arab Normalization with Israel and The Presence of Refugees in Arab Countries. We find that the best-performing model was bert-base-arabertv02-twitter, and we further improve its performance by providing context about the topic during the training phase, achieving an F1-score of 0.86 and ranking second among the participating teams.

We present NakbaEcho, a dataset derived from Palestinian testimonies about the 1948 Nakba. The resource is constructed from transcribing over 2,180 hours of recorded interviews gathered through the Palestine Remembered Oral History index and linked to multiple repositories, including the Palestinian Oral History Archive (POHA) and YouTube-hosted interviews. We harmonize interview-level metadata and generate timestamp-aligned transcripts from the original Arabic recordings using an automatic transcription pipeline configured for Palestinian Arabic. The dataset includes speaker-labeled segments and auxiliary annotations designed to support downstream research in Arabic speech processing, natural language processing, digital humanities, and oral-history analysis. NakbaEcho contributes a structured computational resource for studying Palestinian oral testimony while expanding the availability of dialectal Arabic materials for speech, text, and social research.

Hallucination in Large Language Models (LLMs) remains a significant challenge and continues to draw substantial research attention. The problem becomes especially critical when hallucinations arise in sensitive domains, such as religious discourse. To address this gap, we introduce IslamicEval 2025—the first shared task specifically focused on evaluating and detecting hallucinations in Islamic content. The task consists of two subtasks: (1) Hallucination Detection and Correction of quoted verses (Ayahs) from the Holy Quran and quoted Hadiths; and (2) Qur’an and Hadith Question Answering, which assesses retrieval models and LLMs by requiring answers to be retrieved from grounded, authoritative sources. Thirteen teams participated in the final phase of the shared task, employing a range of pipelines and frameworks. Their diverse approaches underscore both the complexity of the task and the importance of effectively managing hallucinations in Islamic discourse.

We present two comparable diachronic corpora of scientific English and German from the Late Modern Period (17th c.–19th c.) annotated with Universal Dependencies. We describe several steps of data pre-processing and evaluate the resulting parsing accuracy showing how our pre-processing steps significantly improve output quality. As a sanity check for the representativity of our data, we conduct a case study comparing previously gained insights on grammatical change in the scientific genre with our data. Our results reflect the often reported trend of English scientific discourse towards heavy noun phrases and a simplification of the sentence structure (Halliday, 1988; Halliday and Martin, 1993; Biber and Gray, 2011; Biber and Gray, 2016). We also show that this trend applies to German scientific discourse as well. The presented corpora are valuable resources suitable for the contrastive analysis of syntactic diachronic change in the scientific genre between 1650 and 1900. The presented pre-processing procedures and their evaluations are applicable to other languages and can be useful for a variety of Natural Language Processing tasks such as syntactic parsing.