We present an overview of the FIGNEWSshared task, organized as part of the Arabic-NLP 2024 conference co-located with ACL2024. The shared task addresses bias and pro-paganda annotation in multilingual news posts.We focus on the early days of the Israel War onGaza as a case study. The task aims to fostercollaboration in developing annotation guide-lines for subjective tasks by creating frame-works for analyzing diverse narratives high-lighting potential bias and propaganda. In aspirit of fostering and encouraging diversity,we address the problem from a multilingualperspective, namely within five languages: En-glish, French, Arabic, Hebrew, and Hindi. Atotal of 17 teams participated in two annota-tion subtasks: bias (16 teams) and propaganda(6 teams). The teams competed in four evalua-tion tracks: guidelines development, annotationquality, annotation quantity, and consistency.Collectively, the teams produced 129,800 datapoints. Key findings and implications for thefield are discussed.

This paper presents the system developed by the NGU_CNLP team for addressing the shared task on Propaganda Detection in Arabic at WANLP 2022. The team participated in the shared tasks’ two sub-tasks which are: 1) Propaganda technique identification in text and 2) Propaganda technique span identification. In the first sub-task, the goal is to detect all employed propaganda techniques in some given piece of text out of a possible 17 different techniques or to detect that no propaganda technique is being used in that piece of text. As such, this first sub-task is a multi-label classification problem with a pool of 18 possible labels. Subtask 2 extends sub-task 1, by requiring the identification of the exact text span in which a propaganda technique was employed, making it a sequence labeling problem. For task 1, a combination of a data augmentation strategy coupled with an enabled transformer-based model comprised our classification model. This classification model ranked first amongst the 14 systems participating in this subtask. For sub-task two, a transfer learning model was adopted. The system ranked third among the 3 different models that participated in this subtask.

In the past years, toxic comments and offensive speech are polluting the internet and manual inspection of these comments is becoming a tiresome task to manage. Having a machine learning based model that is able to filter offensive Arabic content is of high need nowadays. In this paper, we describe the model that was submitted to the Shared Task on Offensive Language Detection that is organized by (The 4th Workshop on Open-Source Arabic Corpora and Processing Tools). Our model makes use transformer based model (BERT) to detect offensive content. We came in the fourth place in subtask A (detecting Offensive Speech) and in the third place in subtask B (detecting Hate Speech).

Final submission for NileTMRG on RumourEval 2017.

This paper describes two systems that were used by the NileTMRG for addressing Arabic Sentiment Analysis as part of SemEval-2017, task 4. NileTMRG participated in three Arabic related subtasks which are: Subtask A (Message Polarity Classification), Subtask B (Topic-Based Message Polarity classification) and Subtask D (Tweet quantification). For subtask A, we made use of NU’s sentiment analyzer which we augmented with a scored lexicon. For subtasks B and D, we used an ensemble of three different classifiers. The first classifier was a convolutional neural network that used trained (word2vec) word embeddings. The second classifier consisted of a MultiLayer Perceptron while the third classifier was a Logistic regression model that takes the same input as the second classifier. Voting between the three classifiers was used to determine the final outcome. In all three Arabic related tasks in which NileTMRG participated, the team ranked at number one.

This paper presents NileULex, which is an Arabic sentiment lexicon containing close to six thousands Arabic words and compound phrases. Forty five percent of the terms and expressions in the lexicon are Egyptian or colloquial while fifty five percent are Modern Standard Arabic. While the collection of many of the terms included in the lexicon was done automatically, the actual addition of any term was done manually. One of the important criterions for adding terms to the lexicon, was that they be as unambiguous as possible. The result is a lexicon with a much higher quality than any translated variant or automatically constructed one. To demonstrate that a lexicon such as this can directly impact the task of sentiment analysis, a very basic machine learning based sentiment analyser that uses unigrams, bigrams, and lexicon based features was applied on two different Twitter datasets. The obtained results were compared to a baseline system that only uses unigrams and bigrams. The same lexicon based features were also generated using a publicly available translation of a popular sentiment lexicon. The experiments show that usage of the developed lexicon improves the results over both the baseline and the publicly available lexicon.