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This paper introduces the methodology of BSC-LANGTECH team for the FIGNEWS 2024 Shared Task on News Media Narratives. Following the bias annotation subtask, we apply the theory and methods of framing analysis to develop guidelines to annotate bias in the corpus provided by the task organizators. The manual annotation of a subset, with which a moderate IAA agreement has been achieved, is further used in Deep Learning techniques to explore automatic annotation and test the reliability of our framework.

ACL 2024

BSC-LANGTECH at FIGNEWS 2024 Shared Task: Exploring Semi-Automatic Bias Annotation using Frame Analysis

guidelines large language models

bias annotation

text classification

workshop paper

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The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024) will take place in Bangkok, Thailand from August 11th to 16th, 2024. More information will be announced soon.

The proliferation of bias and propaganda on social media is an increasingly significant concern, leading to the development of techniques for automatic detection. This article presents a multilingual corpus of 12, 000 Facebook posts fully annotated for bias and propaganda. The corpus was created as part of the FigNews 2024 Shared Task on News Media Narratives for framing the Israeli War on Gaza. It covers various events during the War from October 7, 2023 to January 31, 2024. The corpus comprises 12, 000 posts in five languages (Arabic, Hebrew, English, French, and Hindi), with 2, 400 posts for each language. The annotation process involved 10 graduate students specializing in Law. The Inter-Annotator Agreement (IAA) was used to evaluate the annotations of the corpus, with an average IAA of 80.8% for bias and 70.15% for propaganda annotations. Our team was ranked among the bestperforming teams in both Bias and Propaganda subtasks. The corpus is open-source and available at https://sina.birzeit.edu/fada

Sina at FigNews 2024: Multilingual Datasets Annotated with Bias and Propaganda.

This paper is a part of the FIGNEWS 2024 Datathon Shared Task and it aims to investigate bias and double standards in media coverage of the Gaza-Israel 2023-2024 conflict through a comprehensive analysis of news articles. The methodology integrated both manual labeling as well as the application of a natural language processing (NLP) tool, which is the Facebook/BART-large-MNLI model. The annotation process involved categorizing the dataset based on identified biases, following a set of guidelines in which categories of bias were defined by the team. The findings revealed that most of the media texts provided for analysis included bias against Palestine, whether it was through the use of biased vocabulary or even tone. It was also found that texts written in Hebrew contained the most bias against Palestine. In addition, when comparing annotations done by AAI-1 and AAI-2, the results turned out to be very similar, which might be mainly due to the clear annotation guidelines set by the annotators themselves. Thus, we recommend the use of clear guidelines to facilitate the process of annotation by future researchers.

SQUad at FIGNEWS 2024 Shared Task: Unmasking Bias in Social Media Through Data Analysis and Annotation

This research paper presents an in-depth examination of bias identification in media content related to the Israel-Palestine war. Focusing on the annotation guidelines and process developed by our team of researchers, the document outlines a systematic approach to discerning bias in articles. Through meticulous analysis, key indicators of bias such as emotive language, weasel words, and loaded comparisons are identified and discussed. The paper also explores the delineation between facts and opinions, emphasizing the importance of maintaining objectivity in annotation. Ethical considerations, including the handling of sensitive data and the promotion of multipartiality among annotators, are carefully addressed. The annotation guidelines also include other ethical considerations such as identifying rumors, false information, exercising prudence and selective quotations. The research paper offers insights into the annotation experience, highlighting common mistakes and providing valuable guidelines for future research in bias identification. By providing a comprehensive framework for evaluating bias in media coverage of the Israel-Palestine war, this study contributes to a deeper understanding of the complexities inherent in media discourse surrounding contentious geopolitical issues.

Eagles at FIGNEWS 2024 Shared Task: A Context-informed Prescriptive Approach to Bias Detection in Contentious News Narratives

This article presents the participation of "The Guideline Specialists" in the FIGNEWS 2024 Shared Task, which aims to unravel bias and propaganda in news media narratives surrounding the Gaza-Israel 2023-2024 war. Leveraging innovative annotation methodologies and drawing on a diverse team of annotators, our approach focuses on meticulously annotating news articles using a linguistic approach to uncover the intricate nuances of bias. By incorporating detailed examples and drawing on related work that show how language structure represented in the use of passive voice or the use of nominalization and the choice of vocabulary carry bias, our findings provide valuable insights into the representation of the Gaza-Israel conflict across various languages and cultures. The guideline we developed detected the bias against Gaza, against Israel and others by setting keywords that are based on linguistic background tested by the AntConc concordance tool. The result was an annotation guideline that have a solid base. Through this collaborative effort, we developed a guideline that contributes to fostering a deeper understanding of media narratives during one of the most critical moments in recent history.

The Guidelines Specialists at FIGNEWS 2024 Shared Task: An annotation guideline to Unravel Bias in News Media Narratives Using a Linguistic Approach

This paper outlines the KSAA-CAD shared task, highlighting the Contemporary Arabic Language Dictionary within the scenario of developing a Reverse Dictionary (RD) system and enhancing Word Sense Disambiguation (WSD) capabilities. The first KSAA-RD (Al-Matham et al., 2023) highlighted significant gaps in the domain of RDs, which are designed to retrieve words by their meanings or definitions. This shared task comprises two tasks: RD and WSD. The RD task focuses on identifying word embeddings that most accurately match a given definition, termed a "gloss," in Arabic. Conversely, the WSD task involves determining the specific meaning of a word in context, particularly when the word has multiple meanings. The winning team achieved the highest-ranking score of 0.0644 in RD using Electra embeddings. In this paper, we describe the methods employed by the participating teams and provide insights into the future direction of KSAA-CAD.

KSAA-CAD Shared Task: Contemporary Arabic Dictionary for Reverse Dictionary and Word Sense Disambiguation

We present Team Cher's submission to the ArabicNLP 2024 KSAA-CAD shared task on the reverse dictionary for Arabic---the retrieval of words using definitions as a query. Our approach is based on a multi-task learning framework that jointly learns reverse dictionary, definition generation, and reconstruction tasks. This work explores different tokenization strategies and compares retrieval performance for each embedding architecture. Evaluation using the KSAA-CAD benchmark demonstrates the effectiveness of our multi-task approach and provides insights into the reverse dictionary task for Arabic. It is worth highlighting that we achieve strong performance without using any external resources in addition to the provided training data.

Cher at KSAA-CAD 2024: Compressing Words and Definitions into the Same Space for Arabic Reverse Dictionary

This research paper presents our approach for the KSAA-CAD 2024 competition, focusing on Arabic Reverse Dictionary (RD) task (Alshammari et al., 2024). Leveraging the functionalities of the Arabic Reverse Dictionary, our system allows users to input glosses and retrieve corresponding words. We provide all associated notebooks and developed models on GitHub and Hugging face, respectively. Our task entails working with a dataset comprising dictionary data and word embedding vectors, utilizing three different architectures of contextualized word embeddings: AraELECTRA, AraBERTv2, and camelBERT-MSA. We fine-tune the AraT5v2-base-1024 model for predicting each embedding, considering various hyperparameters for training and validation. Evaluation metrics include ranking accuracy, mean squared error (MSE), and cosine similarity. The results demonstrate the effectiveness of our approach on both development and test datasets, showcasing promising performance across different embedding types.

MISSION at KSAA-CAD 2024: AraT5 with Arabic Reverse Dictionary

Semantic search tasks have grown extremely fast following the advancements in large language models, including the Reverse Dictionary and Word Sense Disambiguation in Arabic. This paper describes our participation in the Contemporary Arabic Dictionary Shared Task. We propose two models that achieved first place in both tasks. We conducted comprehensive experiments on the latest five multilingual sentence transformers and the Arabic BERT model for semantic embedding extraction. We achieved a ranking score of 0.06 for the reverse dictionary task, which is double than last year's winner. We had an accuracy score of 0.268 for the Word Sense Disambiguation task.

ASOS at KSAA-CAD 2024: One Embedding is All You Need for Your Dictionary

The domain of reverse dictionaries (RDs), while advancing in languages like English and Chinese, remains underdeveloped for Arabic. This study attempts to explore a data-driven approach to enhance word retrieval processes in Arabic RDs. The research focuses on the ArabicNLP 2024 Shared Task, named KSAA-CAD, which provides a dictionary dataset of 39,214 word-gloss pairs, each with a corresponding target word embedding. The proposed solution aims to surpass the baseline performance by employing SOTA deep learning models and innovative data expansion techniques. The methodology involves enriching the dataset with contextually relevant examples, training a T5 model to align the words to their glosses in the space, and evaluating the results on the shared task metrics. We find that our model is closely aligned with the baseline performance on bertseg and bertmsa targets, however does not perform well on electra target, suggesting the need for further exploration.

Baleegh at KSAA-CAD 2024: Towards Enhancing Arabic Reverse Dictionaries

We describe the findings of the fifth Nuanced Arabic Dialect Identification Shared Task (NADI 2024). NADI’s objective is to help advance SoTA Arabic NLP by providing guidance, datasets, modeling opportunities, and standardized evaluation conditions that allow researchers to collaboratively compete on prespecified tasks. NADI 2024 targeted both dialect identification cast as a multi-label task (Subtask 1), identification of the Arabic level of dialectness (Subtask 2), and dialect-to-MSA machine translation (Subtask 3). A total of 51 unique teams registered for the shared task, of whom 12 teams have participated (with 76 valid submissions during the test phase). Among these, three teams participated in Subtask 1, three in Subtask 2, and eight in Subtask 3. The winning teams achieved 50.57 F1 on Subtask 1, 0.1403 RMSE for Subtask 2, and 20.44 BLEU in Subtask 3, respectively. Results show that Arabic dialect processing tasks such as dialect identification and machine translation remain challenging. We describe the methods employed by the participating teams and briefly offer an outlook for NADI.

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BSC-LANGTECH at FIGNEWS 2024 Shared Task: Exploring Semi-Automatic Bias Annotation using Frame Analysis

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Sina at FigNews 2024: Multilingual Datasets Annotated with Bias and Propaganda.

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