Thailand

In this study, we present a novel approach to annotating bias and propaganda in social media data by leveraging topic modeling techniques. Utilizing the BERTopic tool, we performed topic modeling on the FIGNEWS Shared-task dataset, which initially comprised 13,500 samples. From this dataset, we identified 35 distinct topics and selected approximately 50 representative samples from each topic, resulting in a subset of 1,812 samples. These selected samples were meticulously annotated for bias and propaganda labels. Subsequently, we employed multiple methods like KNN, SVC, XGBoost, and RAG to develop a classifier capable of detecting bias and propaganda within social media content. Our approach demonstrates the efficacy of using topic modeling for efficient data subset selection and provides a robust foundation for improving the accuracy of bias and propaganda detection in large-scale social media datasets.

ACL 2024

DRAGON at FIGNEWS 2024 Shared Task: a Dedicated RAG for October 7th conflict News

propaganda

topic modeling

text classification

social media

bias

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workshop paper

News bias is difficult for humans to identify, but even more so for machines. This is largely due to the lack of linguistically appropriate annotated datasets suitable for use by classifier algorithms. The FIGNEWS Subtask 1: Bias Annotation involved classifying bias through manually annotated 1800 headlines from social media. Our proposed guidelines investigated which combinations of keywords available for classification, across sentence and token levels, may be used to detect possible bias in a conflict where neutrality is highly undesirable. Much of the headlines’ percentage required contextual knowledge of events to identify criteria that matched biased or targeted language. The final annotation guidelines paved the way for a theoretical system which uses keyword and hashtag significance to classify major instances of bias. Minor instances with bias undertones or clickbait may require advanced machine learning methods which learn context through scraping user engagements on social media.

LexiconLadies at FIGNEWS 2024 Shared Task: Identifying Keywords for Bias Annotation Guidelines of Facebook News Headlines on the Israel-Palestine 2023 War

This paper outlines the University of Tripoli's initiative in creating annotation guidelines to detect bias in news articles concerning the Palestinian-Israeli conflict. Our team participated in the Framing of Israeli Gaza News Media Narrative (FIGNEWS 2024) shared task. We developed annotation guidelines to label bias in news articles. Using those guidelines we managed to annotate 3,900 articles with the aid of our custom-developed annotation tool. Among 16 participating teams, we scored 48.7 on the macro F1 measure in the quality track in which we ranked 4th. In the centrality track we were ranked at the 6th position using the macro F1 avg measure, however, we achieved the 4th best kappa coefficient. Our bias annotation guidelines was ranked in the 9th position.

Uot1 at FIGNEWS 2024 Shared Task: Labeling News Bias

This paper details our participation in the FIGNEWS-2024 shared task on bias and propaganda annotation in Gaza conflict news. Our objectives were to develop robust guidelines and annotate a substantial dataset to enhance bias detection. We iteratively refined our guidelines and used examples for clarity. Key findings include the challenges in achieving high inter-annotator agreement and the importance of annotator awareness of their own biases. We also explored the integration of ChatGPT as an annotator to support consistency. This paper contributes to the field by providing detailed annotation guidelines, and offering insights into the subjectivity of bias annotation.

Bias Bluff Busters at FIGNEWS 2024 Shared Task: Developing Guidelines to Make Bias Conscious

In this paper, we present our approach for FIGNEWS Subtask 1, which focuses on detecting bias in news media narratives about the Israel war on Gaza. We used a Large Language Model (LLM) and prompt engineering, using GPT-3.5 Turbo API, to create a model that automatically flags biased news media content with 99% accuracy. This approach provides Natural Language Processing (NLP) researchers with a robust and effective solution for automating bias detection in news media narratives using supervised learning algorithms. Additionally, this paper provides a detailed analysis of the labeled content, offering valuable insights into media bias in conflict reporting. Our work advances automated content analysis and enhances understanding of media bias.

Ceasefire at FIGNEWS 2024 Shared Task: Automated Detection and Annotation of Media Bias Using Large Language Models

In today's digital age, the spread of propaganda through news channels has become a pressing concern. To address this issue, the research community has organized a shared task on detecting propaganda in news posts. This paper aims to present the work carried out at the University of Tripoli for the development and implementation of data annotation guidelines by a team of five annotators. The guidelines were used to annotate 2600 news articles. Each article is labeled as “propaganda”, “Not propaganda”, “Not Applicable”, or “Not clear”. The shared task results put our efforts in the third position among 6 participating teams in the consistency track.

Sahara Pioneers at FIGNEWS 2024 Shared Task: Data Annotation Guidelines for Propaganda Detection in News Items

In this study, we aimed to identify biased language in a dataset provided by the FIGNEWS 2024 committee on the Gaza-Israel war. We classified entries into seven categories: Unbiased, Biased against Palestine, Biased against Israel, Biased against Others, Biased against both Palestine and Israel, Unclear, and Not Applicable. Our team reviewed the literature to develop a codebook of terminologies and definitions. By coding each example, we sought to detect language tendencies used by media outlets when reporting on the same event. The primary finding was that most examples were classified as "Biased against Palestine," as all examined language data used one-sided terms to describe the October 7 event. The least used category was "Not Applicable," reserved for irrelevant examples or those lacking context. It is recommended to use neutral and balanced language when reporting volatile political news.

BiasGanda at FIGNEWS 2024 Shared Task: A Quest to Uncover Biased Views in News Coverage

This paper presents The_CyberEquity_Lab team’s participation in the FIGNEWS 2024 Shared Task (Zaghouani, et al., 2024). The task is to annotate a corpus of Facebook posts into bias and propaganda in covering the Gaza-Israel war. The posts represent news articles written in five different languages. The paper presents the guidelines of annotation that the team has adhered in identifying both bias and propaganda in coverage of this continuous conflict.

The CyberEquity Lab at FIGNEWS 2024 Shared Task: Annotating a Corpus of Facebook Posts to Label Bias and Propaganda in Gaza-Israel War Coverage in Five Languages

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.

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

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.

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