Researchers from MBZUAI have proposed a new taxonomy of eight temporal frames and studied their persuasive use in news discourse. They created a multilingual dataset by expertly annotating 458 English and German news articles, identifying over 2,000 temporally framed sentences and approximately 3,000 annotations. Their experiments demonstrated that temporal framing is learnable at the sentence level, with supervised models significantly outperforming zero-shot classification approaches. Why it matters: This research provides a valuable dataset and methodology for understanding how time-related language shapes interpretation in news, contributing to advancements in NLP for media analysis and potentially countering disinformation.
MBZUAI Professor Preslav Nakov has developed FRAPPE, an interactive website that analyzes news articles to identify persuasion techniques. FRAPPE helps users understand framing, persuasion, and propaganda at an aggregate level, across different news outlets and countries. Presented at EACL, FRAPPE uses 23 specific techniques categorized into six broader buckets, such as 'attack on reputation' and 'manipulative wording'. Why it matters: The tool addresses the increasing difficulty in discerning factual information from disinformation, providing a means to identify biases in news media from different countries.
A new methodology emulating fact-checker criteria assesses news outlet factuality and bias using LLMs. The approach uses prompts based on fact-checking criteria to elicit and aggregate LLM responses for predictions. Experiments demonstrate improvements over baselines, with error analysis on media popularity and region, and a released dataset/code at https://github.com/mbzuai-nlp/llm-media-profiling.
MBZUAI researchers presented a new approach to video analysis at ICCV in Paris, led by Syed Talal Wasim. The approach builds on still image processing techniques like focal modulation to analyze spatial and temporal information in video separately. It aims to improve temporal aggregation while avoiding the computational complexity of transformers. Why it matters: This research advances video understanding in computer vision by offering a more efficient method for temporal modeling, crucial for applications like activity recognition and video surveillance.
Manling Li from UIUC proposes a new research direction: Event-Centric Multimodal Knowledge Acquisition, which transforms traditional entity-centric single-modal knowledge into event-centric multi-modal knowledge. The approach addresses challenges in understanding multimodal semantic structures using zero-shot cross-modal transfer (CLIP-Event) and long-horizon temporal dynamics through the Event Graph Model. Li's work aims to enable machines to capture complex timelines and relationships, with applications in timeline generation, meeting summarization, and question answering. Why it matters: This research pioneers a new approach to multimodal information extraction, moving from static entity-based understanding to dynamic, event-centric knowledge acquisition, which is essential for advanced AI applications in understanding complex scenarios.