Finding frames with BERT: A transformer-based approach to generic news frame detection

• URL: http://arxiv.org/abs/2409.00272v1
• Date: Fri, 30 Aug 2024 22:05:01 GMT
• Title: Finding frames with BERT: A transformer-based approach to generic news frame detection
• Authors: Vihang Jumle, Mykola Makhortykh, Maryna Sydorova, Victoria Vziatysheva,
• Abstract summary: We introduce a transformer-based approach for generic news frame detection in Anglophone online content. We discuss the composition of the training and test datasets, the model architecture, and the validation of the approach.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Framing is among the most extensively used concepts in the field of communication science. The availability of digital data offers new possibilities for studying how specific aspects of social reality are made more salient in online communication but also raises challenges related to the scaling of framing analysis and its adoption to new research areas (e.g. studying the impact of artificial intelligence-powered systems on representation of societally relevant issues). To address these challenges, we introduce a transformer-based approach for generic news frame detection in Anglophone online content. While doing so, we discuss the composition of the training and test datasets, the model architecture, and the validation of the approach and reflect on the possibilities and limitations of the automated detection of generic news frames.

Related papers

• Customized Information and Domain-centric Knowledge Graph Construction with Large Language Models [0.0]
  We propose a novel approach based on knowledge graphs to provide timely access to structured information. Our framework encompasses a text mining process, which includes information retrieval, keyphrase extraction, semantic network creation, and topic map visualization. We apply our methodology to the domain of automotive electrical systems to demonstrate the approach, which is scalable.
  arXiv  Detail & Related papers  (2024-09-30T07:08:28Z)
• Inference is All You Need: Self Example Retriever for Cross-domain Dialogue State Tracking with ChatGPT [19.93135583452212]
  We propose a novel method that leverages inference and in-context learning with ChatGPT for domain transfer in dialogue state tracking. Experimental results on the MultiWOZ dataset demonstrate competitive performance and promising generalization across domains.
  arXiv  Detail & Related papers  (2024-09-10T06:24:46Z)
• WISDOM: An AI-powered framework for emerging research detection using weak signal analysis and advanced topic modeling [1.8434042562191815]
  We present an automated artificial intelligence-enabled framework, called WISDOM, for detecting emerging research themes. WISDOM detects emerging research themes using advanced topic modeling and weak signal analysis. We assess WISDOM's performance in identifying emerging research as well as their trends, in the field of underwater sensing technologies.
  arXiv  Detail & Related papers  (2024-09-09T18:08:08Z)
• Trustworthy Image Semantic Communication with GenAI: Explainablity, Controllability, and Efficiency [59.15544887307901]
  Image semantic communication (ISC) has garnered significant attention for its potential to achieve high efficiency in visual content transmission. Existing ISC systems based on joint source-channel coding face challenges in interpretability, operability, and compatibility. We propose a novel trustworthy ISC framework that employs Generative Artificial Intelligence (GenAI) for multiple downstream inference tasks.
  arXiv  Detail & Related papers  (2024-08-07T14:32:36Z)
• Detecting misinformation through Framing Theory: the Frame Element-based Model [6.4618518529384765]
  We focus on the nuanced manipulation of narrative frames - an under-explored area within the AI community. We propose an innovative approach leveraging the power of pre-trained Large Language Models and deep neural networks to detect misinformation.
  arXiv  Detail & Related papers  (2024-02-19T21:50:42Z)
• Foundational Models Defining a New Era in Vision: A Survey and Outlook [151.49434496615427]
  Vision systems to see and reason about the compositional nature of visual scenes are fundamental to understanding our world. The models learned to bridge the gap between such modalities coupled with large-scale training data facilitate contextual reasoning, generalization, and prompt capabilities at test time. The output of such models can be modified through human-provided prompts without retraining, e.g., segmenting a particular object by providing a bounding box, having interactive dialogues by asking questions about an image or video scene or manipulating the robot's behavior through language instructions.
  arXiv  Detail & Related papers  (2023-07-25T17:59:18Z)
• Re-mine, Learn and Reason: Exploring the Cross-modal Semantic Correlations for Language-guided HOI detection [57.13665112065285]
  Human-Object Interaction (HOI) detection is a challenging computer vision task. We present a framework that enhances HOI detection by incorporating structured text knowledge.
  arXiv  Detail & Related papers  (2023-07-25T14:20:52Z)
• Multi-modal Misinformation Detection: Approaches, Challenges and Opportunities [5.4482836906033585]
  Social media platforms are evolving from text-based forums into multi-modal environments. Misinformation spreaders have recently targeted contextual connections between the modalities e.g., text and image. We analyze, categorize and identify existing approaches in addition to challenges and shortcomings they face in order to unearth new research opportunities in the field of multi-modal misinformation detection.
  arXiv  Detail & Related papers  (2022-03-25T19:45:33Z)
• TransVG: End-to-End Visual Grounding with Transformers [102.11922622103613]
  We present a transformer-based framework for visual grounding, namely TransVG, to address the task of grounding a language query to an image. We show that the complex fusion modules can be replaced by a simple stack of transformer encoder layers with higher performance.
  arXiv  Detail & Related papers  (2021-04-17T13:35:24Z)
• A Survey of Community Detection Approaches: From Statistical Modeling to Deep Learning [95.27249880156256]
  We develop and present a unified architecture of network community-finding methods. We introduce a new taxonomy that divides the existing methods into two categories, namely probabilistic graphical model and deep learning. We conclude with discussions of the challenges of the field and suggestions of possible directions for future research.
  arXiv  Detail & Related papers  (2021-01-03T02:32:45Z)
• Survey of Network Intrusion Detection Methods from the Perspective of the Knowledge Discovery in Databases Process [63.75363908696257]
  We review the methods that have been applied to network data with the purpose of developing an intrusion detector. We discuss the techniques used for the capture, preparation and transformation of the data, as well as, the data mining and evaluation methods. As a result of this literature review, we investigate some open issues which will need to be considered for further research in the area of network security.
  arXiv  Detail & Related papers  (2020-01-27T11:21:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.