Maintaining Journalistic Integrity in the Digital Age: A Comprehensive
NLP Framework for Evaluating Online News Content
- URL: http://arxiv.org/abs/2401.03467v1
- Date: Sun, 7 Jan 2024 12:27:14 GMT
- Title: Maintaining Journalistic Integrity in the Digital Age: A Comprehensive
NLP Framework for Evaluating Online News Content
- Authors: Ljubisa Bojic, Nikola Prodanovic, Agariadne Dwinggo Samala
- Abstract summary: This paper proposes a comprehensive framework to analyze online news texts using natural language processing (NLP) techniques.
The framework incorporates ten journalism standards-objectivity, balance and fairness, readability and clarity, sensationalism and clickbait, ethical considerations, public interest and value, source credibility, relevance and timeliness, factual accuracy, and attribution and transparency.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The rapid growth of online news platforms has led to an increased need for
reliable methods to evaluate the quality and credibility of news articles. This
paper proposes a comprehensive framework to analyze online news texts using
natural language processing (NLP) techniques, particularly a language model
specifically trained for this purpose, alongside other well-established NLP
methods. The framework incorporates ten journalism standards-objectivity,
balance and fairness, readability and clarity, sensationalism and clickbait,
ethical considerations, public interest and value, source credibility,
relevance and timeliness, factual accuracy, and attribution and transparency-to
assess the quality of news articles. By establishing these standards,
researchers, media organizations, and readers can better evaluate and
understand the content they consume and produce. The proposed method has some
limitations, such as potential difficulty in detecting subtle biases and the
need for continuous updating of the language model to keep pace with evolving
language patterns.
Related papers
- A Survey on Automatic Credibility Assessment of Textual Credibility Signals in the Era of Large Language Models [6.538395325419292]
Credibility assessment is fundamentally based on aggregating credibility signals.
Credibility signals provide a more granular, more easily explainable and widely utilizable information.
A growing body of research on automatic credibility assessment and detection of credibility signals can be characterized as highly fragmented and lacking mutual interconnections.
arXiv Detail & Related papers (2024-10-28T17:51:08Z) - Ethio-Fake: Cutting-Edge Approaches to Combat Fake News in Under-Resourced Languages Using Explainable AI [44.21078435758592]
Misinformation can spread quickly due to the ease of creating and disseminating content.
Traditional approaches to fake news detection often rely solely on content-based features.
We propose a comprehensive approach that integrates social context-based features with news content features.
arXiv Detail & Related papers (2024-10-03T15:49:35Z) - Exploring Precision and Recall to assess the quality and diversity of LLMs [82.21278402856079]
We introduce a novel evaluation framework for Large Language Models (LLMs) such as textscLlama-2 and textscMistral.
This approach allows for a nuanced assessment of the quality and diversity of generated text without the need for aligned corpora.
arXiv Detail & Related papers (2024-02-16T13:53:26Z) - Prompt-and-Align: Prompt-Based Social Alignment for Few-Shot Fake News
Detection [50.07850264495737]
"Prompt-and-Align" (P&A) is a novel prompt-based paradigm for few-shot fake news detection.
We show that P&A sets new states-of-the-art for few-shot fake news detection performance by significant margins.
arXiv Detail & Related papers (2023-09-28T13:19:43Z) - fakenewsbr: A Fake News Detection Platform for Brazilian Portuguese [0.6775616141339018]
This paper presents a comprehensive study on detecting fake news in Brazilian Portuguese.
We propose a machine learning-based approach that leverages natural language processing techniques, including TF-IDF and Word2Vec.
We develop a user-friendly web platform, fakenewsbr.com, to facilitate the verification of news articles' veracity.
arXiv Detail & Related papers (2023-09-20T04:10:03Z) - An Inclusive Notion of Text [69.36678873492373]
We argue that clarity on the notion of text is crucial for reproducible and generalizable NLP.
We introduce a two-tier taxonomy of linguistic and non-linguistic elements that are available in textual sources and can be used in NLP modeling.
arXiv Detail & Related papers (2022-11-10T14:26:43Z) - Interpretable Fake News Detection with Topic and Deep Variational Models [2.15242029196761]
We focus on fake news detection using interpretable features and methods.
We have developed a deep probabilistic model that integrates a dense representation of textual news.
Our model achieves comparable performance to state-of-the-art competing models.
arXiv Detail & Related papers (2022-09-04T05:31:00Z) - User Experience Design for Automatic Credibility Assessment of News
Content About COVID-19 [0.33262200259340124]
We present two empirical studies to evaluate the usability of graphical interfaces that offer credibility assessment.
Rating scale, sub-criteria and algorithm authorship are important predictors of the usability.
The authorship of a news text is more important than the authorship of the credibility algorithm used to assess the content quality.
arXiv Detail & Related papers (2022-04-29T08:38:45Z) - TextFlint: Unified Multilingual Robustness Evaluation Toolkit for
Natural Language Processing [73.16475763422446]
We propose a multilingual robustness evaluation platform for NLP tasks (TextFlint)
It incorporates universal text transformation, task-specific transformation, adversarial attack, subpopulation, and their combinations to provide comprehensive robustness analysis.
TextFlint generates complete analytical reports as well as targeted augmented data to address the shortcomings of the model's robustness.
arXiv Detail & Related papers (2021-03-21T17:20:38Z) - InfoBERT: Improving Robustness of Language Models from An Information
Theoretic Perspective [84.78604733927887]
Large-scale language models such as BERT have achieved state-of-the-art performance across a wide range of NLP tasks.
Recent studies show that such BERT-based models are vulnerable facing the threats of textual adversarial attacks.
We propose InfoBERT, a novel learning framework for robust fine-tuning of pre-trained language models.
arXiv Detail & Related papers (2020-10-05T20:49:26Z)
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.