Detecting Bias in News Articles Using NLP

Background

The widespread dissemination of misinformation, amplified by bots and trolls, has created a trust crisis in online media. Events such as the 2020 US Presidential Campaign, Brexit, and the Coronavirus pandemic illustrate the dangers of unchecked fake news. This issue affects individuals, organizations, and society at large, with financial misinformation alone causing an estimated $17 billion in damages. Mavin, an impact-driven startup, addresses this challenge by developing AI solutions to detect bias and improve the quality of online discourse.

Objective

The primary goal of this project was to develop Natural Language Processing (NLP) models that evaluate the trustworthiness, quality, and bias in news articles. Specifically, the models aim to identify various types of media bias, such as spin, sensationalism, and opinion statements disguised as facts, while providing actionable insights for readers and organizations.

Approach

To achieve its goals, the team employed a multi-step strategy:

1. Dataset Preparation:

• Selected news datasets and defined relevant bias categories based on research, including AllSides’ 11 media bias types.
• Annotated over 1,000 news articles using Doccano, focusing on key units such as paragraphs or sentences.

2. Model Development:

• Built a multi-label machine learning classifier leveraging state-of-the-art language models like BERT or GPT.
• Designed the classifier to assign probabilities to bias categories (e.g., Spin, Sensationalism).
• Used quality metrics like Krippendorff’s alpha and RMSE to evaluate model performance against human judgment.

3. API and Pipeline Creation:

• Developed an API to return bias labels and weights for any given article.
• Established a retraining pipeline to maintain and improve model accuracy over time.

4. Perplexity Score Implementation:

• Created a perplexity scoring model to evaluate sentence coherence, ensuring logical consistency in news articles.

Results and Impact

The project delivered a comprehensive bias detection tool capable of identifying and quantifying biases in news articles. Key outcomes included:

• Bias Score: An AI-generated bias score highlighting specific bias types like sensationalism or spin, aiding readers in assessing the credibility of news sources.
• Perplexity Score: A measure of article coherence, enhancing the detection of poorly constructed or deceptive content.
• An API and retraining pipeline to ensure the solution remains scalable and adaptive to evolving news trends.

These innovations empower organizations to combat misinformation, improve user engagement, and foster trust in online content.

Future Implications

The findings and tools from this project have far-reaching implications:

• Media Accountability: Encouraging publishers to produce unbiased, high-quality content.
• Policy Development: Informing regulatory frameworks for addressing misinformation.
• Further Research: Expanding bias detection to multilingual datasets and incorporating more nuanced bias categories.Social Media Integrity: Applying these tools to social media platforms to mitigate the spread of misinformation.