In this article, the author presents a text classification model built using a "true and false news" dataset and a Naive Bayes classifier. The model is designed to quickly assess the authenticity of an article based on its content.
" I haven't waited for the truth to be ready. Lies have already run through most of the world."
— Winston Churchill
Since the 2016 U.S. presidential election, "fake news" has become a major topic in politics. Many political figures claimed that misinformation significantly influenced the election results. However, researchers from Stanford and New York Universities expressed skepticism about these claims. Regardless of the debate, it's undeniable that fake news spreads rapidly through social media platforms like Facebook.
What is fake news?
Fake news refers to information that is intentionally misleading. However, with the rise of social media and evolving language use, the definition has expanded. Today, some people label factual information as fake if it contradicts their beliefs. A notable example is former President Donald Trump, who frequently used the term "fake news" to dismiss unfavorable reports. This vague definition makes it easy to misuse and manipulate.
The data science community has responded to this challenge by developing tools to detect and combat fake news. Competitions like the "Fake News Challenge" have emerged, and platforms such as Facebook are using AI to filter out false content. In fact, identifying fake news is essentially a text classification problem, and the solution is straightforward: build a model that can distinguish between true and false news.
This is exactly what I set out to do. I gathered a dataset containing both real and fake news articles. To develop a model capable of determining the authenticity of each article, I employed a Naive Bayes classifier.
Data Collection
My training dataset consists of "real news" and "false news." Collecting fake news was relatively straightforward, as Kaggle provided a dataset of 13,000 articles from the 2016 election. For real news, I turned to AllSides, a website known for publishing politically balanced news and commentary. This site offers reliable content across various topics and political spectrums. I collected 5,279 real news articles from reputable sources like the New York Times, Wall Street Journal, and National Public Radio, published between 2015 and 2016.
The final dataset contains 10,558 news articles, including titles, full content, and labels indicating whether they are true or false. You can access the complete data on GitHub.
Goals and Expectations
Initially, I knew that achieving perfect accuracy would be challenging. My goal was to build a classifier that could distinguish between real and fake news, while also gaining insights from the process. At first, I thought the task was similar to spam detection.
However, models based on countvectorizer or tfidfmatrix often overlook important factors like word order and text structure. Two articles with the same number of words may convey entirely different meanings. I didn’t expect my model to handle overlapping content effectively, but I hoped to gain valuable experience from the process.
Modeling
For this text classification task, I used a Naive Bayes classifier. The key steps involved converting the text into numerical features (using countvectorizer or tfidfvectorizer) and selecting the type of text (title or full article). This led to four different configurations.
I optimized the parameters using grid search in Scikit-learn. After testing, I found that the countvectorizer with full-text training performed best. The optimal settings included non-lowercase formatting and bi-grams, with a minimum word frequency of three.
Surprisingly, the model achieved a cross-validation accuracy of 91.7%, a recall of 92.6%, and an AUC of 95%. These results were better than I had anticipated.
The ROC curve shows a threshold where FPR is around 0.08 and TPR is around 0.90, offering a balanced trade-off between false positives and true positives.
Results and Summary
While the scores are impressive, the real test comes when applying the model to unseen data. On the remaining 5,234 fake news articles, the model correctly identified 88.2% of them, which is slightly lower than the cross-validation score but still strong.
I initially believed that classifying news would be difficult, but the results proved me wrong. Although the model performs well, it’s clear that the task is complex and requires more advanced techniques.
To better understand the model, I analyzed the most common words in both real and fake news. Using a technique inspired by Kevin Markham, I calculated the ratio of word frequencies between the two categories. The results were intriguing: the top "fake" words included internet slang and nonsensical terms, while the top "real" words were mostly political names and high-frequency terms.
However, this approach has limitations. Words that appear more frequently in fake news don’t necessarily make an article false. Subjectivity also plays a role, as the selection of real and fake news was somewhat subjective.
In conclusion, while a basic Naive Bayes model can offer useful insights, more advanced methods like deep learning are needed to effectively combat fake news. This case highlights the importance of data understanding and sensitivity in data science. The line between true and false news is often blurred, and sometimes, human intuition and domain knowledge matter more than the model itself.
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