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Article summary:

1. The article discusses the use of natural language processing (NLP) techniques to detect fake news on social media platforms.

2. The author explains the two feature extraction techniques used in building the model: Bag of Words (BOW) and TF-IDF.

3. The article provides a summary of the dataset, data pre-processing steps, exploratory data analysis, NLP techniques applied, and the training and validation process using both Bag of Words and TF-IDF.

Article analysis:

The article titled "NLP — Detecting Fake News On Social Media" discusses the use of natural language processing (NLP) techniques to identify fake news on social media platforms. While the topic is relevant and important, there are several aspects of the article that require critical analysis.

Firstly, the article lacks a clear introduction or background information about the problem of fake news and its impact on society. It jumps straight into discussing feature extraction techniques without providing sufficient context for readers who may not be familiar with the subject matter. This omission limits the accessibility of the article and assumes prior knowledge on the part of the reader.

Additionally, the article does not provide any evidence or examples to support its claim that consumers are creating and sharing misleading information on social media platforms. It states this as a fact without citing any research or studies to back it up. This lack of evidence weakens the credibility of the article's argument.

Furthermore, while the article briefly mentions two feature extraction techniques - Bag of Words (BOW) and TF-IDF - it does not explain how these techniques are specifically used to detect fake news. The article would benefit from providing more detailed explanations and examples to help readers understand how NLP can be applied in this context.

Another issue with the article is its lack of discussion on potential biases in detecting fake news using NLP techniques. NLP models are trained on existing data, which may contain biases present in society. These biases can be inadvertently learned by the model and result in biased predictions. It is important to address this concern and discuss ways to mitigate bias in NLP-based fake news detection systems.

Moreover, while the article mentions future work possibilities such as implementing Word2Vec, LSTM, and neural networks, it does not explore potential limitations or challenges associated with these approaches. It would have been valuable to discuss potential risks or drawbacks of using these techniques for fake news detection.

Additionally, there is a lack of discussion on the ethical implications of using NLP to detect fake news. The article does not address issues such as privacy, censorship, or the potential for misuse of these technologies. It is important to consider these ethical considerations when discussing the use of NLP in sensitive areas such as news verification.

Lastly, the article includes several links to external sources and references, but it does not provide any analysis or critique of these sources. It would have been beneficial to evaluate the credibility and reliability of these sources to ensure that readers are receiving accurate information.

In conclusion, while the article touches on an important topic, it lacks depth and critical analysis. It fails to provide sufficient evidence, explanations, and considerations for potential biases and ethical implications. A more comprehensive and balanced approach would have enhanced the credibility and value of the article.