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Article summary:

1. Fake news is a growing challenge in today's era, consisting of intentional lies or hoaxes spread through conventional news media or online social media.

2. The propagation and distribution of fake news present significant risks, including from a national security standpoint.

3. It is necessary to build a system using natural language processing to automatically detect fake news and reduce its adverse effects.

Article analysis:

The article titled "Natural Language Processing based Online Fake News Detection Challenges – A Detailed Review" aims to discuss the challenges and strategies in detecting fake news using natural language processing techniques. While the topic is relevant and important, there are several aspects of the article that require critical analysis.

Firstly, the article starts by stating that fake news consists of intentional lies or hoaxes spread through conventional news media or online social media. However, it fails to provide any evidence or examples to support this claim. Without supporting evidence, it becomes difficult to assess the validity of this statement and understand the extent of the problem.

Furthermore, the article mentions that fake news is written and distributed to mislead individuals or organizations financially or politically. Again, no evidence is provided to support this claim. It is crucial for an article discussing such a serious issue to present concrete examples or studies that demonstrate the intentions behind creating and spreading fake news.

Additionally, the article highlights the risks associated with the propagation and distribution of false news from a national security standpoint. However, it does not elaborate on these risks or provide any specific examples. This lack of information makes it challenging for readers to fully comprehend the potential consequences of fake news on national security.

Moreover, while discussing strategies for detecting fake news, the article mentions various approaches without providing sufficient details or evidence for their effectiveness. It would have been beneficial if the authors had included studies or experiments that validate these strategies and their success rates in identifying fake news accurately.

Another aspect worth considering is whether there are any biases present in the article. Since no specific sources are mentioned, it is difficult to determine if there are any biases in terms of data selection or research methodologies used. Additionally, there may be a bias towards promoting natural language processing techniques as effective solutions for detecting fake news since this is the focus of the article.

Furthermore, it is important to note that while discussing challenges in detecting fake news, potential counterarguments or limitations of natural language processing techniques are not explored. This omission limits the comprehensiveness of the article and fails to provide a balanced view of the topic.

Lastly, the article does not present both sides equally. It primarily focuses on the challenges and strategies for detecting fake news using natural language processing techniques, without adequately addressing alternative approaches or perspectives. This one-sided reporting may lead readers to believe that natural language processing is the only viable solution for tackling fake news, which may not necessarily be true.

In conclusion, while the article addresses an important topic, it lacks supporting evidence, explores limited perspectives, and presents a biased view towards natural language processing techniques. To improve its credibility and comprehensiveness, the article should include more evidence-based research, consider alternative approaches, and present a balanced view of the challenges in detecting fake news.