Summary:
This paper was primarily about how fake news appears to resemble satirical news as opposed to the commonly-held belief that fake news resembles real news. Through analyzing data sets containing fake, real, and satirical news, comparisons were made between these groups based on a set of features. Some of the key conclusions drawn are listed below.
- The content of fake news and real news articles is vastly different from one another. Real news articles tend to be slightly longer, whereas fake articles use fewer technical words and typically require a lower educational level to read. Fake news also contains more redundancy and personal pronouns.
- Titles are a significant factor in differentiating between fake and real news articles. Fake news titles tend to be longer and usually contain more capitalized words and proper nouns. It’s also common for fake news titles to have several verb clauses, as the writers try to squeeze as much substance as they can.
- Fake news articles are more similar to satire than real news articles. Both types of articles usually contain smaller words and tend to use fewer technical words. In addition, both fake news and satirical articles tend to contain more redundancy.
Reflection:
I have listed below a few of the lines that interested me in the paper.
“SentiStrength is a sentiment analysis tool that reports a negative sentiment integer score between -1 and -5 and a positive sentiment integer score between 1 and 5, where -5 is the most negative and 5 is the most positive.”
SentiStrength is one of the more interesting features these data sets are being analyzed upon. The results of the study showed that there was a correlation between fake news and generally negative sentiment. Although the sentiment of real news articles tends to be more neutral, the majority of satirical articles have a negative sentiment as well. Which begs the question, could there be a sentiment analysis tool that could analyze sarcasm and humor? Such a tool could certainly aid in distinguishing between fake news articles and satire.
“Fake news packs the main claim of the article into its title, which often is about a specific person and entity, allowing the reader to skip reading the article, which tends to be short, repetitive, and less informative.”
Fake news has been one of the prevalent issues that our country has faced in recent years, primarily due to having a significant impact on the 2016 US Presidential Election. If Facebook or Google users only read a headline without delving in and determining the legitimacy of an article, then fake news suddenly changes from being an annoyance to a danger. Machine learning has an obvious application here in tracking down fake news, but this raises several other questions.
- Will users be alright with Facebook restricting what they can and cannot share with their friends?
- What happens if there are articles with a mix of real news (to bypass fake news detectors) but also contain fake news?
- Is this task too much for AI and does it require the presence of a human?
Other Thoughts:
Overall, the paper’s results were not surprising at all, as satire is one of the primary forms of fake news. Although with a different agenda, satire does not contain legitimate news, and has the potential to mislead readers. One of the principal questions asked in determining the credibility of an article is “Does this article seem like satire?”
