We now have about 150 students working in small teams to build fake news detection models. From the field reports, they are having lots of fun learning new ways to think about news, facts, data, tools, and model building. Some of the oservations in this report by George McIntire may be useful for people thinking about the challenge. One paragraph that caught my eye:
“One big assumption underlying this project is that there is considerable overlap in the topics covered by each class of article. If certain words or phrases show up more often in “real” news instead of “fake” news it doesn’t necessarily mean that those terms are associated more with “real” news, but instead could just mean that those words are used in topics more common in the real news dataset. For example, the names of politicians showed up more often in articles classified as “real” than in ones classified as “fake”. I would be committing a huge error if I came to the conclusion that articles mentioning politicians are significantly more likely to be to factual. Some of the most prominent and outlandish fake news articles circulating throughout the internet are ludicrous conspiracy theories about politicians.”
First of all let me tell you, you have got a great blog. I am interested in looking for more of such topics and would like to have further information. Hope to see the next blog soon.
We are a group of volunteers and starting a new initiative in a community. Your blog provided us valuable information to work on. You have done a marvelous job!
i am also intrested for this type of project.please guide me
Please send us an email at: help@discovertext.com
Please write directly to Stu@texifter.com for more information.
Can you please provide us with the dataset for fake news detection.
Please write to stu@texifter.com about this request. We do have sets we can share.