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Permalink Reply by Vincent Granville on March 27, 2018 at 5:42am

We have posted a few articles on this topic, see here.  One way to handle it is to

• Automatically crawl the web to see if the same article is also posted in trustworthy outlets
• Has the media source in question been flagged in the past for posting news that might be fake?
• Does the source domain contain words such as "viral", e.g. viralnews.com?
• Is the source domain a new website, or has it been around for a while?

Eventually, you want to attach a trustworthiness score to each post, and make those with a low score less visible.

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Permalink Reply by surya prakash Sahu on March 28, 2018 at 6:35am

I am mainly focusing on researches/fields that are being done by filtering tweets or things that have been achieved by filtering tweets. So here, I want to know about filtering of tweets and it's various uses and implications.But your suggestion is towards different aspects and procedures of "fake news detection".

Vincent Granville said:

We have posted a few articles on this topic, see here.  One way to handle it is to

• Automatically crawl the web to see if the same article is also posted in trustworthy outlets
• Has the media source in question been flagged in the past for posting news that might be fake?
• Does the source domain contain words such as "viral", e.g. viralnews.com?
• Is the source domain a new website, or has it been around for a while?

Eventually, you want to attach a trustworthiness score to each post, and make those with a low score less visible.

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Permalink Reply by Jim Sharpe on March 29, 2018 at 1:53pm

IMO this is an excellent podcast series. The one on sarcasm is episode 48 is particularly good.

https://blogs.nvidia.com/ai-podcast/

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