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There are a lot of research going on tweets like fake news, event detection, sarcasm detection etc., So depending upon situation, people may leverage a common observable pattern to filter relevant tweets from the tweet stream.

  1. Have anyone come across any such implementation or research?
  2. How such application can be proved to be useful from theoretical/implementation/research view point, apart from the fact they will be time efficient?

Tags: analytics, filtering, research, text analytics, tweet, twitter

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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.

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.

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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