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[https://cbail.github.io/textasdata/Text_as_Data.html?fbclid=IwAR1Nl93wTvZlhmVdifK_-I91viDfkH1R69rGwSzE2wM__OOVT_w3mJatgvI] - - public:weinreich
how_to, qualitative, quantitative, research, social_media, twitter - 6 | id:309754 -

This class covers a range of different topics that build on top of each other. For example, in the first tutorial, you will learn how to collect data from Twitter, and in subsequent tutorials you will learn how to analyze those data using automated text analysis techniques. For this reason, you may find it difficult to jump towards one of the most advanced issues before covering the basics. Introduction: Strengths and Weaknesses of Text as Data Application Programming Interfaces Screen-Scraping Basic Text Analysis Dictionary-Based Text Analysis Topic Modeling Text Networks Word Embeddings

[https://www.linkedin.com/pulse/paid-social-worth-investment-wes-finley/] - - public:weinreich
advertising, management, social_media - 3 | id:281059 -

Often, a Facebook page with no Fans can drive greater visibility with $500 of investment than a page can achieve organically with 90 Million+ Fans. This Facebook campaign reaches 1.3 Million people and achieves 42,000 clicks through to a website for $643. Despite the declining ROI of organic content, surprisingly few brands actually promote their social posts regularly. And by ignoring this paid investment they waste time and money creating imagery and copy that will be seen by very few people.

[https://www.theguardian.com/commentisfree/2020/feb/08/misinformation-coronavirus-contagious-infections?CMP=Share_AndroidApp_Tweet] - - public:weinreich
health_communication, social_media - 2 | id:279220 -

To fully explain how viral content – and viruses – spread, we need to move away from the idea that outbreaks involve simple clockwork infections, passing along a chain from person to person to person until large numbers have been exposed. During the 2015 outbreak of the Mers coronavirus in South Korea, 82 out of 186 infections came from a single “superspreading event” in a hospital where an infected person was being treated. It’s not yet clear how common such superspreading is in the current outbreak, but we do know that these kinds of events are how information goes viral online; most outbreaks on Twitter are dominated by a handful of individuals or media outlets, which are responsible for a large proportion of transmission. If you heard about snake flu, you might have told a couple of friends; meanwhile, newspaper headlines were telling millions. When tackling disease outbreaks, health agencies often work to identify potential superspreading events, isolating infected individuals to prevent further transmission. However, this isn’t the only way to stop an outbreak. As well as tracking down people who are infectious, it’s possible to target broader social interactions that might amplify transmission. For example, many cities in China have recently closed schools, which can be hotspots for respiratory infections.

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