Trump obteve COVID e o Twitter está pegando fogo

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Oh cara, todos pensaram que 2020 estava em ordem, mas cara, que construção narrativa cuidadosa!

Sim, Trump foi infectado com covid-19 e, como o título sugere, o Twitter era o lugar para estar. Esse tweet ganhou popularidade na esquerda e na direita e fez alguns observadores se perguntarem sobre a distribuição ideológica de seu tráfego. As pessoas que estão engajadas neste tweet principalmente mostram seu apoio a Trump ou estão aqui para se gabar?

Felizmente, esta é uma questão empírica. Podemos provar alguns dos engajamentos (ou seja, retuítes) e estimar sua ideologia política.

Engajamento de amostra

Vou começar com a ressalva: por enquanto (afaik), a API do Twitter não fornece informações sobre quem gosta de um tweet (que pode ser onde acontece a maior parte do entusiasmo). No entanto, ele suporta retweeters, então podemos ver quais contas retuitaram o tuíte cobiçoso de Trump.
Existem vários pacotes para obter informações do Twitter, mas o meu favorito de longe é rtweet (Kearney, 2020).
Depois de definir seus tokens de API, obter retweeters é muito fácil:

retweeters 

This function is limited to 100 users for a given status, so I wrapped it in a loop until it returned a couple of thousands of retweeters. This will be our sample for ideological estimation. We can then look them up to get their screen name and all the info we need further down the road.

users 

Ideological Estimation

How does one estimate ideology on Twitter? There are several ways, however, the most popular one I know of (perhaps due to my own psych bias) is the Barberá method (Barberá et al., 2015). I won’t get much into the technical stuff, but in broad terms, it uses your Twitter network.

There are accounts of individuals (e.g., politicians, public figures) and institutions (e.g., newspapers) that we pretty much know their political orientation. Given a certain user follows some of these high profile accounts, the algorithm can infer the user’s political orientation (interesting, right? read the paper!). This means that for every user in our sample of Trump retweeters, we need to get their entire (or actually max 5000) network of people they follow (aka “friends” in the Twitter API jargon) to estimate their ideology.
I don’t think Twitter particularly likes that because it allows for 15 network calls every 15 minutes, which really backlogs the estimation process.

To use the ideological estimation algorithm, first, you should follow the explanation here. Then, the rest of the process looks like this;

library(tweetscores)

#pre allocate ideology vector
users$ideology 

Plot it like it’s hot

It looks like we got 753 accounts with known ideology. Not bad!
What is their ideological distribution?
I wanted to make a density plot and fill it with ranging colors from blue to red. For some reason geom_density won’t work, so thanks to Google and StackOverflow, I found a workaround:

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library(tidyverse)

x 

I ❤

pretty plots.

The X-axis corresponds to ideology, wherein negative values stand for liberal views and positive values for conservative views. The Y-axis corresponds to the probability density function.

So what do we have here?
We definitely see that most retweeters from our sample are right-leaning, which may suggest most gloating happen on subtweets.


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I should note, though that my ad-hoc sampling technique could be subjected to bias. For instance, it could be the case that the tweet was especially trending among conservatives in the specific moments when I pulled the retweets, so take this exercise with a grain of salt.

The post Trump Got COVID and Twitter Is on Fire appeared first on Almog Simchon.



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