Retweet Network Analysis in Cryptocurrencies
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In this tutorial, we will run a simple network analysis on retweets that contain the hashtag “#Crypto” by taking into consideration Twitter data. For this tutorial, we assume that you are familiar with SNA, and you know how to get Twitter data using R or Python. For this tutorial, we will work in R.
Get the Tweets
The first thing that we need to do is to get the tweets that contain the hashtag “#Crypto“. Let’s install the libraries and get authorized.
######################################### ################# Crypto Network Analysis ######################################### library(rtweet) library(tidyverse) library(igraph) ## store api keys (these are fake example values; replace with your own keys) api_key <- "e2doc7YCE7QXOTJEMZjuto1u3" api_secret_key <- "kEXdrury86McHUouQMy4SdEyFzgNBcJvoAjD1NJ3xL81RLuLgu" access_token <- "313313903-fauYDPDt3PT6ACsaB8H1hIJsUygwLKbO2BNFYQyd" access_token_secret <- "4qK7jeN9PX8zXwek1GyeGVOEUlEzbZU8d4OV0YifWBKuz" ## authenticate via web browser token <- create_token( app = "gpipis_test_01", consumer_key = api_key, consumer_secret = api_secret_key, access_token = access_token, access_secret = access_token_secret) get_token()
Now, let’s get 50,000 tweets that contain the hashtag “#Crypto“. Then we will keep the domain names of the source, i.e. the users who posted the tweets and the target, i.e. the users who retweeted the post.
# search for tweets containing the hashtag #Crypto rt_all<-search_tweets("#Crypto", n=50000, include_rts = TRUE, retryonratelimit=TRUE) # keep only the screen_name and the retweet_screen_name rt<-rt_all[, c("screen_name", "retweet_screen_name")] # remove the NA rows and duplicates rt<-rt%>%na.omit()%>%distinct_all()
It will be helpful to get an idea of the data that we obtained, starting with the “tweets”.
We can have a look at the tweets that got the most likes.
View(rt_all%>%arrange(desc(favorite_count))%>%head(10))
Retweet Network Analysis
Our goal here is to find the users who are influencers, meaning that their tweets are becoming popular and are retweeted by other users. In network analysis, we call it “in-degree centrality”. The data frame of the “source” and “target” is the following:
head(rt) # A tibble: 6 x 2 screen_name retweet_screen_name <chr> <chr> 1 Aughauztyne Cryptoconflict_ 2 JavadHo49442036 Raiinmakerapp 3 maubigwin_dong kakanftworld 4 tanvirtarek5 ThePulseLorian 5 merGeyi93956765 KCC_Enthusiast 6 merGeyi93956765 Texan_Shinja
Using the igraph library we will create the directed network and then we will get the users with the highest in-degree centrality.
# create the directed network graph crypto_network <- graph_from_edgelist(el = as.matrix(rt), directed = TRUE) # get the in-degree i.e. users who are re-tweeted in_degree<-degree(crypto_network, mode=c("in")) # get the top 10 users in terms of in degree as.data.frame(in_degree)%>%arrange(desc(in_degree))%>%head(10)
We get:
in_degree SombraNetwork 382 kakanftworld 337 ThePulseLorian 289 revolut20 247 DeeColinok 195 CryptoTownEU 188 Trush_io 181 cryptoo_kingg 113 kucoincom 105 dio_ianakiara 97
We can have a look at the profiles of these “key” users.
Apart from the in-degree centrality, there is also the out-degree centrality, meaning the users who retweet. Let’s see the accounts with the highest out-degree centrality.
# get the out-degree i.e. users who re-tweet out_degree<-degree(crypto_network, mode=c("out")) # get the top 10 users in terms of out degree as.data.frame(out_degree)%>%arrange(desc(out_degree))%>%head(10)
And we get:
out_degree Jisan909 13 yamada42663496 9 HERO_SAMURAI1 8 Realist481 8 smartsandal 7 Okami_mxsamurai 7 Fabriciosx 7 E__dollar 7 roseannb17 7 Shido_samuraii 6
Finally, we can get the betweenness centrality, which is a measurement of a user’s influence in the flow of information in the social network. We can consider them as the bridge between two key players.
# between centrality bt_degree<-betweenness(crypto_network, directed = TRUE) as.data.frame(bt_degree)%>%arrange(desc(bt_degree))%>%head(10)
And we get:
bt_degree Shido_samuraii 30.5 Emmy_prime_ 14.0 miticomansamur1 12.0 ultracig 11.0 Okami_mxsamurai 9.5 Sohylasamurai2 6.0 Nanceebaybehh 6.0 BobEatsBacon1 5.0 VanZoy1 5.0 _borutoKING 4.0
Final Thoughts
We know that asset prices are affected by the available information. There is a motto, we buy the rumors and we sell the news. Since many investors are analyzing trends in social media such as Twitter, the retweet network analysis can help to detect the influencers of the network and is a good idea to keep monitoring them since they can impact the market to some extent.
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