What Tweets and YouTube comments have in common? Sentiment and Graph analysis on data related to US Elections 2020.

The presidential elections in the United States on November 3rd 2020 caused extensive discussions on social media. A part of the content on US elections is organic, coming from users discussing their opinions on the candidates, political positions, or relevant content presented on television. Another significant part originates from organized campaigns, both official, including communication campaigns and dissemination, or unofficial, including astroturfing and targeting manipulation of the electorate.

In this study, we obtain approximately 19.8M tweets from 4.5M users, based on prevalent hashtags related to the 2020 US election. From these, we mined 28.343 YouTube links tweeted and obtained likes, dislikes and comments of these videos. In this paper, we study the connection between the two social networks. We employ an array of techniques, including volume analysis, exploring the retweet graph, sentiment and graph analysis on the communities formed in YouTube and Twitter. Furthermore, we propose a method to combine the results of community detection on the two social networks and measure the differences between them.

Particularly, we study the daily traffic per prevalent hashtags, plot the retweet graph from July to November 2020, highlight the two main entities (‘Biden’ and ‘Trump’) and show how the discussion around those entities grows in the period closer to the elections. Additionally, we perform a sentiment analysis of both the Twitter corpus and the YouTube comments in tweeted videos. We found that 35,2% o the users contained in our Twitter dataset express positive sentiment towards Trump and 28% express positive sentiment towards Biden; while 18% of the users in our YouTube dataset express positive sentiment towards Trump and 12% express positive sentiment towards Biden. Finally, we link the Twitter Retweet graph with the YouTube comment graph using tweeted video links. We measure their similarity and differences and show the interactions and the correlation between the largest communities on YouTube and Twitter.