The upcoming November 2020 presidential elections in the United States have caused extensive discussions on social media. A part of the content on US elections URLanic, coming from users discussing their opinions of the candidates, political positions, or relevant content presented on television. Another significant part of the content generated originates URLanized campaigns, both official and by astroturfing. In this study, we obtain approximately 7.5M tweets , containing 1.4M users, based on prevalent hashtags related to US election 2020, as well as the related YouTube links, contained in the Twitter dataset, likes, dislikes and comments of the videos and conduct volume, sentiment and graph analysis on the communities formed. Particularly, we study the daily traffic per prevalent hashtags and show the evolution of the retweet graph from July to September 2020, highlighting the two main entities ('Biden' and 'Trump') contained in our dataset. The results of sentiment analysis indicate that 45.7  express positive sentiment towards Trump in Twitter and 33.8  positive sentiment towards Biden, while 14.55  of users express positive sentiment in YouTube metadata gathered towards Trump and 8.7  positive sentiment towards Biden  .
The upcoming November 2020 presidential elections in the United States have caused extensive discussions on social media. A part of the content on US elections URLanic, coming from users discussing their opinions of the candidates, political positions, or relevant content presented on television. Another significant part of the content generated originates URLanized campaigns, both official and by astroturfing. In this study, we obtain approximately 17.5 M tweets containing 3M users, based on prevalent hashtags related to US election 2020, as well as the related YouTube links, contained in the Twitter dataset, likes, dislikes and comments of the videos and conduct volume, sentiment and graph analysis on the communities formed. Particularly, we study the daily traffic per prevalent hashtags and show the evolution of the retweet graph from July to September 2020, highlighting the two main entities ('Biden' and 'Trump') contained in our dataset. Additionally, we gather the related YouTube links contained in the previous dataset and perform sentiment analysis. The results on sentiment analysis on the Twitter corpus and the YouTube metadata gathered, show the positive and negative sentiment for the two entities throughout this period. The results of sentiment analysis indicate that 45.7 \% express positive sentiment towards Trump in Twitter and 33.8 \% positive sentiment towards Biden, while 14.55 \% of users express positive sentiment in YouTube metadata gathered towards Trump and 8.7 \% positive sentiment towards Biden . Our analysis fill the gap between the connection of offline events and their consequences in social media by monitoring important events in real world and measuring public volume and sentiment before and after the event in social media .
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7.5M tweets , containing 1.4M
17.5 M tweets containing 3M
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The results
Additionally, we gather the related YouTube links contained in the previous dataset and perform sentiment analysis. The results on sentiment analysis on the Twitter corpus and the YouTube metadata gathered, show the positive and negative sentiment for the two entities throughout this period. The results
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. Our analysis fill the gap between the connection of offline events and their consequences in social media by monitoring important events in real world and measuring public volume and sentiment before and after the event in social media