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We applied complex network analysis to ~27,000 tweets posted by the 2016 presidential elections principal participants in the USA. We identified the stages of the election campaigns and the recurring topics addressed by the candidates. Finally, we revealed the leader-follower relationships between the candidates. We conclude that Secretary Hillary Clintons Twitter performance was subordinate to that of Donald Trump, which may have been one factor that led to her electoral defeat.
It is a widely accepted fact that state-sponsored Twitter accounts operated during the 2016 US presidential election spreading millions of tweets with misinformation and inflammatory political content. Whether these social media campaigns of the so-c
The dynamics and influence of fake news on Twitter during the 2016 US presidential election remains to be clarified. Here, we use a dataset of 171 million tweets in the five months preceding the election day to identify 30 million tweets, from 2.2 mi
It is a widely accepted fact that state-sponsored Twitter accounts operated during the 2016 US presidential election, spreading millions of tweets with misinformation and inflammatory political content. Whether these social media campaigns of the so-
As more and more data being created every day, all of it can help take better decisions with data analysis. It is not different from data generated in financial markets. Here we examine the process of how the global economy is affected by the market
We use a method based on machine learning, big-data analytics, and network theory to process millions of messages posted in Twitter to predict election outcomes. The model has achieved accurate results in the current Argentina primary presidential el