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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 election on August 11, 2019 by predicting the large difference win of candidate Alberto Fernandez over president Mauricio Macri; a result that none of the traditional pollsters in that country was able to predict, and has led to a major bond market collapse. We apply the model to the upcoming Argentina presidential election on October 27, 2019 yielding the following results: Fernandez 47.5%, Macri 30.9% and third party 21.6%. Our method improves over traditional polling methods which are based on direct interactions with small number of individuals that are plagued by ever declining response rates, currently falling in the low single digits. They provide a reliable polling method that can be applied not only to predict elections but to discover any trend in society, for instance, what people think about climate change, politics or education.
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
Identifying and characterizing disinformation in political discourse on social media is critical to ensure the integrity of elections and democratic processes around the world. Persistent manipulation of social media has resulted in increased concern
There is currently no easy way to fact-check content on WhatsApp and other end-to-end encrypted platforms at scale. In this paper, we analyze the usefulness of a crowd-sourced tipline through which users can submit content (tips) that they want fact-
Today, an estimated 75% of the British public access information about politics and public life online, and 40% do so via social media. With this context in mind, we investigate information sharing patterns over social media in the lead-up to the 201