  In the past decade we have witnessed the failure of traditional polls in
predicting presidential election outcomes across the world. To understand the
reasons behind these failures we analyze the raw data of a trusted pollster
which failed to predict, along with the rest of the pollsters, the surprising
2019 presidential election in Argentina which has led to a major market
collapse in that country. Analysis of the raw and re-weighted data from
longitudinal surveys performed before and after the elections reveals clear
biases (beyond well-known low-response rates) related to mis-representation of
the population and, most importantly, to social-desirability biases, i.e., the
tendency of respondents to hide their intention to vote for controversial
candidates. We then propose a longitudinal opinion tracking method based on
big-data analytics from social media, machine learning, and network theory that
overcomes the limits of traditional polls. The model achieves accurate results
in the 2019 Argentina elections predicting the overwhelming victory of the
candidate Alberto Fern\'andez over the president Mauricio Macri; a result that
none of the traditional pollsters in the country was able to predict. Beyond
predicting political elections, the framework we propose is more general and
can be used to discover trends in society; for instance, what people think
about economics, education or climate change.
