Although the area was still largely unexplored, Kristensen's research showed that only very little, publicly available data was needed to predict which way someone would vote.
By liking politicians' public Facebook posts, a person's voting intention could be predicted with up to 70 per cent accuracy in a multi-party system.
"A few selective digital traces produce prediction accuracies that are on par or even greater than most current approaches based upon bigger and broader datasets.
"Combining the online and offline, we connect a subsample of surveyed respondents to their public Facebook activity and apply machine learning classifiers to explore the link between their political liking behaviour and actual voting intention.
"Through this work, we show that even a single selective Facebook 'like' can reveal as much about political voter intention as hundreds of heterogeneous 'likes'.
Within the field of election forecasting, research has shown the potential for predicting election outcomes based on digital data from a diverse range of platforms including YouTube, Google, Twitter, Facebook, and even Wikipedia.
"The techniques we developed are not language dependent and can be employed in any country where Facebook is popular with the general population," he said.
"Future work will involve drawing on the results from our paper and using them to make election result predictions."