  The authors of fake news often use facts from verified news sources and mix
them with misinformation to create confusion and provoke unrest among the
readers. The spread of fake news can thereby have serious implications on our
society. They can sway political elections, push down the stock price or crush
reputations of corporations or public figures. Several websites have taken on
the mission of checking rumors and allegations, but are often not fast enough
to check the content of all the news being disseminated. Especially social
media websites have offered an easy platform for the fast propagation of
information. Towards limiting fake news from being propagated among social
media users, the task of this year's PAN 2020 challenge lays the focus on the
fake news spreaders. The aim of the task is to determine whether it is possible
to discriminate authors that have shared fake news in the past from those that
have never done it. In this notebook, we describe our profiling system for the
fake news detection task on Twitter. For this, we conduct different feature
extraction techniques and learning experiments from a multilingual perspective,
namely English and Spanish. Our final submitted systems use character n-grams
as features in combination with a linear SVM for English and Logistic
Regression for the Spanish language. Our submitted models achieve an overall
accuracy of 73% and 79% on the English and Spanish official test set,
respectively. Our experiments show that it is difficult to differentiate
solidly fake news spreaders on Twitter from users who share credible
information leaving room for further investigations. Our model ranked 3rd out
of 72 competitors.
