  Social networking and micro-blogging services, such as Twitter, play an
important role in sharing digital information. Despite the popularity and
usefulness of social media, there have been many instances where corrupted
users found ways to abuse it, as for instance, through raising or lowering
user's credibility. As a result, while social media facilitates an
unprecedented ease of access to information, it also introduces a new challenge
- that of ascertaining the credibility of shared information. Currently, there
is no automated way of determining which news or users are credible and which
are not. Hence, establishing a system that can measure the social media user's
credibility has become an issue of great importance. Assigning a credibility
score to a user has piqued the interest of not only the research community but
also most of the big players on both sides - such as Facebook, on the side of
industry, and political parties on the societal one. In this work, we created a
model which, we hope, will ultimately facilitate and support the increase of
trust in the social network communities. Our model collected data and analysed
the behaviour of~50,000 politicians on Twitter. Influence score, based on
several chosen features, was assigned to each evaluated user. Further, we
classified the political Twitter users as either trusted or untrusted using
random forest, multilayer perceptron, and support vector machine. An active
learning model was used to classify any unlabelled ambiguous records from our
dataset. Finally, to measure the performance of the proposed model, we used
precision, recall, F1 score, and accuracy as the main evaluation metrics.
