  Social media include diverse interaction metrics related to user popularity,
the most evident example being the number of user followers. The latter has
raised concerns about the credibility of the posts by the most popular
creators. However, most existing approaches to assess credibility in social
media strictly consider this problem a binary classification, often based on a
priori information, without checking if actual real-world facts back the users'
comments. In addition, they do not provide automatic explanations of their
predictions to foster their trustworthiness. In this work, we propose a
credibility assessment solution for financial creators in social media that
combines Natural Language Processing and Machine Learning. The reputation of
the contributors is assessed by automatically classifying their forecasts on
asset values by type and verifying these predictions with actual market data to
approximate their probability of success. The outcome of this verification is a
continuous credibility score instead of a binary result, an entirely novel
contribution by this work. Moreover, social media metrics (i.e., user context)
are exploited by calculating their correlation with the credibility rankings,
providing insights on the interest of the end-users in financial posts and
their forecasts (i.e., drop or rise). Finally, the system provides natural
language explanations of its decisions based on a model-agnostic analysis of
relevant features.
