  In the context of fake news, bias, and propaganda, we study two important but
relatively under-explored problems: (i) trustworthiness estimation (on a
3-point scale) and (ii) political ideology detection (left/right bias on a
7-point scale) of entire news outlets, as opposed to evaluating individual
articles. In particular, we propose a multi-task ordinal regression framework
that models the two problems jointly. This is motivated by the observation that
hyper-partisanship is often linked to low trustworthiness, e.g., appealing to
emotions rather than sticking to the facts, while center media tend to be
generally more impartial and trustworthy. We further use several auxiliary
tasks, modeling centrality, hyperpartisanship, as well as left-vs.-right bias
on a coarse-grained scale. The evaluation results show sizable performance
gains by the joint models over models that target the problems in isolation.
