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In the context of fake news, bias, and propaganda, we study two important butrelatively under-explored problems: (i) trustworthiness estimation (on a3-point scale) and (ii) political ideology detection (left/right bias on a7-point scale) of entire news outlets, as opposed to evaluating individualarticles. In particular, we propose a multi-task ordinal regression frameworkthat models the two problems jointly. This is motivated by the observation thathyper-partisanship is often linked to low trustworthiness, e.g., appealing toemotions rather than sticking to the facts, while center media tend to begenerally more impartial and trustworthy. We further use several auxiliarytasks, modeling centrality, hyperpartisanship, as well as left-vs.-right biason a coarse-grained scale. The evaluation results show sizable performancegains by the joint models over models that target the problems in isolation.

Multi-Task Ordinal Regression for Jointly Predicting the Trustworthiness and the Leading Political Ideology of News Media