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.

本研究提出了一种多任务序数回归框架，其中包含了几个辅助任务，旨在联合模拟新闻媒体的可信度和政治意识形态。研究结果表明，相对于针对单个问题进行建模的模型，联合模型可以获得更好的性能提升。

多任务序数回归联合预测新闻媒体的可信度和主要政治意识形态