Informational bias is widely present in news articles. It refers to providing
one-sided, selective or suggestive information of specific aspects of certain
entity to guide a specific interpretation, thereby biasing the reader's
opinion. Sentence-level informational bias detection is a very challenging task
in a way that such bias can only be revealed together with the context,
examples include collecting information from various sources or analyzing the
entire article in combination with the background. In this paper, we integrate
three levels of context to detect the sentence-level informational bias in
English news articles: adjacent sentences, whole article, and articles from
other news outlets describing the same event. Our model, MultiCTX (Multi-level
ConTeXt), uses contrastive learning and sentence graphs together with Graph
Attention Network (GAT) to encode these three degrees of context at different
stages by tactically composing contrastive triplets and constructing sentence
graphs within events. Our experiments proved that contrastive learning together
with sentence graphs effectively incorporates context in varying degrees and
significantly outperforms the current SOTA model sentence-wise in informational
bias detection.

本文探讨了如何利用多种不同级别的语境来检测新闻文章中的信息偏见。通过使用对比学习和句子图形以及 GAT 编码三种不同程度的语境，成功地在不同的事件中组成对比三元组和构建句子图形。实验证明，对比学习和句子图形有效地吸收了不同程度的上下文，显著优于当前 SOTA 模型在句子级情感分析方面的表现。

通过对比学习和语句图网络建模多层上下文进行信息偏倚检测

Modeling Multi-level Context for Informational Bias Detection by Contrastive Learning and Sentential Graph Network

Informational bias is bias conveyed through sentences or clauses that provide
tangential, speculative or background information that can sway readers'
opinions towards entities. By nature, informational bias is context-dependent,
but previous work on informational bias detection has not explored the role of
context beyond the sentence. In this paper, we explore four kinds of context
for informational bias in English news articles: neighboring sentences, the
full article, articles on the same event from other news publishers, and
articles from the same domain (but potentially different events). We find that
integrating event context improves classification performance over a very
strong baseline. In addition, we perform the first error analysis of models on
this task. We find that the best-performing context-inclusive model outperforms
the baseline on longer sentences, and sentences from politically centrist
articles.

通过探究四种不同的上下文对英语新闻文章中的信息偏误进行分类，我们发现将事件上下文整合在一起可以提高分类的性能并且可以更好地减少错误率。

信息偏见检测中的背景

Context in Informational Bias Detection

The increasing prevalence of political bias in news media calls for greater
public awareness of it, as well as robust methods for its detection. While
prior work in NLP has primarily focused on the lexical bias captured by
linguistic attributes such as word choice and syntax, other types of bias stem
from the actual content selected for inclusion in the text. In this work, we
investigate the effects of informational bias: factual content that can
nevertheless be deployed to sway reader opinion. We first produce a new
dataset, BASIL, of 300 news articles annotated with 1,727 bias spans and find
evidence that informational bias appears in news articles more frequently than
lexical bias. We further study our annotations to observe how informational
bias surfaces in news articles by different media outlets. Lastly, a baseline
model for informational bias prediction is presented by fine-tuning BERT on our
labeled data, indicating the challenges of the task and future directions.

本研究调查了信息性偏见在新闻媒体中的频率以及 Fine-tuning BERT 在预测信息性偏见方面的挑战和未来方向。