Preventing the spread of misinformation is challenging. The detection of
misleading content presents a significant hurdle due to its extreme linguistic
and domain variability. Content-based models have managed to identify deceptive
language by learning representations from textual data such as social media
posts and web articles. However, aggregating representative samples of this
heterogeneous phenomenon and implementing effective real-world applications is
still elusive. Based on analytical work on the language of misinformation, this
paper analyzes the linguistic attributes that characterize this phenomenon and
how representative of such features some of the most popular misinformation
datasets are. We demonstrate that the appropriate use of pertinent symbolic
knowledge in combination with neural language models is helpful in detecting
misleading content. Our results achieve state-of-the-art performance in
misinformation datasets across the board, showing that our approach offers a
valid and robust alternative to multi-task transfer learning without requiring
any additional training data. Furthermore, our results show evidence that
structured knowledge can provide the extra boost required to address a complex
and unpredictable real-world problem like misinformation detection, not only in
terms of accuracy but also time efficiency and resource utilization.

通过分析虚假信息的语言属性及相关数据集，本论文展示了在检测虚假内容方面，采用相关符号知识和神经语言模型的适当使用可以取得最先进的性能，在准确性、时间效率和资源利用方面提供了有效且稳健的替代方法。

提升基于内容的虚假信息检测的关键符号特征捕捉

Capturing Pertinent Symbolic Features for Enhanced Content-Based  Misinformation Detection

Social media may disseminate medical claims that highlight misleading
correlations between social identifiers and diseases due to not accounting for
structural determinants of health. Our research aims to identify biased medical
claims on Twitter and measure their spread. We propose a machine learning
framework that uses two models in tandem: RoBERTa to detect medical claims and
DistilBERT to classify bias. After identifying original biased medical claims,
we conducted a retweet cascade analysis, computing their individual reach and
rate of spread. Tweets containing biased claims were found to circulate faster
and further than unbiased claims.

社交媒体上可能会传播强调社会识别因素与疾病之间误导性相关性的医疗声明，而不考虑健康的结构性决定因素。我们的研究旨在识别 Twitter 上的偏见医疗声明并测量它们的传播程度。我们提出了一个机器学习框架，使用 RoBERTa 模型检测医疗声明，使用 DistilBERT 模型分类偏见。在识别出原始的有偏见医疗声明后，我们进行了转推级联分析，计算它们的个体传播范围和传播速率。我们发现，包含偏见声明的推文传播得比无偏见声明的更快、更远。