Capturing Pertinent Symbolic Features for Enhanced Content-Based
  Misinformation Detection
Flavio Merenda José Manuel Gómez-Pérez
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.