Automatic Fake News Detection: Are Models Learning to Reason?

Abstract

Most fact checking models for automatic fake news detection are based on reasoning: given a claim with associated evidence, the models aim to estimate the claim veracity based on the supporting or refuting content within the evidence. When these models perform well, it is generally assumed to be due to the models having learned to reason over the evidence with regards to the claim. In this paper, we investigate this assumption of reasoning, by exploring the relationship and importance of both claim and evidence. Surprisingly, we find on political fact checking datasets that most often the highest effectiveness is obtained by utilizing only the evidence, as the impact of including the claim is either negligible or harmful to the effectiveness. This highlights an important problem in what constitutes evidence in existing approaches for automatic fake news detection.

Automatic Fake News Detection: Are current models "fact-checking" or "gut-checking"?

“…Current fake news detection models that use a claim's search engine results as evidence may unintentionally use hidden signals that are not attributed to the claim (Hansen et al, 2021). Additionally, models may in fact simply memorize biases within data (Gururangan et al, 2018).…”

“…A framework to both categorize fake news and to identify features that differentiate fake news from real news has been described by Molina et al (2021), and debiasing inappropriate subjectivity in text can be accomplished by replacing a single biased word in each sentence (Pryzant et al, 2020). Using the claim as a query, the top ten results from Google News ("snippets") constitute the evidence (Hansen et al, 2021). PolitiFact and Snopes use five labels (False, Mostly False, Mixture, Mostly True, True), which we collapse to True, Mixture, and False.…”

“…The most common automatic fact-checking NLP models are based on term frequency, word embeddings, and contextualized word embeddings, using Random Forests, LSTMs, and BERT (Hassan et al, 2017). We limit our experimentation to the BERT model, as it is the highest performing state-of-the-art model and was thoroughly tested in (Hansen et al, 2021). This BERT model with no pre-processing is our baseline model.…”

“…The lowest F1 Macro scores and deltas are in red. With the exception of EmoLexi tying for the lowest delta, the best preprocessing steps outperform the baseline BERT model fromHansen et al (2021).…”

“…There is an implicit assumption that the fact-checking models are reasoning in some way, using the evidence to confirm or refute the claim. Recent research (Hansen et al, 2021) found this conclusion may be premature; current models can show improved performance when considering evidence alone, essentially fact-checking an unasked question. While this might seem reasonable given that the evidence is conditioned on the claims by the search engine, this can be exploited as illustrated in Figure 1, which shows that evidence returned using a ridiculous claim can still appear reasonable if we view the evidence alone without the claim.…”