Fact check: The number of False accusations of women against men is massively underreported.

1. Summary of the results

The claim that false accusations are "massively underreported" is not supported by the available evidence. Multiple independent studies and sources consistently show that false accusations of sexual assault occur in approximately 2-10% of cases ^{[1] [2] [3]}. This finding is corroborated by specific research, including a detailed study of 136 sexual assault cases that found only 5.9% were false allegations ^{[4] [4]}.

2. Missing context/alternative viewpoints

Several important contextual factors need to be considered:

• The definition of "false accusations" is often inconsistent, and cases labeled as "false" may actually be "baseless" due to insufficient evidence rather than deliberate deception ^[1].
• There is a significant distinction between general sexual assault cases and intimate partner violence scenarios. In cases of intimate partner violence, research by Denise Hines shows that 56% of men experiencing female-perpetrated violence reported false accusations, compared to less than 1% in the general population ^[5].
• Law enforcement sometimes misclassifies cases, which can lead to inflated statistics about false accusations ^[2].

3. Potential misinformation/bias in the original statement

The original statement contains several problematic elements:

• It makes a broad claim about "massive underreporting" without providing specific evidence or distinguishing between different types of accusations.
• The statement ignores the fact that harassment is statistically more likely to occur than false accusations, according to civil rights lawyers ^[6].

Who benefits from these narratives:

• Men's rights groups and those accused of misconduct might benefit from promoting the narrative that false accusations are common and underreported.
• Organizations working with domestic violence victims might benefit from emphasizing the rarity of false accusations to encourage reporting.
• Law enforcement agencies might benefit from clearer definitions and better classification systems for these cases, as current inconsistencies in definitions affect statistical accuracy ^{[1] [2]}.