# Fake News Detection via Wisdom of Synthetic & Representative Crowds

arXiv: https://arxiv.org/abs/2408.03154 · HTML: https://mlsanity.com/paper/2408.03154/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Aug 2024.

## Built on by (most cited first, 2 of 2)

- [Detecting Fake News on Social Media: A Novel Reliability Aware Machine-Crowd Hybrid Intelligence-Based Method](https://mlsanity.com/paper/2412.06833/) (2024)
- [BharatGather: A Culturally-Informed Benchmark Dataset for Misinformation and Fake News Detection in Indian Public Events](https://mlsanity.com/paper/2609.02895/) (2026)

## Abstract (arXiv)

Social media companies have struggled to provide a democratically legitimate definition of "Fake News". Reliance on expert judgment has attracted criticism due to a general trust deficit and political polarisation. Approaches reliant on the ``wisdom of the crowds'' are a cost-effective, transparent and inclusive alternative. This paper provides a novel end-to-end methodology to detect fake news on X via "wisdom of the synthetic & representative crowds". We deploy an online survey on the Lucid platform to gather veracity assessments for a number of pandemic-related tweets from crowd-workers. Borrowing from the MrP literature, we train a Hierarchical Bayesian model to predict the veracity of each tweet from the perspective of different personae from the population of interest. We then weight the predicted veracity assessments according to a representative stratification frame, such that decisions about ``fake'' tweets are representative of the overall polity of interest. Based on these aggregated scores, we analyse a corpus of tweets and perform a second MrP to generate state-level estimates of the number of people who share fake news. We find small but statistically meaningful heterogeneity in fake news sharing across US states. At the individual-level: i. sharing fake news is generally rare, with an average sharing probability interval [0.07,0.14]; ii. strong evidence that Democrats share less fake news, accounting for a reduction in the sharing odds of [57.3%,3.9%] relative to the average user; iii. when Republican definitions of fake news are used, it is the latter who show a decrease in the propensity to share fake news worth [50.8%, 2.0%]; iv. some evidence that women share less fake news than men, an effect worth a [29.5%,4.9%] decrease.