# Distilling Information Reliability and Source Trustworthiness from Digital Traces

arXiv: https://arxiv.org/abs/1610.07472 · HTML: https://mlsanity.com/paper/1610.07472/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Oct 2016.

## Built on by (most cited first, 5 of 5)

- [Counterfactual Temporal Point Processes](https://mlsanity.com/paper/2111.07603/) (2021)
- [Cross-Modal Augmentation for Few-Shot Multimodal Fake News Detection](https://mlsanity.com/paper/2407.12880/) (2024)
- [Surfacing Estimation Uncertainty in the Decay Parameters of Hawkes Processes with Exponential Kernels](https://mlsanity.com/paper/2104.01029/) (2021)
- [Large Visual-Language Models Are Also Good Classifiers: A Study of In-Context Multimodal Fake News Detection](https://mlsanity.com/paper/2407.12879/) (2024)
- [Cumulative Distribution Function based General Temporal Point Processes](https://mlsanity.com/paper/2402.00388/) (2024)

## Abstract (arXiv)

Online knowledge repositories typically rely on their users or dedicated editors to evaluate the reliability of their content. These evaluations can be viewed as noisy measurements of both information reliability and information source trustworthiness. Can we leverage these noisy evaluations, often biased, to distill a robust, unbiased and interpretable measure of both notions? In this paper, we argue that the temporal traces left by these noisy evaluations give cues on the reliability of the information and the trustworthiness of the sources. Then, we propose a temporal point process modeling framework that links these temporal traces to robust, unbiased and interpretable notions of information reliability and source trustworthiness. Furthermore, we develop an efficient convex optimization procedure to learn the parameters of the model from historical traces. Experiments on real-world data gathered from Wikipedia and Stack Overflow show that our modeling framework accurately predicts evaluation events, provides an interpretable measure of information reliability and source trustworthiness, and yields interesting insights about real-world events.