# Belittling the Source: Trustworthiness Indicators to Obfuscate Fake News on the Web

arXiv: https://arxiv.org/abs/1809.00494 · HTML: https://mlsanity.com/paper/1809.00494/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Sep 2018.

## Built on by (most cited first, 4 of 4)

- [Mapping the Media Landscape: Predicting Factual Reporting and Political Bias Through Web Interactions](https://mlsanity.com/paper/2410.17655/) (2024)
- [Technological Approaches to Detecting Online Disinformation and Manipulation](https://mlsanity.com/paper/2108.11669/) (2021)
- [ECOL: Early Detection of COVID Lies Using Content, Prior Knowledge and Source Information](https://mlsanity.com/paper/2101.05499/) (2021)
- [Supporting verification of news articles with automated search for semantically similar articles](https://mlsanity.com/paper/2103.15581/) (2021)

## Abstract (arXiv)

With the growth of the internet, the number of fake-news online has been proliferating every year. The consequences of such phenomena are manifold, ranging from lousy decision-making process to bullying and violence episodes. Therefore, fact-checking algorithms became a valuable asset. To this aim, an important step to detect fake-news is to have access to a credibility score for a given information source. However, most of the widely used Web indicators have either been shut-down to the public (e.g., Google PageRank) or are not free for use (Alexa Rank). Further existing databases are short-manually curated lists of online sources, which do not scale. Finally, most of the research on the topic is theoretical-based or explore confidential data in a restricted simulation environment. In this paper we explore current research, highlight the challenges and propose solutions to tackle the problem of classifying websites into a credibility scale. The proposed model automatically extracts source reputation cues and computes a credibility factor, providing valuable insights which can help in belittling dubious and confirming trustful unknown websites. Experimental results outperform state of the art in the 2-classes and 5-classes setting.