# Combining Machine Learning with Knowledge Engineering to detect Fake News in Social Networks-a survey

arXiv: https://arxiv.org/abs/2201.08032 · HTML: https://mlsanity.com/paper/2201.08032/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 61 references that are in the map.
- First posted on arXiv 20 Jan 2022.

## Builds on (most cited first, 7 of 7)

- ["Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection](https://mlsanity.com/paper/1705.00648/) (2017)
- [Detection and Resolution of Rumours in Social Media: A Survey](https://mlsanity.com/paper/1704.00656/) (2017)
- [CSI: A Hybrid Deep Model for Fake News Detection](https://mlsanity.com/paper/1703.06959/) (2017)
- [Stance and Sentiment in Tweets](https://mlsanity.com/paper/1605.01655/) (2016)
- [A Survey on Truth Discovery](https://mlsanity.com/paper/1505.02463/) (2015)
- [Discriminative Predicate Path Mining for Fact Checking in Knowledge Graphs](https://mlsanity.com/paper/1510.05911/) (2015)
- [People on Media: Jointly Identifying Credible News and Trustworthy Citizen Journalists in Online Communities](https://mlsanity.com/paper/1705.02667/) (2017)

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

- [A Survey of Graph-based Deep Learning for Anomaly Detection in Distributed Systems](https://mlsanity.com/paper/2206.04149/) (2022)
- [Anomaly Detection in Dynamic Graphs: A Comprehensive Survey](https://mlsanity.com/paper/2406.00134/) (2024)
- [BOURNE: Bootstrapped Self-supervised Learning Framework for Unified Graph Anomaly Detection](https://mlsanity.com/paper/2307.15244/) (2023)
- [BiMind: A Dual-Head Reasoning Model with Attention-Geometry Adapter for Incorrect Information Detection](https://mlsanity.com/paper/2604.06022/) (2026)

## Abstract (arXiv)

Due to extensive spread of fake news on social and news media it became an emerging research topic now a days that gained attention. In the news media and social media the information is spread highspeed but without accuracy and hence detection mechanism should be able to predict news fast enough to tackle the dissemination of fake news. It has the potential for negative impacts on individuals and society. Therefore, detecting fake news on social media is important and also a technically challenging problem these days. We knew that Machine learning is helpful for building Artificial intelligence systems based on tacit knowledge because it can help us to solve complex problems due to real word data. On the other side we knew that Knowledge engineering is helpful for representing experts knowledge which people aware of that knowledge. Due to this we proposed that integration of Machine learning and knowledge engineering can be helpful in detection of fake news. In this paper we present what is fake news, importance of fake news, overall impact of fake news on different areas, different ways to detect fake news on social media, existing detections algorithms that can help us to overcome the issue, similar application areas and at the end we proposed combination of data driven and engineered knowledge to combat fake news. We studied and compared three different modules text classifiers, stance detection applications and fact checking existing techniques that can help to detect fake news. Furthermore, we investigated the impact of fake news on society. Experimental evaluation of publically available datasets and our proposed fake news detection combination can serve better in detection of fake news.