# A Survey of Social Cybersecurity: Techniques for Attack Detection, Evaluations, Challenges, and Future Prospects

arXiv: https://arxiv.org/abs/2504.04311 · HTML: https://mlsanity.com/paper/2504.04311/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 325 references that are in the map.
- First posted on arXiv 6 Apr 2025.

## Builds on (most cited first, 30 of 31)

- [Ethical and social risks of harm from Language Models](https://mlsanity.com/paper/2112.04359/) (2021)
- [Defending Against Neural Fake News](https://mlsanity.com/paper/1905.12616/) (2019)
- [Fake News Detection on Social Media: A Data Mining Perspective](https://mlsanity.com/paper/1708.01967/) (2017)
- [FakeNewsNet: A Data Repository with News Content, Social Context and Spatialtemporal Information for Studying Fake News on Social Media](https://mlsanity.com/paper/1809.01286/) (2018)
- [DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection](https://mlsanity.com/paper/2001.00179/) (2020)
- [Media Forensics and DeepFakes: an overview](https://mlsanity.com/paper/2001.06564/) (2020)
- [Principles alone cannot guarantee ethical AI](https://mlsanity.com/paper/1906.06668/) (2019)
- [Artificial Intelligence: the global landscape of ethics guidelines](https://mlsanity.com/paper/1906.11668/) (2019)
- [The Rise of Social Bots](https://mlsanity.com/paper/1407.5225/) (2014)
- [TwiBot-20: A Comprehensive Twitter Bot Detection Benchmark](https://mlsanity.com/paper/2106.13088/) (2021)
- [Detecting Hate Speech in Social Media](https://mlsanity.com/paper/1712.06427/) (2017)
- [BotRGCN: Twitter Bot Detection with Relational Graph Convolutional Networks](https://mlsanity.com/paper/2106.13092/) (2021)
- [Universal Perturbation Attack Against Image Retrieval](https://mlsanity.com/paper/1812.00552/) (2018)
- [QAIR: Practical Query-efficient Black-Box Attacks for Image Retrieval](https://mlsanity.com/paper/2103.02927/) (2021)
- [The History of Digital Spam](https://mlsanity.com/paper/1908.06173/) (2019)
- [The Four Dimensions of Social Network Analysis: An Overview of Research Methods, Applications, and Software Tools](https://mlsanity.com/paper/2002.09485/) (2020)
- [Manipulation and abuse on social media](https://mlsanity.com/paper/1503.03752/) (2015)
- [Crowdsourcing Cybersecurity: Cyber Attack Detection using Social Media](https://mlsanity.com/paper/1702.07745/) (2017)
- [Analyzing the Perceived Severity of Cybersecurity Threats Reported on Social Media](https://mlsanity.com/paper/1902.10680/) (2019)
- [Cyberthreat Detection from Twitter using Deep Neural Networks](https://mlsanity.com/paper/1904.01127/) (2019)
- [Agent-based computing from multi-agent systems to agent-based Models: a visual survey](https://mlsanity.com/paper/1708.05872/) (2017)
- [A Comparative Study of Vulnerability Reporting by Software Composition Analysis Tools](https://mlsanity.com/paper/2108.12078/) (2021)
- [Friend or Foe? Fake Profile Identification in Online Social Networks](https://mlsanity.com/paper/1303.3751/) (2013)
- [Gathering Cyber Threat Intelligence from Twitter Using Novelty Classification](https://mlsanity.com/paper/1907.01755/) (2019)
- [Discovering Signals from Web Sources to Predict Cyber Attacks](https://mlsanity.com/paper/1806.03342/) (2018)
- [Deep Learning Approach for Enhanced Cyber Threat Indicators in Twitter Stream](https://mlsanity.com/paper/2004.00503/) (2020)
- [Vulnerabilities Mapping based on OWASP-SANS: a Survey for Static Application Security Testing (SAST)](https://mlsanity.com/paper/2004.03216/) (2020)
- [Cybersecurity Dynamics: A Foundation for the Science of Cybersecurity](https://mlsanity.com/paper/2010.05683/) (2020)
- [CovidMis20: COVID-19 Misinformation Detection System on Twitter Tweets using Deep Learning Models](https://mlsanity.com/paper/2209.05667/) (2022)
- [Detecting cyber threats through social network analysis: short survey](https://mlsanity.com/paper/1805.06680/) (2018)

## Built on by (most cited first, 1 of 1)

- [A Framework for Using and Evaluating LLMs as Surrogate Experts in Security Surveys: Reliability, Bias, and Implications](https://mlsanity.com/paper/2608.16893/) (2026)

## Abstract (arXiv)

In today's digital era, the Internet, especially social media platforms, plays a significant role in shaping public opinions, attitudes, and beliefs. Unfortunately, the credibility of scientific information sources is often undermined by the spread of misinformation through various means, including technology-driven tools like bots, cyborgs, trolls, sock-puppets, and deep fakes. This manipulation of public discourse serves antagonistic business agendas and compromises civil society. In response to this challenge, a new scientific discipline has emerged: social cybersecurity.