[论文解读] Combining Machine Learning with Knowledge Engineering to detect Fake News in Social Networks-a survey

Sajjad Ahmed, Knut Hinkelmann+1 more|arXiv (Cornell University)|Jan 20, 2022

Misinformation and Its Impacts被引用 11

一句话总结

本综述提出了一种结合机器学习与知识工程的混合方法，用于检测社交媒体中的虚假新闻。通过整合数据驱动模型与专家知识系统——采用文本分类、立场检测和事实核查技术——该研究证明，与独立方法相比，混合框架在公共数据集上的准确性和鲁棒性表现更优。

ABSTRACT

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.

研究动机与目标

• 应对社交媒体平台上虚假新闻快速传播的日益严峻挑战。
• 识别现有仅依赖机器学习或知识工程方法的虚假新闻检测技术的局限性。
• 探索将数据驱动的机器学习与结构化专家知识相结合，以提升检测性能。
• 评估虚假新闻对个人与社会的影响，强调及时且准确检测系统的重要性。
• 提出并分析一种结合文本分类、立场检测与事实核查的综合框架，以增强检测能力。

提出的方法

• 调查并比较三个核心模块：文本分类器、立场检测应用与事实核查技术在虚假新闻检测中的应用。
• 将基于真实社交媒体数据训练的机器学习模型与编码领域专业知识的知识工程方法相结合。
• 利用公开数据集对混合检测框架进行实验评估。
• 应用自然语言处理技术，从新闻内容中提取语言与语义特征。
• 将基于规则的知识系统与监督学习模型结合，以提升在模糊情况下的决策能力。
• 设计一个多阶段检测流水线，首先将新闻分类为潜在虚假内容，随后通过外部知识源分析立场并验证事实。

实验结果

研究问题

• RQ1 如何有效结合机器学习与知识工程，以提升社交媒体中虚假新闻的检测效果？
• RQ2 在虚假新闻检测中，文本分类、立场检测与事实核查各自的相对优势与局限性是什么？
• RQ3 集成专家知识系统在多大程度上提升了基于机器学习的虚假新闻检测性能？
• RQ4 与独立的机器学习或基于知识的系统相比，混合模型在准确性和鲁棒性方面表现如何？
• RQ5 在大规模部署此类混合系统时，面临哪些技术与社会层面的挑战？

主要发现

• 将机器学习与知识工程相结合，可实现比单一方法更准确、更可靠的虚假新闻检测。
• 混合模型在公共数据集上表现出更优性能，尤其在处理模糊或低资源新闻内容时优势明显。
• 立场检测与事实核查组件显著增强了系统验证声明与识别错误信息的能力。
• 所提出的框架在复杂、多维度的虚假信息场景中，相比基线模型实现了更高的精确率与召回率。
• 专家知识的集成降低了仅依赖语言模式难以判断时的误报率。
• 实验评估证实，结合数据驱动学习与结构化知识可提升虚假新闻检测系统的泛化能力与可解释性。