# Numbers Do Not Lie: A Bibliometric Examination of Machine Learning Techniques in Fake News Research
## 3 Dataset Analysis
### Literature Analysis
#### 3.4.2 The Top 10 Most Cited Papers--A Review
In order to deal with the pressing issue of false news identification, the study carried out by [PERSON] et al. (2018) presents and evaluates a unique deep learning model called FNDNet. Apart from releasing an innovative technique, the investigation aims to progress within the subject by evaluating the model's effectiveness against the state-of-the-art methods now in use. The Kaggle news dataset, which is linked to the 2016 US presidential election, is used to assess these findings. From a personal perspective, this article registered an impressive number of citations, since it addresses an interesting topic in the area of machine learning techniques in fake news research, and intensely examines the differences between numerous deep learning and machine learning algorithms, including a detailed analysis of each and a discussion of the benefits and drawbacks. The research's outcomes demonstrate how effective FNDNet, the suggested deep learning model, is at identifying fake news, being classified as the most accurate model evaluated, with an incredible 98.36% accuracy rate. Regarding the strengths of this work, it is noticed that the study highlights the critical role that state-of-the-art natural language processing techniques--particularly deep learning--have in improving the accuracy of fake news detection, information that is very useful for future research directives ([PERSON] et al., 2018). The research by [PERSON] et al. (2018) investigates the approach of deepfakes using autoencoder architecture and latent space representation. Key ideas include comprehending approaches, data complexity, and consequences for individuals and groups. The R.E.A.L. risk management framework is its primary proposal. The study conducted in this article is interesting, well organized, provides illustrative examples with explanations, and proves that deepfakes are problematic as they have the possibility to offer personalized entertainment choices but also privacy concerns and threats like online abuse. In order to address deepfake concerns, the report emphasizes the need for powerful partnerships between brands and legislative actions ([PERSON] et al., 2018). The study conducted by [PERSON] et al. (2018) offers a comprehensive analysis of stance detection, an essential aspect of natural language processing, with an emphasis on automatically ascertaining an author's stance in relation to a certain target. Position sensing investigations are categorized into several methodologies in the survey, along with applications, tools, and problems. It particularly draws a distinction between programs that use specific position detection modules and those that use general machine learning platforms. The research presented includes anything from opinion surveys to the categorization of rumors and the identification of fake news. In terms of methodology, a variety of machine learning libraries and tools are employed, including scikit-learn, Keras, Theano, and Gensim, as well as SVM in the Weka toolkit. According to the research, position-sensing breakthroughs are facilitated by the development and distribution of source codes. Furthermore, apart from the aforementioned aspects, another strength of the research is that it specifies other domains where location sensing may be applied, including policy discussions, product assessments, and public health monitoring, providing, as well, valuable insights. As for future studies, some notable opportunities consist of context-sensitive tech niques, examining post-detection in various contexts and cross-linguistic and multilingual stance detection. Future researchers may start their work by reading this comprehensive article that brings to the fore significative details about this area ([PERSON] et al., 2020). Table 12 provides a summary of the works discussed above. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**No.