# Numbers Do Not Lie: A Bibliometric Examination of Machine Learning Techniques in Fake News Research
## 3 Dataset Analysis
### Literature Analysis
#### 3.4.3 Word Analysis
In second and the third place for those found in abstracts are \"natural language processing\", with 61 occurrences and \"detect fake news\", with 60 occurrences, while, in titles, \"fake news classification\" has 13 occurrences and \"COVID-fake news\" has 9 occurrences. Kindly ask you to consult Table 16 for more details. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Bigrams in Abstracts** & **Occurrences** & **Bigrams in Titles** & **Occurrences** \\ \hline fake news & 994 & fake news & 201 \\ social media & 319 & news detection & 108 \\ machine learning & 253 & deep learning & 49 \\ news detection & 217 & social media & 36 \\ deep learning & 180 & machine learning & 33 \\ learning models & 90 & news classification & 14 \\ neural network & 84 & learning approach & 10 \\ natural language & 70 & natural language & 10 \\ detect fake & 63 & COVID-fake & 9 \\ language processing & 61 & deep neural & 9 \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Top 10 most frequent bigrams in abstracts and titles. Figure 15: Top 50 words based on keywords plus (**A**) and authors’ keywords (**B**). With the goal to address and highlight the subject of fake news in a pandemic situation, bigrams and trigrams are revealing fascinating details about themes and trends. Figure 16 represents a graphical view of the co-occurrence network for the terms in author's keywords. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Trigrams in Abstracts** & **Occurrences** & **Trigrams in Titles** & **Occurrences** \\ \hline fake news detection & 214 & fake news detection & 108 \\ natural language & 61 & fake news classification & 13 \\ processing & 60 & COVID-fake news & 9 \\ detect fake news & 51 & automatic fake news & 8 \\ social media platforms & 43 & natural language & 8 \\ deep learning models & 43 & processing & 8 \\ detecting fake news & 41 & Arabic fake news & 6 \\ machine learning models & 41 & deep learning model & 6 \\ machine learning & 39 & combating fake news & 5 \\ algorithms & 29 & deep learning approach & 5 \\ support vector machine & 26 & deep learning framework & 5 \\ convolutional neural & 26 & deep learning framework & 5 \\ network & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 16: Top 10 most frequent trigrams in abstracts and titles. Figure 16: Co-occurrence network for the terms in authors’ keywords. Based on the information in Figure 16, four clusters were delimited:  
* Cluster 1--red: fake news, machine learning, social media, COVID-19, misinformation, twitter, text classification, social networks, classification, disinformation, data mining, logistic regression, credibility, fact checking, fake news classification, convolutional neural network, false information, natural language processing (nlp), and neural network;
* Cluster 2--blue: deep learning, fake news detection, natural language processing, neural networks, bert, sentiment analysis, rumor detection, stance detection, cnn, convolutional neural networks, lstm, text mining, rumor, and coronavirus;
* Cluster 3--green: feature extraction, social networking (online), blogs, transfer learning, transformers, misinformation detection, text analysis, deep neural networks, data models, Support Vector Machines, analytical models, bit error rate, linguistics, and machine learning (ml);
* Cluster 4--orange: artificial intelligence and deepfakes. Figure 17 sheds light on the thematic map based on the authors' keywords, from which four main themes can be outlined: niche themes, motor themes, emerging or declining themes, and basic themes. The motor themes include feature extraction, social networking (online), and blogs, along with data mining, deepfake, and deepfakes, which was placed at the border between motor and niche themes. Next on the list are basic themes, which are comprised of fake news, deep learning, machine learning, COVID-19, text classification, and artificial intelligence.