# Hybrid RFSVM: Hybridization of SVM and Random Forest Models for Detection of Fake News
## 4 Conclusions  
In the current social media era, fake news detection is a rapidly emerging topic. The literature surveyed here addresses various research gaps identified on the web and media platforms. The authors have surveyed, summarized, compared, and evaluated the ongoing research on fake news which includes various perspectives on fake news. Various evaluation parameters, such as precision, recall, accuracy, and F1-score have been calculated on different machine-learning classifiers by applying the TFIDF feature set. The authors have presented that hybrid RFSVM outperformed the best among others on the basis of different evaluation parameters. In our approach, currently RF is applied and further SVMs for comparisons within the class. In future, a hybridization of RF and SVMs will be applied on the large datasets along with the integration of more modern techniques such as Transformer based models which can further be used to strengthen the comparisons, which further improve the efficiency and effectiveness of the model. In our proposed model as size of the dataset is not to large, which enable us to provide effective, accurate and fast results using hybridization. Hence, modern techniques such as transformer-based model were not used to strengthen the comparisons. In future research, more focus can be on diversified and labeled datasets. Fake news detection at the initial stage, fake news detection in different languages in cross-platform, and hybridization of various intelligent algorithms can be carried out for better results, dynamic and benchmark datasets are major challenges in domain of false news identification that can be used for potential research opportunities. Images containing fake text is also most promising area as future research.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Category**} & **Avg.** & **Avg.** & **Avg.** & **Avg.** & **Avg.** & **Reference** \\  & **F1-Score** & **F1-score** & **F1-Score** & **F1-Score** & **F1-Score** & **F1-Score** \\  & **[NB]** & **[XGBoost]** & **[RF]** & **[SVMs]** & **[RFSVM]** & **[RFSVM]** \\ \hline Agriculture & 0.59 & 0.61 & 0.63 & 0.67 & 0.75 & 0.75 \\ \hline Aviation & 0.32 & 0.34 & 0.36 & 0.45 & 0.59 & 0.61 \\ \hline Sports & 0.54 & 0.56 & 0.58 & 0.64 & 0.77 & 0.76 \\ \hline Roads & 0.19 & 0.21 & 0.23 & 0.28 & 0.34 & 0.49 \\ \hline Residential & 0.35 & 0.37 & 0.39 & 0.43 & 0.51 & 0.51 \\ \hline Forest & 0.56 & 0.58 & 0.60 & 0.60 & 0.72 & 0.69 \\ \hline Village & 0.37 & 0.39 & 0.41 & 0.50 & 0.59 & 0.59 \\ \hline Finance & 0.67 & 0.69 & 0.71 & 0.75 & 0.85 & 0.82 \\ \hline Politics & 0.67 & 0.69 & 0.71 & 0.71 & 0.74 & 0.78 \\ \hline Technology & 0.40 & 0.42 & 0.44 & 0.52 & 0.56 & 0.57 \\ \hline \end{tabular}
\end{table}
Table 9: F1-score values for different news category with different ML algorithms.  
**Author Contributions:** Conceptualization, D.G.D. and V.B.; methodology, D.G.D. and V.B.; software, D.G.D. and V.B.; validation, D.G.D. and V.B.; formal analysis, D.G.D. and V.B.; investigation, D.G.D. and V.B.; resources, D.G.D. and V.B.; data curation, D.G.D. and V.B.; writing--original draft preparation, D.G.D. and V.B.; writing--review and editing, D.G.D. and V.B.; visualization, D.G.D. and V.B.; supervision, D.G.D. and V.B.; project administration, D.G.D. and V.B.; funding acquisition, D.G.D. and V.B. All authors have read and agreed to the published version of the manuscript.  
**Funding:** All authors declare that there is no funding available for this article.  
**Data Availability Statement:** Data are available for this article whenever required.  
**Acknowledgments:** The authors are thankful to the learned reviewer for his valuable comments.  
**Conflicts of Interest:** The authors declare that they have no competing interests.