Table 1.
Recent work related to propaganda.
Fig 1.
Proposed framework for propaganda identification.
Fig 2.
Overall flowchart of the process for identifying propaganda.
Fig 3.
Framework for data extraction from Twitter.
Fig 4.
Proposed annotation scheme using various propaganda techniques.
Fig 5.
Labeled dataset with their corresponding lengths.
Fig 6.
Wordcloud of propaganda text.
Fig 7.
Wordcloud of non-propaganda tex.
Fig 8.
Pictorial representation of various steps in data preprocessing.
Table 2.
Feature statistical analysis.
Table 3.
Features chosen based on hybrid feature engineering.
Fig 9.
Confusion metrics using LR-HaPi algorithm.
Fig 10.
Confusion metrics using MNB-HaPi algorithm.
Fig 11.
Confusion metrics using SVM-HaPi algorithm.
Fig 12.
Confusion metrics using DT-HaPi algorithm.
Table 4.
Classification report based on HaPi.
Fig 13.
Performance of different HaPi-based machine learning classifiers.
Table 5.
Five fold cross validation.
Fig 14.
5-Fold cross validation.
Table 6.
Comparison of proposed approaches with the existing studies.
Fig 15.
Comparison of the proposed approach with existing studies.