# Buzz Tweet Classification Based on Text and Image Features of Tweets Using Multi-Task Learning
## 5 Results  
Table 4 shows the results of the predicted correct response rate of the buzz class for each image feature model obtained in the experiment, Table 5 shows the results of prediction using only text features, and Table 6 shows the results of prediction using only image features. Table 7 shows the results of prediction using single task learning model. Figure 4: Non-buzz tweet like distribution. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Buzz (True Value)** & **Non-Buzz (True Value)** \\ \hline Buzz (Predicted Value) & _TP_ (True Positive) & _FP_ (False Positive) \\ Non-buzz (Predicted Vue) & _FN_ (False Negative) & _TN_ (True Negative) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Confusion matrix of evaluation basis. \begin{table}
\begin{tabular}{c c} \hline
**Image Feature** & **Accuracy** \\ \hline DenseNet & 0.67 \\ InceptionResNetV2 & 0.49 \\ Inception V3 & 0.52 \\ NasNet & 0.59 \\ ResNet50 & 0.71 \\ VGG16 & 0.75 \\ Xception & 0.57 \\ \hline \end{tabular}
\end{table} 
Table 6: Comparison of accuracies for image features only. \begin{table}
\begin{tabular}{c c c} \hline
**Text Feature** & **Image Feature** & **Accuracy** \\ \hline DenseNet & 0.84 \\ InceptionResNetV2 & 0.82 \\ InceptionV3 & 0.83 \\ BERT & NasNet & 0.82 \\ ResNet50 & 0.82 \\ VGG16 & 0.84 \\ Xception & 0.82 \\ \hline \end{tabular}
\end{table} 
Table 8: AUC comparison for each image feature. \begin{table}
\begin{tabular}{c c} \hline
**Text Feature** & **Image Feature** & **Accuracy** \\ \hline DenseNet & 0.84 \\ InceptionResNetV2 & 0.75 \\ Inception V3 & 0.75 \\ ResNet & 0.82 \\ VGG16 & 0.83 \\ Xception & 0.68 \\ \hline \end{tabular}
\end{table} 
Table 7: Comparison of accuracy in the single task learning. \begin{table}
\begin{tabular}{c c} \hline
**Text Feature** & **Image Feature** & **Accuracy** \\ \hline DenseNet & 0.84 \\ InceptionResNetV2 & 0.82 \\ InceptionV3 & 0.83 \\ NASNet & 0.82 \\ ResNet50 & 0.82 \\ VGG16 & 0.84 \\ Xception & 0.82 \\ \hline \end{tabular}
\end{table} 
Table 8: AUC comparison for each image feature. \begin{table}
\begin{tabular}{c c} \hline
**Text Feature** & **Image Feature** & **Accuracy** \\ \hline DenseNet & 0.84 \\ InceptionResNetV2 & 0.82 \\ InceptionV3 & 0.83 \\ NASNet & 0.82 \\ ResNet50 & 0.82 \\ VGG16 & 0.84 \\ Xception & 0.82 \\ \hline \end{tabular}
\end{table} 
Table 9: Accuracy for text features only. These results show that when both text and image features are used as the input, there is little difference in the prediction accuracy rate, approximately 80%, irrespective of the type of image feature. When only text or only image features were used as input, the correct response rate of 0.82 was the highest prediction result, associated with text features alone. In contrast, when using only image features, there was a slight difference in the correct response rate for each image feature type. The lowest correct rate was 0.49 for InceptionResnetV2 and the highest was 0.75 for VGG16. As for the accuracy of the single-task results for predicting buzz/non-buzz, compared with the multi-task results, the accuracy decreased for all models. This confirms the effectiveness of multi-task learning. In particular, when Xception is used as an image feature, the accuracy of a single task changes significantly. Here, the probability that the tweets were misclassified when predicted using only image features was investigated by comparing them to the set of tweets that were misclassified when predicted using a combination of text and image features. The higher the probability that the misclassified tweets are included in the set of tweets predicted by a combination of text and image features, the more the image features contribute to the prediction. Let \(X\) be the set of misclassified tweets in the prediction of the combination of text features and image features, and \(Y\) be the set of misclassified tweets in the prediction of image features alone; the agreement rate \(MR_{error}\) of misclassification can be expressed as in Equation (2).