# A Comprehensive Approach to Bias Mitigation for Sentiment Analysis of Social Media Data
## 3 Proposed Methodology
### Data Preprocessing
* We also checked the bias-specific metrics to see how well the model reduced bias across different demographic groups after including feedback. * Finally, we performed statistical tests, including t-tests, to determine if the improvements were significant. The results showed a clear improvement in the model's overall performance once the user feedback was factored in. Table 6 provides a summary of the metrics before and after feedback integration. The \(p\)-values of the t-tests made it pretty clear that the improvements across all the key metrics--such as accuracy, precision, recall, and F1-score--were statistically significant. This really emphasized how effective the user feedback loop was in improving the model performance, while also helping to reduce bias. By integrating real-world feedback, the model was not just becoming more accurate; it was also learning to be more fair in how it handled different groups, which is crucial for addressing any inherent biases. #### 4.10.2 Longitudinal Study: Impact of User Feedback over Time

To evaluate the impact of the user feedback loop on the performance of the model, we conducted a six-month longitudinal study. This study aimed to track improvements in performance and bias reduction metrics as the model was retrained with collected feedback data over time. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Metric** & **Before Feedback** & **After Feedback** & **Improvement (\%)** & \(p\)**-Value** \\ \hline Accuracy & 0.925 & 0.975 & 5.41\% & 0.01 \\ Precision & 0.932 & 0.980 & 5.15\% & 0.02 \\ Recall & 0.921 & 0.970 & 5.32\% & 0.01 \\ F1-score & 0.926 & 0.975 & 5.29\% & 0.01 \\ Bias metric (KL) & 0.150 & 0.100 & \(-\)33.33\% & 0.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance metrics before and after user feedback integration. \begin{table}
\begin{tabular}{l c} \hline \hline
**Model** & **MCC** \\ \hline Logistic Regression & 0.45 \\ SVM & 0.50 \\ MLP & 0.53 \\ LSTM & 0.58 \\ Counterfactual data augmentation & 0.62 \\ Adversarial training & 0.65 \\ Bias-Product-of-Experts (BPoE) & 0.63 \\ Bias-Regularized Neural Network & 0.66 \\ RoBERTa & 0.80 \\ DistilBERT & 0.78 \\ BiasFinder & 0.76 \\ Proposed method & 0.85 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of Matthews Correlation Coefficient (MCC) for the proposed method, baseline models, and existing models in the literature. Data Collection and Labeling

During the study, approximately 15,000 data points were collected monthly, resulting in a total of 90,000 instances over six months. The data were gathered from publicly available online forums, moderated to ensure ethical compliance, and anonymized to maintain user privacy. The feedback data were labeled by a team of domain experts and crowd-sourced annotators using standardized guidelines to ensure high-quality annotations. Methodology

The study followed three primary steps:

1. Data collection: performance and bias metrics, including accuracy, precision, recall, F1-score, and KL divergence, were recorded at monthly intervals. 2. Trend analysis: changes in the metrics were analyzed over time to observe patterns of improvement and bias reduction. 3. Consistency check: statistical tests were performed to ensure that the observed trends were significant and sustained over the study period. Figure 4 illustrates the evolution of key performance metrics--accuracy, precision, recall, and F1-score--plotted on the left axis, and the bias metric (KL divergence) on the right axis. Findings

The results showed a consistent improvement in all performance metrics, with accuracy increasing from just above 92% to almost 98% at the end of the study. Precision, recall, and F1-score followed similar upward trends, indicating improved prediction quality. Simultaneously, the bias metric (KL divergence) exhibited a steady decline, starting above 0.15 and decreasing to approximately 0.10, underscoring the effectiveness of the feedback loop in reducing bias.