This study proposes a framework that enhances author profiling (AP) performance by using backtranslation and expansion modules. The framework translates an author's text into another language and then back to the original, then expands the text by combining the original and back-translated versions. This enriched text is then classified using a state-of-the-art classifier. The framework is tested on three AP datasets from shared tasks on fake news, hate speech, irony, and stereotypes detection at the CLEF conference. The results show that backtranslation and expansion modules improve performance on all three datasets. The framework is evaluated using three stages: establishing a baseline with no augmentation, generating augmented data using backtranslation, and training a classifier on the enriched data. The results confirm that the backtranslation and expansion modules improve model performance on all three datasets. The framework is tested on three different AP datasets: fake news, hate speech, and irony and stereotypes spreaders. The results show that the framework outperforms the baseline, demonstrating that backtranslation and expansion improve AP performance. The framework is implemented using Python and TensorFlow, and the code is available on GitHub. The study also discusses related work on data augmentation and author profiling, and presents the experimental setup, results, and discussion. The results show that the backtranslation and expansion modules improve performance on all three datasets. The study concludes that the framework improves AP performance and that further research is needed to explore the effectiveness of the framework on other datasets and tasks.This study proposes a framework that enhances author profiling (AP) performance by using backtranslation and expansion modules. The framework translates an author's text into another language and then back to the original, then expands the text by combining the original and back-translated versions. This enriched text is then classified using a state-of-the-art classifier. The framework is tested on three AP datasets from shared tasks on fake news, hate speech, irony, and stereotypes detection at the CLEF conference. The results show that backtranslation and expansion modules improve performance on all three datasets. The framework is evaluated using three stages: establishing a baseline with no augmentation, generating augmented data using backtranslation, and training a classifier on the enriched data. The results confirm that the backtranslation and expansion modules improve model performance on all three datasets. The framework is tested on three different AP datasets: fake news, hate speech, and irony and stereotypes spreaders. The results show that the framework outperforms the baseline, demonstrating that backtranslation and expansion improve AP performance. The framework is implemented using Python and TensorFlow, and the code is available on GitHub. The study also discusses related work on data augmentation and author profiling, and presents the experimental setup, results, and discussion. The results show that the backtranslation and expansion modules improve performance on all three datasets. The study concludes that the framework improves AP performance and that further research is needed to explore the effectiveness of the framework on other datasets and tasks.