harnessing generative ai for overcoming labeled data challenges in social media nlp
abstract
with the introduction of transformers and large language models, the field of nlp has significantly evolved. generative ai, a prominent transformer-based technology for crafting human-like
content, has proven powerful skills across numerous nlp tasks. simultaneously, social media
emerges as a rich source for nlp explorations, offering vast and diverse datasets that capture
real-time language usage, making it a valuable resource for understanding and advancing nlp
techniques. given that supervised learning is the most popular machine learning training method,
numerous nlp studies necessitate labor-intensive annotation of social media text. however, despite the large amount of data available, the social media data annotation process is usually difficult
for human experts due to unique characteristics of text, such as shortness, lack of context, embedded socio-cultural perspectives, and varied writing styles. the challenges in constructing labeled
social media datasets often result in a scarcity of labeled data and the generation of low-quality
labels. moreover, these datasets frequently face class imbalance due to the limitations of labeled
samples. hence, ensuring a balanced, high-quality dataset in sufficient quantities is crucial for the
robust and accurate development of nlp models. to address these challenges, this study has identified the usage of generative ai for social media labeled text generation. specifically, this study
focuses on two key objectives: augmenting existing labeled text samples and annotating unlabeled
text samples using generative ai. as the generative ai technology, the generative pre-trained
transformer model, a prevalent choice for ai-based content generation is employed in different
versions throughout the study and evaluated its performance against traditional text augmentation
and annotation methods. while both studies centered around multi-class classification problems,
the text augmentation approach delves into augmenting human wellness dimensions using reddit
posts, and text annotation tackles stance detection on abortion legalization using twitter posts. by
employing various classifiers, the subsequent investigations aim to enhance classification performance in social media nlp, emphasizing the common goal of expanding labeled datasets, while
enhancing the quality of labels.