Online Social Support Detection in Spanish Social Media Texts

TL;DR

This paper presents a new dataset and methods for detecting online social support in Spanish social media comments, showing that dataset balancing improves classification performance and highlighting GPT-4o's effectiveness.

Contribution

Introduces the first annotated dataset for Spanish social support detection and compares traditional, deep learning, and transformer models, emphasizing the impact of dataset balancing.

Findings

Balanced dataset improves classification accuracy for most tasks.

GPT-4o performs best on support detection in unbalanced data.

Transformer models outperform traditional machine learning methods.

Abstract

The advent of social media has transformed communication, enabling individuals to share their experiences, seek support, and participate in diverse discussions. While extensive research has focused on identifying harmful content like hate speech, the recognition and promotion of positive and supportive interactions remain largely unexplored. This study proposes an innovative approach to detecting online social support in Spanish-language social media texts. We introduce the first annotated dataset specifically created for this task, comprising 3,189 YouTube comments classified as supportive or non-supportive. To address data imbalance, we employed GPT-4o to generate paraphrased comments and create a balanced dataset. We then evaluated social support classification using traditional machine learning models, deep learning architectures, and transformer-based models, including GPT-4o, but only on the unbalanced dataset. Subsequently, we utilized a transformer model to compare the performance between the balanced and unbalanced datasets. Our findings indicate that the balanced dataset yielded improved results for Task 2 (Individual and Group) and Task 3 (Nation, Other, LGBTQ, Black Community, Women, Religion), whereas GPT-4o performed best for Task 1 (Social Support and Non-Support). This study highlights the significance of fostering a supportive online environment and lays the groundwork for future research in automated social support detection.