Advanced Machine Learning Techniques for Social Support Detection on Social Media

TL;DR

This paper applies advanced machine learning techniques, including transformers and zero-shot learning, to classify and analyze social support content on social media, improving accuracy over traditional methods.

Contribution

It introduces a multi-task classification framework for social support detection using transformers and zero-shot learning, addressing data imbalance with K-means clustering.

Findings

Transformer-based models outperform baseline methods.

0.4% increase in macro F1 for support directed at individuals or groups.

0.7% increase in macro F1 for categorizing social support types.

Abstract

The widespread use of social media highlights the need to understand its impact, particularly the role of online social support. This study uses a dataset focused on online social support, which includes binary and multiclass classifications of social support content on social media. The classification of social support is divided into three tasks. The first task focuses on distinguishing between supportive and non-supportive. The second task aims to identify whether the support is directed toward an individual or a group. The third task categorizes the specific type of social support, grouping it into categories such as Nation, LGBTQ, Black people, Women, Religion, and Other (if it does not fit into the previously mentioned categories). To address data imbalances in these tasks, we employed K-means clustering for balancing the dataset and compared the results with the original unbalanced data. Using advanced machine learning techniques, including transformers and zero-shot learning approaches with GPT3, GPT4, and GPT4-o, we predict social support levels in various contexts. The effectiveness of the dataset is evaluated using baseline models across different learning approaches, with transformer-based methods demonstrating superior performance. Additionally, we achieved a 0.4\% increase in the macro F1 score for the second task and a 0.7\% increase for the third task, compared to previous work utilizing traditional machine learning with psycholinguistic and unigram-based TF-IDF values.