Summary of Social Support Detection From Social Media Texts, by Zahra Ahani et al.
Social Support Detection from Social Media Texts
by Zahra Ahani, Moein Shahiki Tash, Fazlourrahman Balouchzahi, Luis Ramos, Grigori Sidorov, Alexander Gelbukh
First submitted to arxiv on: 4 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Databases (cs.DB); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces Social Support Detection (SSD), a Natural language processing (NLP) task that identifies supportive interactions within online communities, fostering a sense of belonging and resilience. The SSD task is divided into three subtasks: binary classification tasks for individual-focused support and group-oriented support, and a multiclass task to identify different types of support. Experiments were conducted on a dataset of 10,000 YouTube comments using traditional machine learning models and neural network-based models with various word embeddings. The results demonstrate the effectiveness of integrating psycholinguistic, emotional, and sentiment features with n-grams in detecting social support, with accuracy ranging from 0.72 to 0.82 for different subtasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to understand how people help each other online, called Social Support Detection (SSD). SSD looks at conversations on websites and apps like YouTube to see if people are being kind and supportive to others. The researchers used special computer programs to analyze these conversations and found that people often help groups of people rather than just one person. They also discovered that combining different types of information, like how people feel and what they’re saying, helps the computer programs understand social support better. Overall, this research can help us understand how people interact online and make the internet a more supportive place. |
Keywords
» Artificial intelligence » Classification » Machine learning » Natural language processing » Neural network » Nlp
