Rearrange Social Overloaded Posts to Prevent Social Overload
| Autor: | Ray-I Chang, Tsui-Ying Lin, Hung-Min Hsu, Yun-Yen Chuang |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Social network
business.industry media_common.quotation_subject Deep learning Internet privacy Sentiment analysis 02 engineering and technology 030227 psychiatry 03 medical and health sciences Social support 0302 clinical medicine 020204 information systems Reading (process) 0202 electrical engineering electronic engineering information engineering Social media Artificial intelligence business Psychology Social psychology media_common |
| Zdroj: | ASONAM |
| DOI: | 10.1145/3110025.3110078 |
| Popis: | According to the latest investigation, there are 1.7 million active social network users in Taiwan. Previous researches indicated social network posts have a great impact on users, and mostly, the negative impact is from the rising demands of social support, which further lead to heavier social overload. In this study, we propose social overloaded posts detection model (SODM) by deploying the latest text mining and deep learning techniques to detect the social overloaded posts and, then with the developed social overload prevention system (SOS), the social overload posts and non-social overload ones are rearranged with different sorting methods to prevent readers from excessive demands of social support or social overload. The empirical results show that our SOS helps readers to alleviate social overload when reading via social media. |
| Databáze: | OpenAIRE |
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