Yun-Yen Chuang
ABSTRACT
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.
- E. Kross, P. Verduyn, E. Demiralp, J. Park, D. S. Lee, N. Lin, et al., "Facebook use predicts declines in subjective well-being in young adults," PloS one, vol. 8, p. e69841, 2013. Google Scholar
- R. Junco and S. R. Cotten, "No A 4 U: The relationship between multitasking and academic performance," Computers & Education, vol. 59, pp. 505--514, 2012. Google ScholarDigital Library
- C. Guidances and T. Watch, "Social support and resilience to stress: from neurobiology to clinical practice," Psychiatry, vol. 4, pp. 35--40, 2007.Google Scholar
- C. Maier, S. Laumer, A. Eckhardt, and T. Weitzel, "WHEN SOCIAL NETWORKING TURNS TO SOCIAL OVERLOAD: EXPLAINING THE STRESS, EMOTIONAL EXHAUSTION, AND QUITTING BEHAVIOR FROM SOCIAL NETWORK SITES'USERS," 2012.Google Scholar
- D. Tang, B. Qin, and T. Liu, "Document Modeling with Gated Recurrent Neural Network for Sentiment Classification," in EMNLP, 2015, pp. 1422--1432.Google Scholar
- M. Denil, A. Demiraj, N. Kalchbrenner, P. Blunsom, and N. de Freitas, "Modelling, visualising and summarising documents with a single convolutional neural network," arXiv preprint arXiv:1406.3830, 2014.Google Scholar
- Y. Kim, "Convolutional neural networks for sentence classification," arXiv preprint arXiv:1408.5882, 2014.Google Scholar
- N. Kalchbrenner, E. Grefenstette, and P. Blunsom, "A convolutional neural network for modelling sentences," arXiv preprint arXiv:1404.2188, 2014.Google Scholar
- T. Mikolov, K. Chen, G. Corrado, and J. Dean, "Efficient estimation of word representations in vector space," arXiv preprint arXiv:1301.3781, 2013.Google Scholar
- Q. V. Le and T. Mikolov, "Distributed Representations of Sentences and Documents," in ICML, 2014, pp. 1188--1196.Google ScholarDigital Library
- J. Pennington, R. Socher, and C. D. Manning, "Glove: Global Vectors for Word Representation," in EMNLP, 2014, pp. 1532--1543.Google Scholar
- E. Grefenstette, P. Blunsom, N. de Freitas, and K. M. Hermann, "A deep architecture for semantic parsing," arXiv preprint arXiv:1404.7296, 2014.Google Scholar
- J. Chung, C. Gulcehre, K. Cho, and Y. Bengio, "Empirical evaluation of gated recurrent neural networks on sequence modeling," arXiv preprint arXiv:1412.3555, 2014.Google Scholar
- S. Ruder, "An overview of gradient descent optimization algorithms," arXiv preprint arXiv:1609.04747, 2016.Google Scholar
- T. Dozat, "Incorporating Nesterov momentum into Adam," Stanford University, Tech. Rep., 2015.[Online]. Available: http://cs229.stanford.edu/proj2015/054report.pdf2015.Google Scholar
- L. Prechelt, "Early stopping-but when?," in Neural Networks: Tricks of the trade, ed: Springer, 1998, pp. 55--69. Google ScholarCross Ref
- D. Erhan, Y. Bengio, A. Courville, P.-A. Manzagol, P. Vincent, and S. Bengio, "Why does unsupervised pre-training help deep learning?," Journal of Machine Learning Research, vol. 11, pp. 625--660, 2010.Google ScholarDigital Library
- C. Maier, S. Laumer, A. Eckhardt, and T. Weitzel, "Giving too much social support: social overload on social networking sites," European Journal of Information Systems, vol. 24, pp. 447--464, 2015. Google ScholarCross Ref
- P.-M. Su, "Predicting Web User's Tendency of Depression Using Event-Driven Negative Emotion Model," Paper of NCKU Institute of medical imformatics, pp. 1--84, 2012.Google Scholar
- C.-M. Tung and W.-H. Lu, "Analysis and Prediction of Blogger's Depression Tendency," in The 2015 Conference on Computational Linguistics and Speech Processing ROCLING, 2015, ed, 2015, pp. 263--276.Google Scholar
- D. P. Kingma and M. Welling, "Auto-encoding variational bayes," arXiv preprint arXiv:1312.6114, 2013.Google Scholar
- Rearrange Social Overloaded Posts to Prevent Social Overload
Recommendations
Why social network site use fails to promote well-being? The roles of social overload and fear of missing out
AbstractConsidering the popularity of social networking sites (SNSs) and the inconsistent results regarding the effect of SNS use on subjective well-being, this study intended to address the question “why SNS fails to predict subjective well-...
Highlights- There was no significant correlation between SNS use and subjective well-being.
Effects of social and technology overload on psychological well-being in young South Korean adults: The mediatory role of social network service addiction
AbstractThis study examined the effects of social and information technology overload on psychological well-being. It also explored the mediating role of social network service (SNS) addiction in the hypothesized relationships between these ...
Highlights- We explored the effect of social and technology overload on SNS addiction and psychological well-being.
The Implications of Social Influence Theory on Continuance Intention for Social Networking Among Chinese University Students
A better understanding of users' continuance intention towards social networking services SNSs gives SNS providers greater insight to reducing turnover rate of their users. Drawing on social influence theory, this study develops a theoretical model to ...
Comments