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Sentiment Based Information Diffusion in Online Social Networks

Sentiment Based Information Diffusion in Online Social Networks

Mohammad Ahsan, Madhu Kumari, Tajinder Singh, Triveni Lal Pal
Copyright: © 2018 |Volume: 8 |Issue: 1 |Pages: 15
ISSN: 1947-9115|EISSN: 1947-9123|EISBN13: 9781522544661|DOI: 10.4018/IJKDB.2018010105
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MLA

Ahsan, Mohammad, et al. "Sentiment Based Information Diffusion in Online Social Networks." IJKDB vol.8, no.1 2018: pp.60-74. http://doi.org/10.4018/IJKDB.2018010105

APA

Ahsan, M., Kumari, M., Singh, T., & Pal, T. L. (2018). Sentiment Based Information Diffusion in Online Social Networks. International Journal of Knowledge Discovery in Bioinformatics (IJKDB), 8(1), 60-74. http://doi.org/10.4018/IJKDB.2018010105

Chicago

Ahsan, Mohammad, et al. "Sentiment Based Information Diffusion in Online Social Networks," International Journal of Knowledge Discovery in Bioinformatics (IJKDB) 8, no.1: 60-74. http://doi.org/10.4018/IJKDB.2018010105

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Abstract

This article describes how social media has emerged as a main vehicle of information diffusion among people. They often share their experience, feelings and knowledge through these channels. Some pieces of information quickly reach a large number of people, while others not. The authors analyzed this variation by collecting tweets on 2016 U.S. presidential election. This article gives a comprehensive understanding of how sentiment encoded in the textual contents can affects the information diffusion, along with the effect of content features, i.e., URLs, hashtags, and contextual features, i.e., number of followers, followees, tweets generated by the user so far, account age, tweet age. In order to explore the relationship between sentiment content and information diffusion, the authors first checked the features' significance as an indicator of diffusibility by using random forests. Finally, support vectors and k-Neighbors regression models are used to capture the complete dynamics of information diffusion. Experiments and results clearly reveal that sentiment prominently helps in making a better prediction of information diffusion.

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