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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.<\/jats:p>","DOI":"10.4018\/ijkdb.2018010105","type":"journal-article","created":{"date-parts":[[2018,3,14]],"date-time":"2018-03-14T09:42:39Z","timestamp":1521020559000},"page":"60-74","source":"Crossref","is-referenced-by-count":3,"title":["Sentiment Based Information Diffusion in Online Social Networks"],"prefix":"10.4018","volume":"8","author":[{"given":"Mohammad","family":"Ahsan","sequence":"first","affiliation":[{"name":"National Institute of Technology, Hamirpur, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Madhu","family":"Kumari","sequence":"additional","affiliation":[{"name":"National Institute of Technology, Hamirpur, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tajinder","family":"Singh","sequence":"additional","affiliation":[{"name":"National Institute of Technology, Hamirpur, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Triveni Lal","family":"Pal","sequence":"additional","affiliation":[{"name":"National Institute of Technology, Hamirpur, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2432","reference":[{"key":"IJKDB.2018010105-0","doi-asserted-by":"publisher","DOI":"10.1016\/j.comcom.2015.09.028"},{"key":"IJKDB.2018010105-1","doi-asserted-by":"publisher","DOI":"10.1016\/j.comcom.2013.03.003"},{"key":"IJKDB.2018010105-2","doi-asserted-by":"publisher","DOI":"10.1145\/2187836.2187907"},{"key":"IJKDB.2018010105-3","doi-asserted-by":"publisher","DOI":"10.1037\/1089-2680.5.4.323"},{"key":"IJKDB.2018010105-4","doi-asserted-by":"publisher","DOI":"10.1509\/jmr.10.0353"},{"key":"IJKDB.2018010105-5","author":"M.Bonzanini","year":"2016","journal-title":"Mastering Social Media Mining with Python"},{"key":"IJKDB.2018010105-6","doi-asserted-by":"crossref","first-page":"925","DOI":"10.1145\/2566486.2567997","article-title":"Can Cascades be Predicted?","author":"J.Cheng","year":"2014","journal-title":"Proceedings of the 23rd International Conference on World Wide Web (WWW-14)"},{"key":"IJKDB.2018010105-7","doi-asserted-by":"publisher","DOI":"10.1109\/PASSAT\/SocialCom.2011.34"},{"issue":"3","key":"IJKDB.2018010105-8","article-title":"Approaches, Tools and Applications for Sentiment Analysis Implementation.","volume":"125","author":"A.D\u2019Andrea","year":"2015","journal-title":"International Journal of Computers and Applications"},{"key":"IJKDB.2018010105-9","doi-asserted-by":"crossref","unstructured":"Ferrara, E., & Yang, Z. 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