Discovering Magnification Rate Of News Flow Control Pattern Using Diffusion Model
Volume 5 - Issue 5, May 2022 Edition
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Naman Jain, Shreya Singh, Rahul Anand
Virality Detection, Diffusion, Recommendation System SI model, Natural language Processing, Social Networks, News Spread Networks.
In the hour of competition, affiliations advance their things and augmentation their pay by taking advantage of their by and large open data. We can achieve this by perceiving the tendencies of per client for models and news type in news spread framework for virality area. Getting out the word over the web that in the end sets off the advancement of momentary frameworks is apparently an incessant method .This concise framework incorporates center points and edges, where centers are insinuated as disseminated articles and relative articles are related through edges. The huge point of convergence of this article is to revamp a frail spoiled "Helpless Infected scattering" model that will track down the spreading illustration of reports. For test examination, the dataset of reports is considered from four spaces (business, advancement, entertainment, and prosperity) and the speed of scattering of articles' is prompted and checked out. This will be helpful in building an idea structure, for instance it is recommending a particular space for publicizing and advancing. Accordingly, it will get up to speed to build the new strategies for reasonable thing endorsing for practical advantage.
. Antaris, S., Rafailidis, D., & Nanopoulos, A. (2014). Link injection for boosting information spread in social networks. Social Network Analysis and Mining, 4(1), 236. doi:10.1007/s13278-014-0236-y
. Gasparetti, F. (2017). UCI Machine Learning Repository. Italy: Faculty of Engineering, Roma Tre University. Retrieved from https://archive.ics.uci.edu/ml/datasets/ News+Aggregator
. Ji, X., Chun, S. A., Wei, Z., & Geller, J. (2015). Twitter sentiment classification for measuring public health concerns. Social Network Analysis and Mining, 5(1), 13. doi:10.1007/s13278-015-0253-5
. Bakshy, E., Hofman, J. M., Mason, W. A., & Watts, D. J. (2011, February). Everyone’s an influencer: quantifying influence on twitter. In Proceedings of the fourth ACM international conference on Web search and data mining (pp. 65-74). ACM. doi:10.1145/1935826.1935845
. Dong, R., Li, L., Zhang, Q., & Cai, G. (2018). Information Diffusion on Social Media During Natural Disasters. IEEE Transactions on Computational Social Systems, 5(1), 265–276. doi:10.1109/TCSS.2017.2786545
. Goel, S., Anderson, A., Hofman, J., & Watts, D. J. (2015). The structural virality of online diffusion. Management Science, 62(1), 180–196.
. Jenders, M., Kasneci, G., & Naumann, F. (2013, May). Analyzing and predicting viral tweets. In Proceedings of the 22nd international conference on world wide web (pp. 657-664). ACM.
. Oktay, H., Firat, A., & Ertem, Z. (2014). Demographic breakdown of twitter users: An analysis based on names. Academy of Science and Engineering. ASE.
. Wang, Y., Zeng, D., Zheng, X., & Wang, F. (2009, June). Propagation of online news: dynamic patterns. In Proceedings of the IEEE International Conference on Intelligence and Security Informatics 2009 (pp. 257-259). IEEE. doi:10.1109/ISI.2009.5137321
. Sarkar, A., Chattopadhyay, S., Dey, P., & Roy, S. (2017, January). The importance of seed nodes in spreading information in social networks: A case study. In Proceedings of the 2017 9th International Conference on Communication Systems and Networks, (pp. 395-396). IEEE. doi:10.1109/COMSNETS.2017.7945410