Modelling a Behavioral Scoring System for Lending Loans using Twitter

Open Access

Issue		ITM Web Conf. Volume 37, 2021 International Conference on Innovative Technology for Sustainable Development (ICITSD-2021)


Article Number		01012
Number of page(s)		10
Section		Innovative Technology for Sustainable Development
DOI		https://doi.org/10.1051/itmconf/20213701012
Published online		17 March 2021

A.S Rao, N Pasojevic, Z Li, T Dsouza,. ‘Klout score: Measuring influence across multiple social networks’, 2015 IEEE International Conference on Big Data (Big Data), (2015) [Google Scholar]
L. Luceri, T. Braun, S. Giordano, ‘Analyzing and inferring human real-life behaviorthrough online social networks with social influence deep learning’, Applied Network Scince, 4(1), (2019) [Google Scholar]
Y. Zhang, H. Jia, Y. Diao, M. Hai, Li H, ‘Research on Credit Scoring by Fusing SocialMedia Information in Online Peer-to-Peer Lending’, Procedia Computer Science, 91, pp.168–174, (2016) [Google Scholar]
S.J. Imtiaz, ‘A Better Comparison Summary of Credit Scoring Classification.’ International Journal of Advanced Computer Science and Applications, 8(7), (2017) [CrossRef] [Google Scholar]
J. Sun, J. Tang, ‘A Survey of Models and Algorithms for Social Influence Analysis.’ Social Network Data Analytics, pp.177–214, (2011) [Google Scholar]
A. Bagherjeiran, and R. Parekh, ‘Combining Behavioral and Social Network Data for Online Advertising’, 2008 IEEE International Conference on Data Mining Workshops, (2008) [Google Scholar]
Y. Wei, P. Yildirim, C.V. Bulte, C. Dellarocas, ‘Credit Scoring with Social Network Data.’ SSRN Electronic Journal, pp 234-258, (2014) [Google Scholar]
S. Gül, Ö. Kabak, I. Topcu, ‘A multiple criteria credit rating approach utilizing social media data.’Data & Knowledge Engineering, 116, pp.80–99, (2018) [Google Scholar]
E. Yasa, B. Mucan, “How Turkish Banks benefit from Social Media: Analyzing Banks Formal Links”, International Journal of Strategic Innovative Marketing, Vol. 1, pp. 120-129, (2014) [Google Scholar]
F.M. Masuabi, L.D. Erasmus, ‘Social media service innovation in South African retail banking: A case study’, 2016 Portland International Conference on Management of Engineering and Technology (PICMET), (2016) [Google Scholar]
X. Cheng, J. Wu, S.S. Liao, ‘A study ofcontagion in the financial system from theperspective of network analytics’, Neurocomputing, 264, pp.42–49, (2017) [Google Scholar]
J. Bollen, H. Mao, ’Twitter Mood as a Stock Market Predictor Computer’, 44(10), pp.91–94, (2011) [Google Scholar]
S. Rönnqvist, P. Sarlin, ’Bank distress in the news: Describing events through deep learning’, Neurocomputing, 264, pp.57–70, (2017) [Google Scholar]
P. Cerchiello, P. Giudici, G. Nicola, ‘Twitter data models for bank risk contagion’, Neurocomputing, 264, pp. 50–56, (2017) [Google Scholar]
V. Rao, L. Kupper, “Ties in Paired Comparison Experiments: A Generalization of the Bradley-Terry Model”, Journal of the American Statistical Association, Vol. 62, no. 317, pp. 194–204, (1967) [Google Scholar]
Paola Cerchiello, Paolo Giudici, Giancarlo Nicola, “Twitter data models for bank risk contagion”, Neurocomputing, Volume 264, Pages 5056, ISSN 0925-2312, https://doi.org/10.1016/j.neucom.2016.10.101, (2017) [Google Scholar]
A. Gupta, P. Kumaraguru, C. Castillo, P. Meier, ’TweetCred: Real-Time Credibility Assessment of Content on Twitter’, In: Aiello L.M., McFarland D.(eds) Social Informatics, SocInfo 2014, Lecture Notes in ComputerScience, Vol 8851, (2014) [Google Scholar]
Taylor Francis, ’Extending The Bradley– Terry Model For Paired Comparisons To Accommodate Weights’, (2020) [Google Scholar]

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