Extracting social and community intelligence from digital footprints

2012 ◽  
Vol 5 (1) ◽  
pp. 1-2 ◽  
Author(s):  
Bin Guo ◽  
Daqing Zhang ◽  
Zhiwen Yu ◽  
Francesco Calabrese
Keyword(s):  
Social And Community Intelligence ◽  
Digital Footprints ◽  
Community Intelligence

Social and Community Intelligence: Technologies and Trends

IEEE Software ◽  
2012 ◽  
Vol 29 (4) ◽  
pp. 88-92 ◽  
Author(s):  
Daqing Zhang ◽  
Zhu Wang ◽  
Bin Guo ◽  
Zhiwen Yu
Keyword(s):  
Social And Community Intelligence ◽  
Community Intelligence

The Emergence of Social and Community Intelligence

Computer ◽  
2011 ◽  
Vol 44 (7) ◽  
pp. 21-28 ◽  
Author(s):  
Daqing Zhang ◽  
Bin Guo ◽  
Zhiwen Yu
Keyword(s):  
Social And Community Intelligence ◽  
Community Intelligence

scholarly journals Methodology and Models for Individuals’ Creditworthiness Management Using Digital Footprint Data and Machine Learning Methods

Mathematics ◽  
2021 ◽  
Vol 9 (15) ◽  
pp. 1820
Author(s):  
Ekaterina V. Orlova
Keyword(s):  
Interest Rates ◽  
Additional Data ◽  
Risk Level ◽  
Gradient Boosting ◽  
Cluster Number ◽  
New Approach ◽  
Credit Risks ◽  
Stochastic Gradient Boosting ◽  
The Stability ◽  
Digital Footprints

This research deals with the challenge of reducing banks’ credit risks associated with the insolvency of borrowing individuals. To solve this challenge, we propose a new approach, methodology and models for assessing individual creditworthiness, with additional data about borrowers’ digital footprints to implement comprehensive analysis and prediction of a borrower’s credit profile. We suggest a model for borrowers’ clustering based on the method of hierarchical clustering and the k-means method, which groups actual borrowers having similar creditworthiness and similar credit risks into homogeneous clusters. We also design the model for borrowers’ classification based on the stochastic gradient boosting (SGB) method, which reliably determines the cluster number and therefore the risk level for a new borrower. The developed models are the basis for decision making regarding the decision about lending value, interest rates and lending terms for each risk-homogeneous borrower’s group. The modified version of the methodology for assessing individual creditworthiness is presented, which is to reduce the credit risks and to increase the stability and profitability of financial organizations.

Big Data Analysis with No Digital Footprints Available: Evidence from Cyber-Telecom Fraud

2020 ◽  
Author(s):  
Laura Xiaolei Liu ◽  
Yufei Liu ◽  
Xinghua Ruan ◽  
Yu Zhang
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Big Data Analysis ◽  
Digital Footprints

Digital footprints in the university space

2021 ◽  
Vol 4 (2) ◽  
pp. 52-72
Author(s):  
Timur Khusyainov
Keyword(s):  
Personal Data ◽  
Educational Environment ◽  
Educational Process ◽  
Global Network ◽  
Ethical Standards ◽  
Digital Footprint ◽  
Digital Environments ◽  
The Moment ◽  
The University ◽  
Digital Footprints

This work considers the use of digital traces in the educational environment and the specifics of their collection and analysis at the university. One way or another, all participants in the educational process, as well as those who can potentially become them, for example, applicants, leave digital traces in the digital environments of the university and the Global Network in general, and these traces can be analyzed. At the same time, even the university itself as an organization leaves a certain digital footprint on the Internet. At the moment, most researchers are very optimistic, contemplating on what positive changes can be brought by the analysis of digital traces of applicants, students and teachers for the development of the university itself, the educa-tional process, and the formation of individual learning paths. In contrast to this, the author identifies a number of possible prospects for the analysis of Big Data and the use of Artificial Intelligence for education at the university of the future. Attention is focused on how this can affect the safety of the environment and conflict with ethical standards. Participants in the educational process, falling under the analysis of their digital traces, can both suffer because of them, even if their activities have not been in any way connected with the university, and begin to hide their true digital identity, creating «false» digital traces and becoming anon-ymous. The author assumes that an increase in such control covering actions, thoughts and emotions naturally results in the emergence of the concept of a «Dark» University, which distances itself as much as possible from such methods of analyzing personal data.

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