Characterizing twitter user topics and communication network dynamics of the “Liberate” movement during COVID-19 using unsupervised machine learning and social network analysis

2021 ◽  
Vol 21 ◽  
pp. 100114
Author(s):  
Michael Robert Haupt ◽  
Alex Jinich-Diamant ◽  
Jiawei Li ◽  
Matthew Nali ◽  
Tim K. Mackey
Keyword(s):  
Machine Learning ◽  
Communication Network ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Network Dynamics ◽  
Unsupervised Machine Learning ◽  
Twitter User

Communication Network Analysis of Project Teams in Korean Building Constructions

2013 ◽  
Vol 357-360 ◽  
pp. 2338-2341 ◽  
Author(s):  
Jae Yeob Kim ◽  
Sang Tae No ◽  
Yong Kyu Park
Keyword(s):  
Communication Network ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Construction Project ◽  
Project Teams ◽  
Degree Centrality ◽  
Analysis Program ◽  
Team Members ◽  
Communication Frequency

This study used social network analysis (SNA) in order to analyze communication relationship between project team members in typical cases of Korean building constructions. Data was collected by conducting a survey from key members of construction project teams. We analyzed and digitized degree centrality by using Netminer, a SNA analysis program. According to the result of analysis in communication frequency, intermediate managers such as construction deputy managers were shown the highest and architectural designers were shown the lowest. With respect to communication credibility, construction managers were shown the highest and architectural designers were shown to be low. We discovered that intermediate managers and construction managers of the construction teams play important role in the communication of project teams.

How users tweet about a cyber attack: An explorative study using machine learning and social network analysis

2020 ◽  
Vol 11 (2) ◽  
pp. 195-214 ◽  
Author(s):  
Daniel Vogler ◽  
Florian Meissner
Keyword(s):  
Machine Learning ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Cyber Attack ◽  
Ticket Sales ◽  
Security Issues ◽  
Twitter Users

Cybercrime is a growing threat for firms and customers that emerged with the digitization of business. However, research shows that even though people claim that they are concerned about their privacy online, they do not act correspondingly. This study investigates how prevalent security issues are during a cyber attack among Twitter users. The case under examination is the security breach at the US ticket sales company, Ticketfly, that compromised the information of 26 million users. Tweets related to cybersecurity are detected through the application of automated text classification based on supervised machine learning with support vector machines. Subsequently, the users that wrote security-related tweets are grouped into communities through a social network analysis. The results of this multi-method study show that users concerned about security issues are mostly part of expert communities with already superior knowledge about cybersecurity.

scholarly journals Gragnostics: Fast, Interpretable Features for Comparing Graphs

2019 ◽  
Author(s):  
Robert Gove
Keyword(s):  
Machine Learning ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Linear Time ◽  
Interactive Visualization ◽  
Visualization Tool ◽  
Graph Visualization ◽  
Learning Classifier ◽  
Visualization Tools

Many analytical tasks, such as social network analysis, depend on comparing graphs. Existing methods are slow, or can be difficult to understand. To address these challenges, this paper proposes gragnostics, a set of 10 fast, layperson-understandable graph-level features. Each can be computed in linear time. To evaluate the ability of these features to discriminate different topologies and types of graphs, this paper compares a machine learning classifier using gragnostics to alternative classifiers, and the evaluation finds that the gragnostics classifier achieves higher performance. To evaluate gragnostics' utility in interactive visualization tools, this paper presents Chiron, a graph visualization tool that enables users to explore the subgraphs of a larger graph. Example usage scenarios of Chiron demonstrate that using gragnostics in a rank-by-feature framework can be effective for finding interesting subgraphs.

Machine Learning and Social Network Analysis Applied to Alzheimer's Disease Biomarkers

2013 ◽  
Vol 13 (5) ◽  
pp. 652-662 ◽  
Author(s):  
Javier Di Deco ◽  
Ana M. Gonzalez ◽  
Julia Diaz ◽  
Virginia Mato ◽  
Daniel Garcia–Frank ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Disease Biomarkers

scholarly journals Prediction of MicroRNA-Disease Associations Based on Social Network Analysis Methods

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Quan Zou ◽  
Jinjin Li ◽  
Qingqi Hong ◽  
Ziyu Lin ◽  
Yun Wu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Cross Validation ◽  
Supervised Machine Learning ◽  
Rna Molecules ◽  
Disease Associations ◽  
Endogenous Genes ◽  
Leave One Out

MicroRNAs constitute an important class of noncoding, single-stranded, ~22 nucleotide long RNA molecules encoded by endogenous genes. They play an important role in regulating gene transcription and the regulation of normal development. MicroRNAs can be associated with disease; however, only a few microRNA-disease associations have been confirmed by traditional experimental approaches. We introduce two methods to predict microRNA-disease association. The first method, KATZ, focuses on integrating the social network analysis method with machine learning and is based on networks derived from known microRNA-disease associations, disease-disease associations, and microRNA-microRNA associations. The other method, CATAPULT, is a supervised machine learning method. We applied the two methods to 242 known microRNA-disease associations and evaluated their performance using leave-one-out cross-validation and 3-fold cross-validation. Experiments proved that our methods outperformed the state-of-the-art methods.

scholarly journals An approach to predicting patient experience through machine learning and social network analysis

2020 ◽  
Vol 27 (12) ◽  
pp. 1834-1843
Author(s):  
Vitej Bari ◽  
Jamie S Hirsch ◽  
Joseph Narvaez ◽  
Robert Sardinia ◽  
Kevin R Bock ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Patient Experience ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Healthcare Providers ◽  
Hospital Consumer Assessment ◽  
Consumer Assessment

Abstract Objective Improving the patient experience has become an essential component of any healthcare system’s performance metrics portfolio. In this study, we developed a machine learning model to predict a patient’s response to the Hospital Consumer Assessment of Healthcare Providers and Systems survey’s “Doctor Communications” domain questions while simultaneously identifying most impactful providers in a network. Materials and Methods This is an observational study of patients admitted to a single tertiary care hospital between 2016 and 2020. Using machine learning algorithms, electronic health record data were used to predict patient responses to Hospital Consumer Assessment of Healthcare Providers and Systems survey questions in the doctor domain, and patients who are at risk for responding negatively were identified. Model performance was assessed by area under receiver-operating characteristic curve. Social network analysis metrics were also used to identify providers most impactful to patient experience. Results Using a random forest algorithm, patients’ responses to the following 3 questions were predicted: “During this hospital stay how often did doctors. 1) treat you with courtesy and respect? 2) explain things in a way that you could understand? 3) listen carefully to you?” with areas under the receiver-operating characteristic curve of 0.876, 0.819, and 0.819, respectively. Social network analysis found that doctors with higher centrality appear to have an outsized influence on patient experience, as measured by rank in the random forest model in the doctor domain. Conclusions A machine learning algorithm identified patients at risk of a negative experience. Furthermore, a doctor social network framework provides metrics for identifying those providers that are most influential on the patient experience.

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