Probabilistic Machine Learning Using Social Network Analysis

2021 ◽  
pp. 1-12
Author(s):  
A. Rohini ◽  
T. Sudalai Muthu ◽  
Tanupriya Choudhury
Keyword(s):  
Machine Learning ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Probabilistic Machine Learning

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.

scholarly journals Aislamiento social obligatorio: un análisis de sentimientos mediante machine learning

2021 ◽  
Vol 12 (26) ◽  
pp. 1-13
Author(s):  
Carlos Alberto Arango Pastrana ◽  
Carlos Fernando Osorio Andrade
Keyword(s):  
Machine Learning ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Learning Algorithm ◽  
Data Sets ◽  
Machine Learning Algorithm ◽  
Economic Problems ◽  
Colombian Government

To reduce the rate of contagion by Covid-19, the Colombian government has adopted, among other measures, for mandatory isolation, with divided opinions, because despite helping to reduce the spread of the virus, it generates mental and economic problems that are difficult to overcome. The objective of this document was to analyze the underlying sentiments in the Twitter comments related to isolation, identifying the topics and words most frequently used in this context. A machine learning algorithm was built to identify sentiments in 72,564 posts and a social network analysis was applied establishing the most frequent topics in the data sets. The results suggest that the algorithm is highly accurate in classifying feelings. Also, as the isolation extends, comments related to the quarantine grow proportionally. Fear was identified as the predominant feeling throughout the period of confinement in Colombia.

scholarly journals Identifying social media user demographics and topic diversity with computational social science: a case study of a major international policy forum

2020 ◽  
Vol 3 (1) ◽  
pp. 167-188 ◽  
Author(s):  
John Brandt ◽  
Kathleen Buckingham ◽  
Cody Buntain ◽  
Will Anderson ◽  
Sabin Ray ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sustainable Development ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Computational Social Science ◽  
Economic Benefits ◽  
Sustainable Land Use ◽  
Demographic Differences

AbstractWhen the world’s countries agreed on the 2030 Agenda for Sustainable Development, they recognized that equity and inclusion should be at the center of implementing the 17 Sustainable Development Goals (SDGs). SDG 15, which calls for protecting, restoring, and promoting the sustainable use of terrestrial ecosystems, has spurred commitments to restore 350 million hectares of land by 2030. These commitments, primarily made in a top-down manner at the international scale, must be implemented by actively engaging individual landholders and local communities. Ensuring that diverse and marginalized audiences are engaged in the land restoration movement is critical to equitably distributing the economic benefits of restoration. This publication uses social network analysis and machine learning to understand how important the voices of Africans, women, and young people are in governing restoration in Africa. We analyze location- and machine learning-identified demographics from Twitter data collected during the Global Landscapes Forum (GLF), which is the world’s largest platform for promoting sustainable land use practices. Our results suggest that convening the GLF in Nairobi, Kenya elevated the voices of African leaders in comparison to the previous GLF in Bonn, Germany. We also found significant demographic differences in topic-level engagement between different ages, races, and genders. The primary contributions of this paper are a novel methodology for quantifying demographic differences in social media engagement and the application of social media and social network analysis to provide critical insights into the inclusivity of a large political conference aimed at engaging youth and African voices.

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