An analysis of Twitter users’ long term political view migration using cross-account data mining

2021 ◽  
Vol 26 ◽  
pp. 100177
Author(s):  
Alexandra Sosnkowski ◽  
Carol J. Fung ◽  
Shivram Ramkumar
Keyword(s):  
Data Mining ◽  
Political View ◽  
Twitter Users

scholarly journals Rationale for Timing of Follow-Up Visits to Assess Gluten-Free Diet in Celiac Disease Patients Based on Data Mining

Nutrients ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 357
Author(s):  
Alfonso Rodríguez-Herrera ◽  
Joaquín Reyes-Andrade ◽  
Cristina Rubio-Escudero
Keyword(s):  
Data Mining ◽  
Celiac Disease ◽  
Cultural Context ◽  
Gluten Free Diet ◽  
Gluten Free ◽  
Adherence To Diet ◽  
And Gender ◽  
Few Data

The assessment of compliance of gluten-free diet (GFD) is a keystone in the supervision of celiac disease (CD) patients. Few data are available documenting evidence-based follow-up frequency for CD patients. In this work we aim at creating a criterion for timing of clinical follow-up for CD patients using data mining. We have applied data mining to a dataset with 188 CD patients on GFD (75% of them are children below 14 years old), evaluating the presence of gluten immunogenic peptides (GIP) in stools as an adherence to diet marker. The variables considered are gender, age, years following GFD and adherence to the GFD by fecal GIP. The results identify patients on GFD for more than two years (41.5% of the patients) as more prone to poor compliance and so needing more frequent follow-up than patients with less than 2 years on GFD. This is against the usual clinical practice of following less patients on long term GFD, as they are supposed to perform better. Our results support different timing follow-up frequency taking into consideration the number of years on GFD, age and gender. Patients on long term GFD should have a more frequent monitoring as they show a higher level of gluten exposure. A gender perspective should also be considered as non-compliance is partially linked to gender in our results: Males tend to get more gluten exposure, at least in the cultural context where our study was carried out. Children tend to perform better than teenagers or adults.

scholarly journals Long-term predictors of dengue outbreaks in Bangladesh: A data mining approach

2018 ◽  
Vol 3 ◽  
pp. 322-330 ◽  
Author(s):  
Olav Titus Muurlink ◽  
Peter Stephenson ◽  
Mohammad Zahirul Islam ◽  
Andrew W. Taylor-Robinson
Keyword(s):  
Data Mining ◽  
Data Mining Approach ◽  
Dengue Outbreaks

Customer Relationship Management and Data Mining

2016 ◽  
pp. 1362-1401
Author(s):  
Niccolò Gordini ◽  
Valerio Veglio
Keyword(s):  
Data Mining ◽  
Customer Relationship Management ◽  
Customer Value ◽  
Relationship Management ◽  
Global Market ◽  
Customer Relationship ◽  
Digital Revolution ◽  
Marketing Activities ◽  
Potential Customers

In the global market of today, Customer Relationship Management (CRM) plays a fundamental role in market-oriented companies to understand customer behaviors, achieve and maintain a long-term relationship with them, and maximize the customer value. Moreover, the digital revolution has made information easy and fairly inexpensive to capture. Thus, companies have stored a large amount of data about their current and potential customers. However, this data is often raw and meaningless. Within the CRM framework, Data Mining (DM) is a very popular tool for extracting useful information from this data and for predicting customer behaviors in order to make profitable marketing decisions. This research aims to demonstrate the classification decision tree as one of the main computational data mining models able to forecast accurate marketing performance within global organizations. Particular attention is paid to the identification of the best marketing activities to which firms should concentrate their future marketing investments. The criteria is based on the loss functions that confirm the accuracy of this model.

An Application of Text Mining to Capture and Analyze eWOM

2016 ◽  
pp. 168-186 ◽  
Author(s):  
Taşkın Dirsehan
Keyword(s):  
Data Mining ◽  
Text Mining ◽  
Customer Relationship ◽  
Competitive Advantages ◽  
Strategic Decisions ◽  
Data Mining Tool ◽  
Mining Tool ◽  
The Moment ◽  
Mining Tools

Marketing concept has progressed through different phases of evolution in the past. At the moment, customer relationship management is considered as the last era of marketing development. The main purpose of this approach is to build long-term oriented profitable relationships with customers. So, companies should know better their customers. This knowledge can be created through a deeper analysis of companies' data with data mining tools. Companies which are able to use data mining tools will gain strong competitive advantages for their strategic decisions. Hotel industry is selected in this study, since it provides a warehouse of customer comments from which precious knowledge can be obtained if text mining as a data mining tool is used appropriately. Thus, this study attempts to explain the stages of text mining with the use of Rapidminer. As a result, different approaches according to the customer satisfaction/dissatisfaction are discussed to build competitive advantages.

EEG Data Mining Using PCA

2011 ◽  
pp. 161-180 ◽  
Author(s):  
Lenka Lhotská ◽  
Vladimír Krajca ◽  
Jitka Mohylová ◽  
Svojmil Petránek ◽  
Václav Gerla
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Principal Components ◽  
The Neural Network ◽  
Eeg Data ◽  
Eigen Decomposition ◽  
Electroencephalogram Eeg ◽  
Epileptic Spike ◽  
Eeg Signal Processing

This chapter deals with the application of principal components analysis (PCA) to the field of data mining in electroencephalogram (EEG) processing. The principal components are estimated from the signal by eigen decomposition of the covariance estimate of the input. Alternatively, they can be estimated by a neural network (NN) configured for extracting the first principal components. Instead of performing computationally complex operations for eigenvector estimation, the neural network can be trained to produce ordered first principal components. Possible applications include separation of different signal components for feature extraction in the field of EEG signal processing, adaptive segmentation, epileptic spike detection, and long-term EEG monitoring evaluation of patients in a coma.

scholarly journals Collective Emotions and Social Resilience in the Digital Traces After a Terrorist Attack

2019 ◽  
Vol 30 (4) ◽  
pp. 617-628 ◽  
Author(s):  
David Garcia ◽  
Bernard Rimé
Keyword(s):  
Large Scale ◽  
Terrorist Attack ◽  
Terrorist Attacks ◽  
Social Resilience ◽  
Collective Emotions ◽  
The Social ◽  
Collective Effervescence ◽  
Scale Test ◽  
Twitter Users

After collective traumas such as natural disasters and terrorist attacks, members of concerned communities experience intense emotions and talk profusely about them. Although these exchanges resemble simple emotional venting, Durkheim’s theory of collective effervescence postulates that these collective emotions lead to higher levels of solidarity in the affected community. We present the first large-scale test of this theory through the analysis of digital traces of 62,114 Twitter users after the Paris terrorist attacks of November 2015. We found a collective negative emotional response followed by a marked long-term increase in the use of lexical indicators related to solidarity. Expressions of social processes, prosocial behavior, and positive affect were higher in the months after the attacks for the individuals who participated to a higher degree in the collective emotion. Our findings support the conclusion that collective emotions after a disaster are associated with higher solidarity, revealing the social resilience of a community.

A Framework for the Interaction of Active Audiences and Influencers on Twitter: The Case of Zika Virus

2020 ◽  
Vol 19 (04) ◽  
pp. 2050032
Author(s):  
Shadi Shakeri
Keyword(s):  
Agenda Setting ◽  
Crisis Communication ◽  
Explicit Knowledge ◽  
User Involvement ◽  
Short Term ◽  
Health Crisis ◽  
Network Community ◽  
Twitter Users ◽  
Over Time

In this paper, we investigate communication among Twitter users in the context of the 2016 Zika crisis, to understand how influencers and audiences contribute to agenda setting in health crisis communication. We analyse the content of 146,953 Zika-related tweets posted between April and September 2016 and examine how discussion topics vary by network community and user involvement over time. We argue that audiences adopt a broad view of health crisis-related issues and advocate for long-term solutions drawn from theories of active audiences and agenda-setting. Based on our observations on the Zika crisis case, we propose a framework for the dynamics in health crisis communication, which suggests a shift of discourse from a short-term perspective on specific issues to a long-term perspective on broader issues. The research contributes to the KM literature by suggesting a new method for converting individual tacit knowledge to collective explicit knowledge. Applying the framework to the coronavirus pandemic conversations can offer significant insights into the crisis.

A hybrid data mining driven algorithm for long term electric peak load and energy demand forecasting

Energy ◽  
2020 ◽  
Vol 204 ◽  
pp. 117948 ◽  
Author(s):  
Mohammad-Rasool Kazemzadeh ◽  
Ali Amjadian ◽  
Turaj Amraee
Keyword(s):  
Data Mining ◽  
Energy Demand ◽  
Demand Forecasting ◽  
Peak Load ◽  
Energy Demand Forecasting ◽  
Hybrid Data

Solar image parameter data from the SDO: Long-term curation and data mining

2015 ◽  
Vol 13 ◽  
pp. 86-98 ◽  
Author(s):  
M.A. Schuh ◽  
R.A. Angryk ◽  
P.C. Martens
Keyword(s):  
Data Mining ◽  
Solar Image
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