Exploring the Feature Selection-Based Data Analytics Solutions for Text Mining Online Communities by Investigating the Influential Factors: A Case Study of Programming CQA in Stack Overflow

2016 ◽  
pp. 49-93
Author(s):  
Shu Zhou ◽  
Simon Fong
Keyword(s):  
Feature Selection ◽  
Text Mining ◽  
Online Communities ◽  
Data Analytics ◽  
Influential Factors ◽  
Stack Overflow

scholarly journals Text Mining for Social Research: A Reddit-based Case Study of George Floyd

2020 ◽  
Author(s):  
Marina Wiebe
Keyword(s):  
Social Media ◽  
Text Mining ◽  
Online Communities ◽  
Social Research ◽  
Public Forum ◽  
The World

Social media sources such as Reddit allow for a large public forum to share opinions and ideas. The discussions in these online communities often reflect events occurring around the world. These discussions can also be influenced and shaped by the participation of political figures online. Using Reddit's public API, comments were scraped in prominent policing related Subreddits during the week of George Floyd's death to understand how his death and Donald's Trump's response to the subsequent rioting influenced the tone of discussion.

Data Analytics to Help Improve Hydraulic Fracture Geometry Prediction Using Support Vector Regression: Case Study

2020 ◽  
Author(s):  
Avinash Wesley ◽  
Bharat Mantha ◽  
Ajay Rajeev ◽  
Aimee Taylor ◽  
Mohit Dholi ◽  
...  
Keyword(s):  
Support Vector Regression ◽  
Hydraulic Fracture ◽  
Data Analytics ◽  
Support Vector ◽  
Fracture Geometry

The Internet is a Semicommons

2018 ◽  
Author(s):  
James Grimmelmann
Keyword(s):  
Open Field ◽  
Online Communities ◽  
Strategic Behavior ◽  
Private Property ◽  
The Internet ◽  
Field System ◽  
Technical Features ◽  
Dynamic Interplay ◽  
Enormous Success

78 Fordham Law Review 2799 (2010)The Internet is a semicommons. Private property in servers and network links coexists with a shared communications platform. This distinctive combination both explains the Internet's enormous success and illustrates some of its recurring problems.Building on Henry Smith's theory of the semicommons in the medieval open-field system, this essay explains how the dynamic interplay between private and common uses on the Internet enables it to facilitate worldwide sharing and collaboration without collapsing under the strain of misuse. It shows that key technical features of the Internet, such as its layering of protocols and the Web's division into distinct "sites," respond to the characteristic threats of strategic behavior in a semicommons. An extended case study of the Usenet distributed messaging system shows that not all semicommons on the Internet succeed; the continued success of the Internet depends on our ability to create strong online communities that can manage and defend the infrastructure on which they rely. Private and common both have essential roles to play in that task, a lesson recognized in David Post's and Jonathan Zittrain's recent books on the Internet.

scholarly journals On Frogs, Monkeys, and Execution Memes: Exploring the Humor-Hate Nexus at the Intersection of Neo-Nazi and Alt-Right Movements in Sweden

2021 ◽  
Vol 22 (2) ◽  
pp. 147-165
Author(s):  
Tina Askanius
Keyword(s):  
Content Analysis ◽  
Online Communities ◽  
Online Communication ◽  
Online Media ◽  
Extreme Right ◽  
Visual Content ◽  
Visual Aesthetics ◽  
Resistance Movement ◽  
Media Practices

This article is based on a case study of the online media practices of the militant neo-Nazi organization the Nordic Resistance Movement, currently the biggest and most active extreme-right actor in Scandinavia. I trace a recent turn to humor, irony, and ambiguity in their online communication and the increasing adaptation of stylistic strategies and visual aesthetics of the Alt-Right inspired by online communities such as 4chan, 8chan, Reddit, and Imgur. Drawing on a visual content analysis of memes ( N = 634) created and circulated by the organization, the analysis explores the place of humor, irony, and ambiguity across these cultural expressions of neo-Nazism and how ideas, symbols, and layers of meaning travel back and forth between neo-Nazi and Alt-right groups within Sweden today.

scholarly journals Perceived Trip Time Reliability and Its Cost in a Rail Transit Network

2021 ◽  
Vol 13 (13) ◽  
pp. 7504
Author(s):  
Jie Liu ◽  
Paul Schonfeld ◽  
Jinqu Chen ◽  
Yong Yin ◽  
Qiyuan Peng
Keyword(s):  
Regression Models ◽  
Influential Factors ◽  
Rail Transit ◽  
Linear Regression Models ◽  
Transit Network ◽  
Time Index ◽  
Trip Time ◽  
Buffer Time ◽  
Transfer Times

Time reliability in a Rail Transit Network (RTN) is usually measured according to clock-based trip time, while the travel conditions such as travel comfort and convenience cannot be reflected by clock-based trip time. Here, the crowding level of trains, seat availability, and transfer times are considered to compute passengers’ Perceived Trip Time (PTT). Compared with the average PTT, the extra PTT needed for arriving reliably, which equals the 95th percentile PTT minus the average PTT, is converted into the monetary cost for estimating Perceived Time Reliability Cost (PTRC). The ratio of extra PTT needed for arriving reliably to the average PTT referring to the buffer time index is proposed to measure Perceived Time Reliability (PTR). To overcome the difficulty of obtaining passengers’ PTT who travel among rail transit modes, a Monte Carlo simulation is applied to generated passengers’ PTT for computing PTR and PTRC. A case study of Chengdu’s RTN shows that the proposed metrics and method measure the PTR and PTRC in an RTN effectively. PTTR, PTRC, and influential factors have significant linear relations among them, and the obtained linear regression models among them can guide passengers to travel reliably.

scholarly journals Architecture For Automation System Metrics Collection, Visualization and Data Engineering – HAMK Sheet Metal Center Building Automation Case Study

2019 ◽  
Vol 9 (1) ◽  
pp. 561-570
Author(s):  
Khoa Dang ◽  
Igor Trotskii
Keyword(s):  
System Integration ◽  
Data Analytics ◽  
Building Energy ◽  
Facility Management ◽  
Automation System ◽  
Building Energy Efficiency ◽  
Building Automation ◽  
Automation Systems ◽  
Integration Data

AbstractEver growing building energy consumption requires advanced automation and monitoring solutions in order to improve building energy efficiency. Furthermore, aggregation of building automation data, similarly to industrial scenarios allows for condition monitoring and fault diagnostics of the Heating, Ventilations and Air Conditioning (HVAC) system. For existing buildings, the commissioned SCADA solutions provide historical trends, alarms management and setpoint curve adjustments, which are essential features for facility management personnel. The development in Internet of Things (IoT) and Industry 4.0, as well as software microservices enables higher system integration, data analytics and rich visualization to be integrated into the existing infrastructure. This paper presents the implementation of a technology stack, which can be used as a framework for improving existing and new building automation systems by increasing interconnection and integrating data analytics solutions. The implementation solution is realized and evaluated for a nearly zero energy building, as a case study.

Quality inspection of complex-shaped metal parts by vibrations and an integrated Mahalanobis classification system

2020 ◽  
pp. 147592172097970
Author(s):  
Liangliang Cheng ◽  
Vahid Yaghoubi ◽  
Wim Van Paepegem ◽  
Mathias Kersemans
Keyword(s):  
Feature Selection ◽  
Mahalanobis Distance ◽  
Classification System ◽  
Gravitational Search Algorithm ◽  
Particle Swarm ◽  
Classification Performance ◽  
Decision Boundary ◽  
Particle Swarm Optimizer ◽  
Selection Phase

The Mahalanobis–Taguchi system is considered as a promising and powerful tool for handling binary classification cases. Though, the Mahalanobis–Taguchi system has several restrictions in screening useful features and determining the decision boundary in an optimal manner. In this article, an integrated Mahalanobis classification system is proposed which builds on the concept of Mahalanobis distance and its space. The integrated Mahalanobis classification system integrates the decision boundary searching process, based on particle swarm optimizer, directly into the feature selection phase for constructing the Mahalanobis distance space. This integration (a) avoids the need for user-dependent input parameters and (b) improves the classification performance. For the feature selection phase, both the use of binary particle swarm optimizer and binary gravitational search algorithm is investigated. To deal with possible overfitting problems in case of sparse data sets, k-fold cross-validation is considered. The integrated Mahalanobis classification system procedure is benchmarked with the classical Mahalanobis–Taguchi system as well as the recently proposed two-stage Mahalanobis classification system in terms of classification performance. Results are presented on both an experimental case study of complex-shaped metallic turbine blades with various damage types and a synthetic case study of cylindrical dogbone samples with creep and microstructural damage. The results indicate that the proposed integrated Mahalanobis classification system shows good and robust classification performance.

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