Data Mining Practical Machine Learning Tools and Techniques

Roberta Comunian tackles head-on some of the dilemmas and challenges which this promising new methodology raise for a researcher in regional studies.

Social Network Analysis

In this digital era, data is new oil and artificial intelligence (AI) is new electricity, which is needed in different elements of operations management (OM) such as manufacturing, product development, services and supply chain. This study explores the feasibility of AI utilization within an organization on six factors such as job-fit, complexity, long-term consequences, affect towards use, social factors and facilitating conditions for different elements of OM by mining the collective intelligence of experts on Twitter and through academic literature. The study provides guidelines for managers for AI applications in different components of OM and concludes by presenting the limitations of the study along with future research directions.

/pdf/understanding-artificial-intelligence-adoption-in-operations-1toq3d8ls1.pdf

Understanding artificial intelligence adoption in operations management: insights from the review of academic literature and social media discussions

Theory building with big data-driven research – Moving away from the “What” towards the “Why”

Context Systematic Literature Review (SLR) studies aim to identify relevant primary papers, extract the required data, analyze, and synthesize results to gain further and broader insight into the investigated domain. Multiple SLR studies have been conducted in several domains, such as software engineering, medicine, and pharmacy. Conducting an SLR is a time-consuming, laborious, and costly effort. As such, several researchers developed different techniques to automate the SLR process. However, a systematic overview of the current state-of-the-art in SLR automation seems to be lacking. Objective This study aims to collect and synthesize the studies that focus on the automation of SLR to pave the way for further research. Method A systematic literature review is conducted on published primary studies on the automation of SLR studies, in which 41 primary studies have been analyzed. Results This SLR identifies the objectives of automation studies, application domains, automated steps of the SLR, automation techniques, and challenges and solution directions. Conclusion According to our study, the leading automated step is the Selection of Primary Studies. Although many studies have provided automation approaches for systematic literature reviews, no study has been found to apply automation techniques in the planning and reporting phase. Further research is needed to support the automation of the other activities of the SLR process.

Automation of systematic literature reviews: A systematic literature review

Researchers have collected Twitter data to study a wide range of topics. This growing body of literature, however, has not yet been reviewed systematically to synthesize Twitter-related papers. The existing literature review papers have been limited by constraints of traditional methods to manually select and analyze samples of topically related papers. The goals of this retrospective study are to identify dominant topics of Twitter-based research, summarize the temporal trend of topics, and interpret the evolution of topics withing the last ten years. This study systematically mines a large number of Twitter-based studies to characterize the relevant literature by an efficient and effective approach. This study collected relevant papers from three databases and applied text mining and trend analysis to detect semantic patterns and explore the yearly development of research themes across a decade. We found 38 topics in more than 18,000 manuscripts published between 2006 and 2019. By quantifying temporal trends, this study found that while 23.7% of topics did not show a significant trend (P = 0.05), 21% of topics had increasing trends and 55.3% of topics had decreasing trends that these hot and cold topics represent three categories: application, methodology, and technology. The contributions of this paper can be utilized in the growing field of Twitter-based research and are beneficial to researchers, educators, and publishers.

/pdf/twitter-and-research-a-systematic-literature-review-through-2199wcn2zf.pdf

Twitter and Research: A Systematic Literature Review Through Text Mining

Privacy needs and stigma pose significant barriers to lesbian, gay, bisexual, and transgender (LGBT) people sharing information related to their identities in traditional settings and research methods such as surveys and interviews. Fortunately, social media facilitates people’s belonging to and exchanging information within online LGBT communities. Compared to heterosexual respondents, LGBT users are also more likely to have accounts on social media websites and access social media daily. However, the current relevant LGBT studies on social media are not efficient or assume that any accounts that utilize LGBT-related words in their profile belong to individuals who identify as LGBT. Our human coding of over 16,000 accounts instead proposes the following three categories of LGBT Twitter users: individual, sexual worker/porn, and organization. This research develops a machine learning classifier based on the profile and bio features of these Twitter accounts. To have an efficient and effective process, we use a feature selection method to reduce the number of features and improve the classifier’s performance. Our approach achieves a promising result with around 88% accuracy. We also develop statistical analyses to compare the three categories based on the average weight of top features.

https://www.mdpi.com/2079-9292/10/15/1822/pdf

Automatic Categorization of LGBT User Profiles on Twitter with Machine Learning

We present “Mystery Agent,” an interactive storytelling voice user interface (VUI) equipped with self-regulated learning strategies to deliver informal health-related learning to older adults through a murder mystery story. We conducted a mixed methods user study with 10 older adults to evaluate Mystery Agent, using usability and perception-based questionnaires, followed by a semi-structured interview and co-design activity to generate design insights and identify design priorities. We found older adults had a positive experience interacting with Mystery Agent and considered storytelling to be an appropriate and engaging way to learn health information. However, older adults identified credibility, compassion, and control as crucial factors influencing long-term use. To address this, we present design guidelines using Mystery Agent as an example to help practitioners and researchers devise novel solutions to address the informal health information learning needs of older adults.

“A Painless Way to Learn:” Designing an Interactive Storytelling Voice User Interface to Engage Older Adults in Informal Health Information Learning

To combat health disinformation shared online, there is a need to identify and characterize the prevalence of topics shared by trolls managed by individuals to promote discord. The current literature is limited to a few health topics and dominated by vaccination. The goal of this study is to identify and analyze the breadth of health topics discussed by left (liberal) and right (conservative) Russian trolls on Twitter. We introduce an automated framework based on mixed methods including both computational and qualitative techniques. Results suggest that Russian trolls discussed 48 health-related topics, ranging from diet to abortion. Out of the 48 topics, there was a significant difference (p-value ≤ 0.004) between left and right trolls based on 17 topics. Hillary Clinton’s health during the 2016 election was the most popular topic for right trolls, who discussed this topic significantly more than left trolls. Mental health was the most popular topic for left trolls, who discussed this topic significantly more than right trolls. This study shows that health disinformation is a global public health threat on social media for a considerable number of health topics. This study can be beneficial for researchers who are interested in political disinformation and health monitoring, communication, and promotion on social media by showing health information shared by Russian trolls.

https://www.mdpi.com/1660-4601/18/4/2159/pdf

Identifying and Analyzing Health-Related Themes in Disinformation Shared by Conservative and Liberal Russian Trolls on Twitter.

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Morgan Lundy

Author Tools

Chat about Author

Papers

Twitter and Research: A Systematic Literature Review Through Text Mining

Automatic Categorization of LGBT User Profiles on Twitter with Machine Learning

“A Painless Way to Learn:” Designing an Interactive Storytelling Voice User Interface to Engage Older Adults in Informal Health Information Learning

Identifying and Analyzing Health-Related Themes in Disinformation Shared by Conservative and Liberal Russian Trolls on Twitter.