DIGITAL LEARNING STRATEGIES IN THAILAND GOVERNMENT: PRACTICES AND POLICIES

Digital learning strategies to study digital learning strategies in Thailand government focus on  practices and policies. It was found that the types of departments, government agencies affiliated with provinces, districts, and local administrative organizations.  It has no relation to the implementation of policies based on digital government. The digital skills of the personnel in the agency and the mechanisms for driving innovation for spatial development  show that the  types of government agencies that receive policies go to different practices, implementing policies according to digital government. The digital skills of the personnel in the agency and the mechanisms to drive innovation for spatial development are no different.   It also found that the implementation of digital government policies and the digital skills of personnel in the agency. It correlates with innovation driving mechanisms for spatial development, while the digital government policy context in the agency. Capital development aligned with digital government and increasing participation in line with digital government When comparing factors influencing innovation mechanisms for spatial development, compared by standard multiples regression coefficients. (Beta) It was found that innovation mechanisms for spatial development were most influenced by the implementation of digital government policies. (Beta=0.375) Digital skills of personnel in the agency (Beta=0.296) statistically significant at 0.01 All independent variables analyzed. It can explain the variation of innovation mechanisms for spatial development with statistical significance at the 0.01 It can be explained by a percentage. 35.5 (R square =0.355

What Drives Sentiments on Social Media? An Exploratory Study on the 2021 Canadian Federal Election

Social media is used for online political discourse. Voter opinions have different sentiments associated with them. Understanding the factors behind these sentiments can help policymakers to take actions that align with voter needs and priorities. This research focuses on identifying the drivers (keywords) of sentiments while also investigating the relationship between these keywords and how fast the related message (the tweet) spreads. Sentiment Analysis (SA) of 779,169 tweets related to the 2021 Canadian Federal election was followed by text clustering to identify sentiment-driving topics. The results suggest some keywords common in opposite sentiment types (positive and negative), which shows polarization in Twitter while some keywords unique to a sentiment type suggest concepts to invest in or mitigate. Chi-Square tests suggest a significant relationship between keywords and the number of retweets for extremely negative tweets

The Problem of Data Extraction in Social Media: A Theoretical Framework

In today's rapidly evolving digital landscape, the pervasive growth of social media platforms has resulted in an era of unprecedented data generation. These platforms are responsible for generating vast volumes of data on a daily basis, forming intricate webs of patterns and connections that harbor invaluable insights crucial for informed decision-making. Recognizing the significance of exploring social media data, researchers have increasingly turned their attention towards leveraging this data to address a wide array of social research issues. Unlike conventional data collection methods such as questionnaires, interviews, or focus groups, social media data presents unique challenges and opportunities, demanding specialized techniques for its extraction and analysis. However, the absence of a standardized and systematic approach to collect and preprocess social media data remains a gap in the field. This gap not only compromises the quality and credibility of subsequent data analysis but also hinders the realization of the full potential inherent in social media data. This paper aims to bridge this gap by presenting a comprehensive framework designed for the systematic extraction and processing of social media data. The proposed framework offers a clear, step-by-step methodology for the extraction and processing of social media data for analysis. In an era where social media data serves as a pivotal resource for understanding human behavior, sentiment, and societal dynamics, this framework offers a foundational toolset for researchers and practitioners seeking to harness the wealth of insights concealed within the vast expanse of social media data

A Review of Machine Learning Approaches for Real Estate Valuation

Real estate managers must identify the value for properties in their current market. Traditionally, this involved simple data analysis with adjustments made based on manager’s experience. Given the amount of money currently involved in these decisions, and the complexity and speed at which valuation decisions must be made, machine learning technologies provide a newer alternative for property valuation that could improve upon traditional methods. This study utilizes a systematic literature review methodology to identify published studies from the past two decades where specific machine learning technologies have been applied to the property valuation task. We develop a data, reasoning, usefulness (DRU) framework that provides a set of theoretical and practice-based criteria for a multi-faceted performance assessment for each system. This assessment provides the basis for identifying the current state of research in this domain as well as theoretical and practical implications and directions for future research