scholarly journals Property Valuation Using Linear Regression and Random Forest Algorithm

2021 ◽  
Vol 10 (4) ◽  
pp. 0-0
Keyword(s):  
Social Media ◽  
Urban Areas ◽  
Recent Past ◽  
Property Valuation ◽  
Nation States ◽  
Social Media Platforms ◽  
User Groups ◽  
Property Sales ◽  
Using Data ◽  
Economic Boom

The economic boom over the recent past and the quest to further develop, has made several nation states the business hubs in their regions. Along with the investments, there has been growth in the number of property sales. Social media has become convenient platform of choice for advertising property sales after the introduction of Web 2.0. This article utilizes social media platforms like Facebook to scrape data from user groups advertising properties and then using data mining techniques and approaches to determine true valuation of properties. This methodology is based on set attributes, in the urban areas by looking at the property sales of the recent past within the same area. This enables investors interested in these properties and provides a fair idea of price of properties based on the key attributes associated with the respective property.

scholarly journals Property Valuation Using Linear Regression and Random Forest Algorithm

2021 ◽  
Vol 10 (4) ◽  
pp. 1-16
Author(s):  
Sam Goundar ◽  
Akashdeep Bhardwaj
Keyword(s):  
Social Media ◽  
Urban Areas ◽  
Recent Past ◽  
Property Valuation ◽  
Nation States ◽  
Social Media Platforms ◽  
User Groups ◽  
Property Sales ◽  
Using Data ◽  
Economic Boom

The economic boom over the recent past and the quest to further develop, has made several nation states the business hubs in their regions. Along with the investments, there has been growth in the number of property sales. Social media has become convenient platform of choice for advertising property sales after the introduction of Web 2.0. This article utilizes social media platforms like Facebook to scrape data from user groups advertising properties and then using data mining techniques and approaches to determine true valuation of properties. This methodology is based on set attributes, in the urban areas by looking at the property sales of the recent past within the same area. This enables investors interested in these properties and provides a fair idea of price of properties based on the key attributes associated with the respective property.

An Empirical Investigation on Social Media Users' Demand for Financial Information Distributed via Social Media Platforms

2018 ◽  
Vol 33 (2) ◽  
pp. 155-175 ◽  
Author(s):  
Robert N. Marley ◽  
Neal M. Snow
Keyword(s):  
Social Media ◽  
Information Content ◽  
Empirical Investigation ◽  
Financial Information ◽  
Social Media Platforms ◽  
User Groups ◽  
Media Communications ◽  
Social Media Presence ◽  
Using Data ◽  
Jel Classifications

ABSTRACT Managers feel significant pressure to establish a social media presence that differentiates their organization from rivals, though few managers feel confident that their organization provides social media users with the information they desire. Thus, while the supply of information provided to social media users by organizations continues to proliferate rapidly, few studies have investigated the information social media users want organizations to provide. This study explores the information desires of two social media user groups: non-professional investors and non-investors. We create and validate a social media information content taxonomy using data from three experiments, finding that the information desires of both groups are relatively similar. Specifically, social media users primarily want organizations to provide them with information that addresses them as customers and non-professional investors desire financial information more than non-investors. Across platforms, Facebook is the platform most closely associated with organizational social media communications. JEL Classifications: M31; M37; M41; G24; D83.

scholarly journals Multi-Platform Social Media Use: Little Evidence of Impacts on Adult Well-Being

2020 ◽  
Author(s):  
Sophie Lohmann ◽  
Emilio Zagheni
Keyword(s):  
Social Media ◽  
Media Use ◽  
Scientific Evidence ◽  
General Social Survey ◽  
Well Being ◽  
Young Female ◽  
Social Media Use ◽  
Social Media Platforms ◽  
Data Source ◽  
Using Data

Social media have become a near-ubiquitous part of our lives. The growing concern that their use may alter our well-being has been met with elusive scientific evidence. Existing literature often simplifies social media use as a homogeneous process. In reality, social media use and functions vary widely depending on platform and demographic characteristics of users, and there may be qualitative differences between using few versus many different social media platforms. Using data from the General Social Survey, an underanalyzed data source for this purpose, we characterize intensive social media users and examine how differential platform use impacts well-being. We document substantial heterogeneity in the demography of users and show that intensive users tend to be young, female, more likely to be Black than Hispanic, from high SES backgrounds, from more religious backgrounds, and from families with migration background, compared to both non-users and moderate users. The intensity of social media use seemed largely unrelated to well-being in both unadjusted models and in propensity-score models that adjusted for selection bias and demographic factors. Among middle-aged and older adults, however, intensive social media use may be slightly associated with depressive symptoms. Our findings indicate that although mediums of communication have changed with the advent of social media, these new mediums are not necessarily detrimental to well-being.

Deep Learning and Machine Learning Techniques for Analyzing Travelers' Online Reviews

2022 ◽  
pp. 20-39
Author(s):  
Elliot Mbunge ◽  
Benhildah Muchemwa
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Deep Learning ◽  
Hospitality Industry ◽  
Learning Models ◽  
Online Data ◽  
Social Media Platforms ◽  
Using Data ◽  
Tourism And Hospitality Industry ◽  
Tourism And Hospitality

Social media platforms play a tremendous role in the tourism and hospitality industry. Social media platforms are increasingly becoming a source of information. The complexity and increasing size of tourists' online data make it difficult to extract meaningful insights using traditional models. Therefore, this scoping and comprehensive review aimed to analyze machine learning and deep learning models applied to model tourism data. The study revealed that deep learning and machine learning models are used for forecasting and predicting tourism demand using data from search query data, Google trends, and social media platforms. Also, the study revealed that data-driven models can assist managers and policymakers in mapping and segmenting tourism hotspots and attractions and predicting revenue that is likely to be generated, exploring targeting marketing, segmenting tourists based on their spending patterns, lifestyle, and age group. However, hybrid deep learning models such as inceptionV3, MobilenetsV3, and YOLOv4 are not yet explored in the tourism and hospitality industry.

scholarly journals Tourism trend and network analysis utilizing big data on social media

2018 ◽  
Vol 7 (2.12) ◽  
pp. 312
Author(s):  
Jae Woo Choi ◽  
Hye YoungKim
Keyword(s):  
Social Media ◽  
Big Data ◽  
Mutual Interaction ◽  
Tourism Promotion ◽  
Promotional Efforts ◽  
Social Media Platforms ◽  
Twitter Account ◽  
User Groups ◽  
Media Platform ◽  
Promotional Strategies

Background/Objectives: With evolving trends, tourism is also experiencing more diverse policies and methods of promotion. In particular, with the development and increasing popularity of social media platforms, a new trend is setting in. In line with such changes, the current study sets out to utilize big data on social media platforms to analyze trends in tourism, ways in which tourism elements mutually interact, and analyze patterns, in order to propose tourism promotion strategies and provide related basic data.Methods/Statistical analysis: Analysis on social media platforms were conducted to visually express relationship among nodes and analyze the structure and status of link in quantitative terms. NodeXL is an add-in program to Microsoft Excel; it allows the user to directly collect data from social media platforms to execute matrics, statistics, and visualization. The data was collected from Korea Tourism Organization (KTO)’s Twitter and Facebook accounts. Hashtags (#) on 3,200 posts on the Twitter account were analyzed to compute the tourism trend, and the inter-node interactions and links on the Facebook fan pages were analyzed in terms of network density and centrality to calculate the form and characteristics of social media networks.Findings: By analyzing social media pages that represent promotional efforts for Korean tourism, we were able to find the following results: On the KTO Twitter account, the higher hashtag terms were “eating tour,” and “exciting travel,” which follow the recent tourism trends. However, because of platform restrictions, the Twitter account, rather than engaging in mutual interactions with its users, only tended to deliver information, and was unable to reflect more diverse tourism trends. On Facebook, 348 nodes were actively linked 14.99 times on average, indicating a healthy level of activity. Average degrees of connection was 2.214, which is smaller than average connection distance of small societies, indicating efficient mutual interaction. There were three core user groups, with eleven individuals serving as media nodes, and six users with Eigenvector centrality.Improvements/Applications: Tourism promotion must be executed in line with diverse and latest trends in the field. Because Facebook has a higher level of mutual interaction than Twitter, the account holder can maximize the promotional effects by utilizing individuals that serve as the centrality node. That is to say that promotional strategies that take into account the characteristics of individual social media platform are required. 

scholarly journals Computers can’t give credit: How automatic attribution falls short in an online remixing community

2017 ◽  
Author(s):  
Andrés Monroy-Hernández ◽  
Jazmin Gonzalez-Rivero ◽  
danah boyd ◽  
Benjamin Mako Hill
Keyword(s):  
Social Media ◽  
Online Community ◽  
User Generated Content ◽  
Technological Systems ◽  
Structured Interviews ◽  
Social Media Platforms ◽  
Media Platform ◽  
The Subject ◽  
Using Data ◽  
Content Reuse

In this paper, we explore the role that attribution plays in shaping user reactions to content reuse, or remixing, in a large user-generated content community. We present two studies using data from the Scratch online community – a social media platform where hundreds of thousands of young people share and remix animations and video games. First, we present a quantitative analysis that examines the ef- fects of a technological design intervention introducing au- tomated attribution of remixes on users’ reactions to being remixed. We compare this analysis to a parallel examination of “manual” credit-giving. Second, we present a qualita- tive analysis of twelve in-depth, semi-structured, interviews with Scratch participants on the subject of remixing and at- tribution. Results from both studies suggest that automatic attribution done by technological systems (i.e., the listing of names of contributors) plays a role that is distinct from, and less valuable than, credit which may superficially involve identical information but takes on new meaning when it is given by a human remixer. We discuss the implications of these findings for the designers of online communities and social media platforms.

Political anxiety over social media in Africa to grow

2016 ◽  
Keyword(s):  
Social Media ◽  
Urban Areas ◽  
Presidential Elections ◽  
Sub Saharan Africa ◽  
Virtual Private Networks ◽  
Content Type ◽  
Media Usage ◽  
High Profile ◽  
Social Media Platforms ◽  
Sub Saharan

Subject Outlook for social media in sub-Saharan Africa. Significance Across sub-Saharan Africa (SSA) in recent months, several high-profile protests have been coordinated using social media platforms, including the #ThisFlag demonstrations in Zimbabwe and opposition unrest following Uganda's presidential elections. This is spurring governments to tighten rules governing online platforms and content, and block platforms such as Twitter and Whatsapp. Impacts Opposition activists will increase use of virtual private networks to circumvent blocks on censored websites. Initiatives such as the Forum on China-Africa Media Cooperation will help governments to police online content. Nevertheless, some Western donors will continue to sponsor initiatives, such as radio call-in shows, encouraging free speech. Clampdowns on social media will mainly affect political mobilisation in urban areas, for now, given poor rural internet penetration. Unit and subscription-related costs for web-enabled phones will continue to fall, increasing social media usage.

scholarly journals Cluster Analysis of Automobile Innovative Users Based on Interactive Innovation Value

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Haowen Lin ◽  
Yaping Chen ◽  
Yushu Yang
Keyword(s):  
Social Media ◽  
Cluster Analysis ◽  
Product Reviews ◽  
Pyramid Model ◽  
User Data ◽  
Social Media Platforms ◽  
User Groups ◽  
Product Creation ◽  
Different Characteristics ◽  
Effective Channel

As a new model of product creation, “interactive innovation” can effectively improve the success rate of enterprise product innovation. Because of the wide application of the Internet and the pervasiveness of social media such as forums and blogs, there is a rapid growth of product reviews online. These social media platforms provide an effective channel for interactive innovation between enterprises and users. This paper is based on the innovation application of automobile products. The purpose of this paper is to identify and classify the innovative users in automobile forums and analyze the characteristics of different user groups. First, we summarize six typical characteristics of innovative users and quantify these six characteristics. Second, we make a cluster analysis of user data and divide innovative users into three classes according to the innovation value. And then, based on the different characteristics of three types of users, we present different interactive innovation methods for three types of users. Finally, we construct a pyramid model of innovative user to show the distribution status of various users.

The Politics of Pandemics

2021 ◽  
pp. 48-73
Keyword(s):  
United States ◽  
Social Media ◽  
United Kingdom ◽  
Supply Chain ◽  
The United States ◽  
The Internet ◽  
Nation States ◽  
Social Media Platforms ◽  
The Masses ◽  
Insight Into

The COVID-19 pandemic has colored the politics of 2020 from international to domestic, and the responses by countries have been politicized and limited by various actors. Regimes, both democratic and not, are using the chaotic pandemic environment to consolidate power under the executive, control the masses through decree, and shifting towards national and power bloc supply chains from the international supply chain that has been for all nations in the era of globalization and immediately after. This chapter will provide insight into how various nation-states are using nationalism to combat the pandemic, including the United States, United Kingdom, Chile, Russia, and Hungary. The chapter highlights the availability of the internet and social media platforms to spread mis- and dis-information that can hinder the work of a legitimate government attempting to respond earnestly and effectively to the pandemic.

scholarly journals Spatiotemporal Changes of Urban Rainstorm-Related Micro-Blogging Activities in Response to Rainstorms: A Case Study in Beijing, China

2019 ◽  
Vol 9 (21) ◽  
pp. 4629 ◽  
Author(s):  
Nan Wang ◽  
Yunyan Du ◽  
Fuyuan Liang ◽  
Jiawei Yi ◽  
Huimeng Wang
Keyword(s):  
Social Media ◽  
Natural Disasters ◽  
Human Activities ◽  
Urban Areas ◽  
Global Climate ◽  
Distribution Patterns ◽  
Physical World ◽  
Virtual Space ◽  
Time Lags ◽  
Social Media Platforms

Natural disasters cause significant casualties and losses in urban areas every year. Further, the frequency and intensity of natural disasters have increased significantly over the past couple of decades in the context of global climate change. Understanding how urban dwellers learn about and response to a natural hazard is of great significance as more and more people migrate to cities. Social media has become one of the most essential communication platforms in the virtual space for users to share their knowledge, information, and opinions about almost everything in the physical world. Geo-tagged posts published on different social media platforms contain a huge amount of information that can help us to better understand the dynamics of collective geo-tagged human activities. In this study, we investigated the spatiotemporal distribution patterns of the collective geo-tagged human activities in Beijing when it was afflicted by the “6-22” rainstorm. We used a variety of machine learning and statistical methods to examine the correlations between rainstorm-related microblogs and the rainstorm characteristics at a fine spatial and a fine temporal scale across Beijing. We also studied factors that could be used to explain the changes of the rainstorm-related blogging activities. Our results show that the human response to a disaster is very consistent, though with certain time lags, in the virtual and physical spaces at both the grid and city scales. Such a consistency varies significantly across our study area.

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