ABSTRACT
Real estate is one of the industries that have been most affected by COVID-19. The purpose of this study is to investigate the factors that affect customer use of New Zealand's real estate digital platforms. Trade Me and realestate.co.nz are the two most popular real estate digital platforms with the most significant number of homebuyers in New Zealand. This study uses the reviews on the two platforms from 2018 to 2021. There are a total of 1349 customer reviews on the two platforms, which represent the total population. A sample of 113 reviews from the realestate.co.nz platform and 113 reviews from the Trade Me platform are used for analysis. The findings show that perceived ease of use and usefulness, and information and system quality are the four main factors that affect the willingness of customers to use real estate digital platforms. After the spread of the pandemic, the impact of these factors on customer adoption of the platforms has been reduced. Therefore, the results of this study will be helpful in formulating and developing a digital marketing strategy for the New Zealand real estate industry in the post-pandemic era. © 2022, Global Social Science Institute. All rights reserved.
ABSTRACT
Real estate is one of the industries that have been most affected by COVID-19. The purpose of this study is to investigate the factors that affect customer use of New Zealand's real estate digital platforms. Trade Me and realestate.co.nz are the two most popular real estate digital platforms with the most significant number of homebuyers in New Zealand. This study uses the reviews on the two platforms from 2018 to 2021. There are a total of 1349 customer reviews on the two platforms, which represent the total population. A sample of 113 reviews from the realestate.co.nz platform and 113 reviews from the Trade Me platform are used for analysis. The findings show that perceived ease of use and usefulness, and information and system quality are the four main factors that affect the willingness of customers to use real estate digital platforms. After the spread of the pandemic, the impact of these factors on customer adoption of the platforms has been reduced. Therefore, the results of this study will be helpful in formulating and developing a digital marketing strategy for the New Zealand real estate industry in the post-pandemic era. © 2022, Global Social Science Institute. All rights reserved.
ABSTRACT
The spread of misinformation has become a major concern to our society, and social media is one of its main culprits. Evidently, health misinformation related to vaccinations has slowed down global efforts to fight the COVID-19 pandemic. Studies have shown that fake news spreads substantially faster than real news on social media networks. One way to limit this fast dissemination is by assessing information sources in a semi-automatic way. To this end, we aim to identify users who are prone to spread fake news in Arabic Twitter. Such users play an important role in spreading misinformation and identifying them has the potential to control the spread. We construct an Arabic dataset on Twitter users, which consists of 1,546 users, of which 541 are prone to spread fake news (based on our definition). We use features extracted from users' recent tweets, e.g., linguistic, statistical, and profile features, to predict whether they are prone to spread fake news or not. To tackle the classification task, multiple learning models are employed and evaluated. Empirical results reveal promising detection performance, where an F1 score of 0.73 was achieved by the logistic regression model. Moreover, when tested on a benchmark English dataset, our approach has outperformed the current state-of-the-art for this task. © European Language Resources Association (ELRA).
ABSTRACT
The main objective of this study is to examine the impact of YouTube pandemic advertising on people’s attitudes towards COVID-19. YouTube, as one of the most well-known social platforms, has performed well in this pandemic situation in terms of transmitting vital information through advertising. A quantitative approach was employed and the data were collected from 205 respondents through an online survey. People’s opinions of pandemic advertisements and the dissemination of information through YouTube are both critical factors in determining the impact of YouTube pandemic advertisements on people’s attitudes towards COVID-19. The findings also reveal that there is an impact of COVID-19 advertising on its viewers. Majority of respondents followed instructions with varied degree such as keeping social distance found in the advertised information and became more willing to pay attention to health issues in future. © 2022 by authors.
ABSTRACT
This research aimed to investigate the impact of electronic word-of-mouth (eWOM) in measuring the service quality of the recreational vehicle tourism industry in New Zealand. SERVQUAL model was employed to measure the service quality among RV rental service providers (Maui and Mighty) in New Zealand. Data was collected from the websites of Maui and Mighty which belong to Tourism Holdings Ltd (thl). A quantitative research approach was adopted to analyze the demographic features of the customers. The study analyzed 461 online customer reviews using qualitative method to measure RV tourism service quality in New Zealand. Findings showed that reviews were more about the tangible factors of the services and were most frequently coded and represented 60.4 percent of the total. Nearly 70 percent of reviews were positive about the services of RV tourism industry. This study revealed that eWOM could conclusively indicate the service quality from direct customer feedback. eWOM would benefit customers, business managers, and tourism industry. This study was conducted in February 2020 just before the COVID-19 pandemic so this gave us an opportunity to analyze the customers' reviews during lockdown period. Further research is necessary to ascertain the impact of eWOM on recreational vehicle tourism industry after COVID-19.
ABSTRACT
We describe the fourth edition of the CheckThat! Lab, part of the 2021 Conference and Labs of the Evaluation Forum (CLEF). The lab evaluates technology supporting tasks related to factuality, and covers Arabic, Bulgarian, English, Spanish, and Turkish. Task 1 asks to predict which posts in a Twitter stream are worth fact-checking, focusing on COVID-19 and politics (in all five languages). Task 2 asks to determine whether a claim in a tweet can be verified using a set of previously fact-checked claims (in Arabic and English). Task 3 asks to predict the veracity of a news article and its topical domain (in English). The evaluation is based on mean average precision or precision at rank k for the ranking tasks, and macro-F1 for the classification tasks. This was the most popular CLEF-2021 lab in terms of team registrations: 132 teams. Nearly one-third of them participated: 15, 5, and 25 teams submitted official runs for tasks 1, 2, and 3, respectively. © 2021, Springer Nature Switzerland AG.
ABSTRACT
We present an overview of Task 1 of the fourth edition of the CheckThat! Lab, part of the 2021 Conference and Labs of the Evaluation Forum (CLEF). The task asks to predict which posts in a Twitter stream are worth fact-checking, focusing on COVID-19 and politics in five languages: Arabic, Bulgarian, English, Spanish, and Turkish. A total of 15 teams participated in this task and most submissions managed to achieve sizable improvements over the baselines using Transformer-based models such as BERT and RoBERTa. Here, we describe the process of data collection and the task setup, including the evaluation measures, and we give a brief overview of the participating systems. We release to the research community all datasets from the lab as well as the evaluation scripts, which should enable further research in check-worthiness estimation for tweets and political debates. © 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).