The traces of ecotourism in a digital world: spatial and trend analysis of geotagged photographs on social media and Google search data for sustainable development

2020 ◽  
Vol 11 (2) ◽  
pp. 183-202
Author(s):  
Hanyoung Go ◽  
Myunghwa Kang ◽  
Yunwoo Nam
Keyword(s):  
Social Media ◽  
Sustainable Development ◽  
Spatial Analysis ◽  
Google Trends ◽  
Internet Search ◽  
Content Type ◽  
Digital World ◽  
Search Data ◽  
Google Search ◽  
Over Time

Purpose This paper aims to track how ecotourism has been presented in a digital world over time using geotagged photographs and internet search data. Ecotourism photographs and Google Trends search data are used to evaluate tourist perceptions of ecotourism by developing a categorization of essential attributes, examining the relation of ecotourism and sustainable development, and measuring the popularity of the ecotourism sites. Design/methodology/approach The researchers collected geotagged photographs from Flickr.com and downloaded Google search data from Google Trends. An integrative approach of content, trend and spatial analysis was applied to develop ecotourism categories and investigate tourist perceptions of ecotourism. First, the authors investigate ecotourism geotagged photographs on a social media to comprehend tourist perceptions of ecotourism by developing a categorization of key ecotourism attributes and measuring the popularity of the ecotourism sites. Second, they examined how ecotourism has been related with sustainable development using internet search data and investigate the trends in search data. Third, spatial analysis using GIS maps was used to visualize the spatial-temporal changes of photographs and tourist views throughout the world. Findings This study identified three primary themes of ecotourism perceptions and 13 categories of ecotourism attributes. Interest over time about ecotourism was mostly presented as its definitions in Google Trends. The result indicates that tracked ecotourism locations and tourist footprints are not congruent with the popular regions of ecotourism Google search. Originality/value This research follows the changing trends in ecotourism over a decade using geotagged photographs and internet search data. The evaluation of the global ecotourism trend provides important insights for global sustainable tourism development and actual tourist perception. Analyzing the trend of ecotourism is a strategic approach to assess the achievement of UN sustainable development goals. Factual perspectives and insights into how tourists are likely to seek and perceive natural attractions are valuable for a range of audiences, such as tourism industries and governments.

Sentiment-based commercial real estate forecasting with Google search volume data

2014 ◽  
Vol 32 (6) ◽  
pp. 540-569 ◽  
Author(s):  
Marian Alexander Dietzel ◽  
Nicole Braun ◽  
Wolfgang Schäfers
Keyword(s):  
Real Estate ◽  
Commercial Real Estate ◽  
Google Trends ◽  
Internet Search ◽  
Price Indices ◽  
Search Query ◽  
Content Type ◽  
Forecasting Models ◽  
Search Data ◽  
Google Search

Purpose – The purpose of this paper is to examine internet search query data provided by “Google Trends”, with respect to its ability to serve as a sentiment indicator and improve commercial real estate forecasting models for transactions and price indices. Design/methodology/approach – This paper examines internet search query data provided by “Google Trends”, with respect to its ability to serve as a sentiment indicator and improve commercial real estate forecasting models for transactions and price indices. Findings – The empirical results show that all models augmented with Google data, combining both macro and search data, significantly outperform baseline models which abandon internet search data. Models based on Google data alone, outperform the baseline models in all cases. The models achieve a reduction over the baseline models of the mean squared forecasting error for transactions and prices of up to 35 and 54 per cent, respectively. Practical implications – The results suggest that Google data can serve as an early market indicator. The findings of this study suggest that the inclusion of Google search data in forecasting models can improve forecast accuracy significantly. This implies that commercial real estate forecasters should consider incorporating this free and timely data set into their market forecasts or when performing plausibility checks for future investment decisions. Originality/value – This is the first paper applying Google search query data to the commercial real estate sector.

Where and When Can We Use Google Trends to Measure Issue Salience?

2013 ◽  
Vol 46 (02) ◽  
pp. 280-290 ◽  
Author(s):  
Jonathan Mellon
Keyword(s):  
Google Trends ◽  
Issue Salience ◽  
Social Scientists ◽  
Download Time ◽  
The World ◽  
Search Data ◽  
Data Source ◽  
Almost All ◽  
Google Search ◽  
Over Time

Google search data have several major advantages over traditional survey data. First, the high costs of running frequent surveys mean that most survey questions are only asked occasionally making comparisons over time difficult. By contrast, Google Trends provides information on search trends measured weekly. Second, there are many countries where surveys are only conducted sporadically, whereas Google search data are available anywhere in the world where sufficient numbers of people use its search engine. The Google Trends website allows researchers to download data for almost all countries at no cost and to download time series of any search term's popularity over time (provided enough people have searched for it). For these reasons, Google Trends is an attractive data source for social scientists.

Sustainability-oriented strategy and Sustainable Development Goals

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Priscila Borin de Oliveira Claro ◽  
Nathalia Ramajo Esteves
Keyword(s):  
Sustainable Development ◽  
Sustainable Development Goals ◽  
Small Sample ◽  
Theoretical Argument ◽  
Negative Externalities ◽  
Global Compact ◽  
Content Type ◽  
For Profit ◽  
Development Goals ◽  
Over Time

PurposeSustainability-oriented strategies involve considering all possible environmental, social and economic factors that impact stakeholders and sustainable development. They could be a crucial contribution of the private sector to Sustainable Development Goals (SDGs). The study’s objective is twofolded. First, the authors want to discover if enterprises doing business in Brazil are contemplating the SDGs in their strategies. Second, the authors want to identify the external and internal factors that motivate them.Design/methodology/approachThe authors collected data through an online survey with employees from Global Compact signatories in Brazil. From a list of 335 for-profit enterprises, the authors got back 132 answers. The sample comprises Brazilian enterprises that only operate in the Brazilian market, Brazilian multinational enterprises (MNEs) and foreign multinationals operating in Brazilian and international markets. For this study, the MNEs’ group comprises Brazilian multinationals and foreign multinationals (MNEs). To characterize the sample and identify the motivating factors, the authors conducted a descriptive analysis. To compare the domestic and MNEs’ mean differences regarding the factors that influenced their strategies and the SDGs, the authors performed Mann–Whitney's U-test.FindingsThe results of the study show that enterprises are addressing the SDGs in their strategies. All internal and external driving factors are similar for domestic and MNEs, except for the value chain's negative externalities. MNEs are more prone to consider their negative externalities, which is a positive trend. Finally, results suggest that both groups of enterprises consider the 17 goals in their strategies, contrary to the theoretical argument that multinationals suffer more pressure because of their broad geographic scope.Research limitations/implicationsThe database of the study involves data collected through a self-response survey. Thus, the authors cannot discuss the effectiveness of real SDGs' strategies once enterprises' discourse on sustainability does not always correspond with practices. Therefore, the authors suggest that researchers address the results of implemented strategies on the SDGs over time to check for improvements and new developments.Practical implicationsThe authors suggest frequent materiality assessment of domestic enterprises' supply chain and articulation of explicit purposes around the selected SDGs, including setting key performance indicators (KPIs) and monitoring progress.Social implicationsThe authors believe that enterprises and decision makers should recognize their essential role to bend the curve on SDGs and shift their behavior toward strategic choices that could contribute to their positive performance over time, without contributing to environmental degradation and socioeconomic chaos.Originality/valuePublication on how enterprises address the SDGs in Brazil is relatively scarce. This study provides some answers to that by focusing on the factors influencing sustainability-oriented strategies on the SDGs. Besides, most previous studies consider a small sample of enterprises and are industry specific or focus on the effects of the SDGs in public policy. The sample of this study is diverse and represents 42% of the for-profit signatories of the Global Compact in Brazil.

Dynamics of convergence behaviour in social media crisis communication – a complexity perspective

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Milad Mirbabaie ◽  
Stefan Stieglitz ◽  
Felix Brünker
Keyword(s):  
Social Media ◽  
Emergency Management ◽  
Crisis Communication ◽  
Design Methodology ◽  
Content Type ◽  
The Public ◽  
Analysis Techniques ◽  
Twitter Data ◽  
Convergence Behaviour ◽  
Over Time

PurposeThe purpose of this study is to investigate communication on Twitter during two unpredicted crises (the Manchester bombings and the Munich shooting) and one natural disaster (Hurricane Harvey). The study contributes to understanding the dynamics of convergence behaviour archetypes during crises.Design/methodology/approachThe authors collected Twitter data and analysed approximately 7.5 million relevant cases. The communication was examined using social network analysis techniques and manual content analysis to identify convergence behaviour archetypes (CBAs). The dynamics and development of CBAs over time in crisis communication were also investigated.FindingsThe results revealed the dynamics of influential CBAs emerging in specific stages of a crisis situation. The authors derived a conceptual visualisation of convergence behaviour in social media crisis communication and introduced the terms hidden and visible network-layer to further understanding of the complexity of crisis communication.Research limitations/implicationsThe results emphasise the importance of well-prepared emergency management agencies and support the following recommendations: (1) continuous and (2) transparent communication during the crisis event as well as (3) informing the public about central information distributors from the start of the crisis are vital.Originality/valueThe study uncovered the dynamics of crisis-affected behaviour on social media during three cases. It provides a novel perspective that broadens our understanding of complex crisis communication on social media and contributes to existing knowledge of the complexity of crisis communication as well as convergence behaviour.

scholarly journals Interest in COVID-19 in Latin America and the Caribbean: an infodemiological study using Google Trends

2021 ◽  
Vol 37 (10) ◽  
Author(s):  
Carlos Jesús Aragón-Ayala ◽  
Julissa Copa-Uscamayta ◽  
Luis Herrera ◽  
Frank Zela-Coila ◽  
Cender Udai Quispe-Juli
Keyword(s):  
Latin America ◽  
Response Time ◽  
Latin American ◽  
Google Trends ◽  
Public Attention ◽  
Search Data ◽  
The Caribbean ◽  
Global And Local ◽  
Google Search ◽  
Latin American And Caribbean

Infodemiology has been widely used to assess epidemics. In light of the recent pandemic, we use Google Search data to explore online interest about COVID-19 and related topics in 20 countries of Latin America and the Caribbean. Data from Google Trends from December 12, 2019, to April 25, 2020, regarding COVID-19 and other related topics were retrieved and correlated with official data on COVID-19 cases and with national epidemiological indicators. The Latin American and Caribbean countries with the most interest for COVID-19 were Peru (100%) and Panama (98.39%). No correlation was found between this interest and national epidemiological indicators. The global and local response time were 20.2 ± 1.2 days and 16.7 ± 15 days, respectively. The duration of public attention was 64.8 ± 12.5 days. The most popular topics related to COVID-19 were: the country’s situation (100 ± 0) and coronavirus symptoms (36.82 ± 16.16). Most countries showed a strong or moderated (r = 0.72) significant correlation between searches related to COVID-19 and daily new cases. In addition, the highest significant lag correlation was found on day 13.35 ± 5.76 (r = 0.79). Interest shown by Latin American and Caribbean countries for COVID-19 was high. The degree of online interest in a country does not clearly reflect the magnitude of their epidemiological indicators. The response time and the lag correlation were greater than in European and Asian countries. Less interest was found for preventive measures. Strong correlation between searches for COVID-19 and daily new cases suggests a predictive utility.

Assessing E-Government Success Strategies using Internet Search Data

2013 ◽  
pp. 289-307
Author(s):  
Katherine M. Boland ◽  
John G. McNutt
Keyword(s):  
Longitudinal Data ◽  
Google Trends ◽  
Success Strategies ◽  
Internet Search ◽  
Dynamic Nature ◽  
Government Programs ◽  
Search Data ◽  
Research Questions ◽  
Internet Searches ◽  
Citizen Response

Evaluating e-government programs can be a challenging task. While determining program features and capacity are relatively straightforward processes, exploring the more dynamic nature of citizen response to e-government is difficult. Fortunately, recent advances in Internet search technology offer researchers new opportunities to address these research questions. Innovations, such as Google Trends and Google Insights for Search, have made longitudinal data on Internet searches accessible to scholars. The availability of this data opens a number of possible research avenues regarding e-government.

Seasonal variations and public search interests in Toxoplasma: a 16-year retrospective analysis of big data on Google Trends

2020 ◽  
Author(s):  
Lei Liu ◽  
Peng Wang ◽  
Su-Qin Jiang ◽  
Zi-Rong Zhong ◽  
Ting-Zheng Zhan ◽  
...  
Keyword(s):  
New Zealand ◽  
Google Trends ◽  
Seasonal Patterns ◽  
Search Term ◽  
Internet Search ◽  
Search Volume ◽  
The Usa ◽  
Search Data ◽  
Relative Search Volume ◽  
The Uk

Abstract Background This study aims to understand whether there is a seasonal change in the internet search interest for Toxoplasma by using the data derived from Google Trends (GT). Methods The present study searched for the relative search volume (RSV) for the search term ‘Toxoplasma’ in GT within six major English-speaking countries (Australia, New Zealand [Southern Hemisphere] and Canada, Ireland, the UK and the USA [Northern Hemisphere] from 1 January 2004 to 31 December 2019, utilizing the category of ‘health’. Data regarding the RSV of Toxoplasma was obtained and further statistical analysis was performed in R software using the ‘season’ package. Results There were significantly seasonal patterns for the RSV of the search term ‘Toxoplasma’ in five countries (all p<0.05), except for the UK. A peak in December–March and a trough in July–September (Canada, Ireland, the UK and the USA) were observed, while a peak in June/August and a trough in December/February (Australia, New Zealand) were also found. Moreover, the presence of seasonal patterns regarding RSV for ‘Toxoplasma’ between the Southern and Northern Hemispheres was also found (both p<0.05), with a reversed meteorological month. Conclusions Overall, our study revealed the seasonal variation for Toxoplasma in using internet search data from GT, providing additional evidence on seasonal patterns in Toxoplasma.

Using Twitter sentiment and emotions analysis of Google Trends for decisions making

2017 ◽  
Vol 51 (3) ◽  
pp. 322-350 ◽  
Author(s):  
Ernesto D’Avanzo ◽  
Giovanni Pilato ◽  
Miltiadis Lytras
Keyword(s):  
Web Service ◽  
Language Processing ◽  
Real World ◽  
Consumer Electronics ◽  
Google Trends ◽  
Search Query ◽  
Content Type ◽  
Monitoring Task ◽  
Twitter Users ◽  
Google Search

Purpose An ever-growing body of knowledge demonstrates the correlation among real-world phenomena and search query data issued on Google, as showed in the literature survey introduced in the following. The purpose of this paper is to introduce a pipeline, implemented as a web service, which, starting with recent Google Trends, allows a decision maker to monitor Twitter’s sentiment regarding these trends, enabling users to choose geographic areas for their monitors. In addition to the positive/negative sentiments about Google Trends, the pipeline offers the ability to view, on the same dashboard, the emotions that Google Trends triggers in the Twitter population. Such a set of tools, allows, as a whole, monitoring real-time on Twitter the feelings about Google Trends that would otherwise only fall into search statistics, even if useful. As a whole, the pipeline has no claim of prediction over the trends it tracks. Instead, it aims to provide a user with guidance about Google Trends, which, as the scientific literature demonstrates, is related to many real-world phenomena (e.g. epidemiology, economy, political science). Design/methodology/approach The proposed experimental framework allows the integration of Google search query data and Twitter social data. As new trends emerge in Google searches, the pipeline interrogates Twitter to track, also geographically, the feelings and emotions of Twitter users about new trends. The core of the pipeline is represented by a sentiment analysis framework that make use of a Bayesian machine learning device exploiting deep natural language processing modules to assign emotions and sentiment orientations to a collection of tweets geolocalized on the microblogging platform. The pipeline is accessible as a web service for any user authorized with credentials. Findings The employment of the pipeline for three different monitoring task (i.e. consumer electronics, healthcare, and politics) shows the plausibility of the proposed approach in order to measure social media sentiments and emotions concerning the trends emerged on Google searches. Originality/value The proposed approach aims to bridge the gap among Google search query data and sentiments that emerge on Twitter about these trends.

Gulf split heralds new uses for cyber capabilities

2018 ◽  
Keyword(s):  
Social Media ◽  
Muslim Brotherhood ◽  
Fake News ◽  
Content Type ◽  
State Action ◽  
News Agency ◽  
Policy Goals ◽  
Actual Conflict ◽  
The Muslim Brotherhood ◽  
Over Time

Significance The Qatar crisis in June 2017 was similarly sparked by a piece of ‘fake news’ planted on Doha’s national news agency showing the Qatari emir as expressing support for Iran and the Muslim Brotherhood movement. The incidents are part of a rising trend of offensive cyber actions and government-backed social media contestation in the region. They may also be the first examples of a combined cyber and physical strategy achieving core foreign policy goals just short of actual conflict. Impacts The GCC’s high online presence and draconian regulatory framework will make social media a key arena for covert state action. Interpretation of past events will fragment, meaning divisions such as the GCC split harden over time and become difficult to reverse. As GCC states’ attitudes to Iran diverge further, their Western allies will find regional diplomacy more labour-intensive.

Discovering business information from search engine query data

2014 ◽  
Vol 38 (4) ◽  
pp. 562-574 ◽  
Author(s):  
Liwen Vaughan
Keyword(s):  
Search Engine ◽  
Business Performance ◽  
Google Trends ◽  
Volume Data ◽  
Content Type ◽  
Search Volume ◽  
Business Information ◽  
Position Data ◽  
Search Data ◽  
One Year

Purpose – The purpose of this paper is to examine the feasibility of discovering business information from search engine query data. Specifically the study tried to determine whether search volumes of company names are correlated with the companies’ business performance and position data. Design/methodology/approach – The top 50 US companies in the 2012 Fortune 500 list were included in the study. The following business performance and position data were collected: revenues, profits, assets, stockholders’ equity, profits as a percentage of revenues, and profits as a percentage of assets. Data on the search volumes of the company names were collected from Google Trends, which is based on search queries users enter into Google. Google Trends data were collected in the two scenarios of worldwide searches and US searches. Findings – The study found significant correlations between search volume data and business performance and position data, suggesting that search engine query data can be used to discover business information. Google Trends’ worldwide search data were better than the US domestic search data for this purpose. Research limitations/implications – The study is limited to only one country and to one year of data. Practical implications – Publicly available search engine query data such as those from Google Trends can be used to estimate business performance and position data which are not always publicly available. Search engine query data are timelier than business data. Originality/value – This is the first study to establish a relationship between search engine query data and business performance and position data.

Sign in / Sign up

Export Citation Format

Share Document