Diffusion of blockchain technology

2019 ◽  
Vol 32 (5) ◽  
pp. 735-757 ◽  
Author(s):  
Purva Grover ◽  
Arpan Kumar Kar ◽  
Marijn Janssen
Keyword(s):  
Social Media ◽  
Decision Process ◽  
Manufacturing Industry ◽  
Content Type ◽  
Academic Literature ◽  
Blockchain Technology ◽  
Data Source ◽  
Single Data ◽  
Almost All ◽  
Media Data

Purpose Although blockchain is often discussed, its actual diffusion seems to be varying for different industries. The purpose of this paper is to explore the blockchain technology diffusion in different industries through a combination of academic literature and social media (Twitter). Design/methodology/approach The insights derived from the academic literature and social media have been used to classify industries into five stages of the innovation-decision process, namely, knowledge, persuasion, decision, implementation and confirmation (Rogers, 1995). Findings Blockchain is found to be diffused in almost all industries, but the level of diffusion varies. The analysis highlights that manufacturing industry is at the knowledge stage. Further public administration is at persuasion stage. Subsequently, transportation, communications, electric, gas and sanitary services and trading industry had reached to the decision stage. Then, services industries have reached to implementation stage while finance, insurance and real estate industries are the innovators of blockchain technologies and have reached the confirmation stage of innovation-decision process. Practical implications Actual implementations of blockchain technology are still in its infancy stage for most of the industries. The findings suggest that specific industries are developing specific blockchain applications. Originality/value To the best of the authors’ knowledge this is the first study which is using social media data for investigating the diffusion of blockchain in industries. The results show that the combination of Twitter and academic literature analysis gives better insights into diffusion than a single data source.

scholarly journals Using social media data to map the areas most affected by ISIS in Syria

2020 ◽  
Vol 26 (1) ◽  
pp. 464-470
Author(s):  
Mohamad Hasan
Keyword(s):  
Social Media ◽  
Language Processing ◽  
Geographical Information ◽  
Data Mapping ◽  
Islamic State ◽  
Social Media Data ◽  
The Social ◽  
Mapping Process ◽  
Data Source ◽  
Media Data

This paper presents a model to collect, save, geocode, and analyze social media data. The model is used to collect and process the social media data concerned with the ISIS terrorist group (the Islamic State in Iraq and Syria), and to map the areas in Syria most affected by ISIS accordingly to the social media data. Mapping process is assumed automated compilation of a density map for the geocoded tweets. Data mined from social media (e.g., Twitter and Facebook) is recognized as dynamic and easily accessible resources that can be used as a data source in spatial analysis and geographical information system. Social media data can be represented as a topic data and geocoding data basing on the text of the mined from social media and processed using Natural Language Processing (NLP) methods. NLP is a subdomain of artificial intelligence concerned with the programming computers to analyze natural human language and texts. NLP allows identifying words used as an initial data by developed geocoding algorithm. In this study, identifying the needed words using NLP was done using two corpora. First corpus contained the names of populated places in Syria. The second corpus was composed in result of statistical analysis of the number of tweets and picking the words that have a location meaning (i.e., schools, temples, etc.). After identifying the words, the algorithm used Google Maps geocoding API in order to obtain the coordinates for posts.

Destination image through social media analytics and survey method

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Michael S. Lin ◽  
Yun Liang ◽  
Joanne X. Xue ◽  
Bing Pan ◽  
Ashley Schroeder
Keyword(s):  
Social Media ◽  
Survey Data ◽  
Destination Image ◽  
Social Media Analytics ◽  
Tourism Destination ◽  
Social Media Data ◽  
Content Type ◽  
Holistic Understanding ◽  
Intercept Survey ◽  
Media Data

Purpose Recent tourism research has adopted social media analytics (SMA) to examine tourism destination image (TDI) and gain timely insights for marketing purposes. Comparing the methodologies of SMA and intercept surveys would provide a more in-depth understanding of both methodologies and a more holistic understanding of TDI than each method on their own. This study aims to investigate the unique merits and biases of SMA and a traditional visitor intercept survey. Design/methodology/approach This study collected and compared data for the same tourism destination from two sources: responses from a visitor intercept survey (n = 1,336) and Flickr social media photos and metadata (n = 11,775). Content analysis, machine learning and text analysis techniques were used to analyze and compare the destination image represented from both methods. Findings The results indicated that the survey data and social media data shared major similarities in the identified key image phrases. Social media data revealed more diverse and more specific aspects of the destination, whereas survey data provided more insights in specific local landmarks. Survey data also included additional subjective judgment and attachment towards the destination. Together, the data suggested that social media data should serve as an additional and complementary source of information to traditional survey data. Originality/value This study fills a research gap by comparing two methodologies in obtaining TDI: SMA and a traditional visitor intercept survey. Furthermore, within SMA, photo and metadata are compared to offer additional awareness of social media data’s underlying complexity. The results showed the limitations of text-based image questions in surveys. The findings provide meaningful insights for tourism marketers by having a more holistic understanding of TDI through multiple data sources.

Determining factors of participants' attitudes toward the ethics of social media data research

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yi Chen ◽  
Chuanfu Chen ◽  
Si Li
Keyword(s):  
Social Media ◽  
Structural Equation ◽  
Ethical Issues ◽  
Equation Modeling ◽  
Research Subjects ◽  
Social Media Data ◽  
Content Type ◽  
Mediating Role ◽  
Determining Factors ◽  
Media Data

PurposeThe purpose of this study was to investigate the participants' attitudes toward the ethical issues caused by collecting social media data (SMD) for research, as well as the effects of familiarity, trust and altruism on the participants' attitudes toward the ethics of SMD research. It is hoped that through this study, scholars will be reminded to respect participants and engage in ethical reflection when using SMD in research.Design/methodology/approachThis study adopted social media users as its research subjects and used Sina Microblog, the world's largest Chinese social media platform, as the example. Based on the 320 valid responses collected from a survey, structural equation modeling was employed to examine the research model.FindingsThe results indicated that altruism, familiarity and trust have significant influences on participants' attitudes toward the ethics of SMD research, and familiarity also influences attitudes through the mediating role of trust and altruism.Originality/valueThis study explored the mechanism underlying the relationship between the determining factors and participants' attitudes toward the ethics of SMD research, and the results demonstrated that the informed consent mechanism is an effective way to communicate with participants and that the guiding responsibility of the platform should be improved to standardize SMD research.

Forecasting daily attraction demand using big data from search engines and social media

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Fengjun Tian ◽  
Yang Yang ◽  
Zhenxing Mao ◽  
Wenyue Tang
Keyword(s):  
Social Media ◽  
Big Data ◽  
Search Engine ◽  
Elastic Net ◽  
Percentage Error ◽  
Mean Square ◽  
Social Media Data ◽  
Content Type ◽  
Forecasting Error ◽  
Media Data

Purpose This paper aims to compare the forecasting performance of different models with and without big data predictors from search engines and social media. Design/methodology/approach Using daily tourist arrival data to Mount Longhu, China in 2018 and 2019, the authors estimated ARMA, ARMAX, Markov-switching auto-regression (MSAR), lasso model, elastic net model and post-lasso and post-elastic net models to conduct one- to seven-days-ahead forecasting. Search engine data and social media data from WeChat, Douyin and Weibo were incorporated to improve forecasting accuracy. Findings Results show that search engine data can substantially reduce forecasting error, whereas social media data has very limited value. Compared to the ARMAX/MSAR model without big data predictors, the corresponding post-lasso model reduced forecasting error by 39.29% based on mean square percentage error, 33.95% based on root mean square percentage error, 46.96% based on root mean squared error and 45.67% based on mean absolute scaled error. Practical implications Results highlight the importance of incorporating big data predictors into daily demand forecasting for tourism attractions. Originality/value This study represents a pioneering attempt to apply the regularized regression (e.g. lasso model and elastic net) in tourism forecasting and to explore various daily big data indicators across platforms as predictors.

scholarly journals Tackling social media data analysis

2019 ◽  
Vol 22 (2) ◽  
pp. 94-113 ◽  
Author(s):  
Violetta Wilk ◽  
Geoffrey N. Soutar ◽  
Paul Harrigan
Keyword(s):  
Social Media ◽  
Data Analysis ◽  
Exploratory Research ◽  
Online Data ◽  
Social Media Data ◽  
Content Type ◽  
Advantages And Disadvantages ◽  
Brand Advocacy ◽  
Qualitative Data Analysis Software ◽  
Media Data

PurposeThis paper aims to offer insights into the ways two computer-aided qualitative data analysis software (CAQDAS) applications (QSR NVivo and Leximancer) can be used to analyze big, text-based, online data taken from consumer-to-consumer (C2C) social media communication.Design/methodology/approachThis study used QSR NVivo and Leximancer, to explore 200 discussion threads containing 1,796 posts from forums on an online open community and an online brand community that involved online brand advocacy (OBA). The functionality, in particular, the strengths and weaknesses of both programs are discussed. Examples of the types of analyses each program can undertake and the visual output available are also presented.FindingsThis research found that, while both programs had strengths and weaknesses when working with big, text-based, online data, they complemented each other. Each contributed a different visual and evidence-based perspective; providing a more comprehensive and insightful view of the characteristics unique to OBA.Research limitations/implicationsQualitative market researchers are offered insights into the advantages and disadvantages of using two different software packages for research projects involving big social media data. The “visual-first” analysis, obtained from both programs can help researchers make sense of such data, particularly in exploratory research.Practical implicationsThe paper provides practical recommendations for analysts considering which programs to use when exploring big, text-based, online data.Originality/valueThis paper answered a call to action for further research and demonstration of analytical programs of big, online data from social media C2C communication and makes strong suggestions about the need to examine such data in a number of ways.

scholarly journals The UK private housebuilding sector: social media perspectives

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Nigel Craig ◽  
Nick Pilcher ◽  
Rebecca MacKenzie ◽  
Chris Boothman
Keyword(s):  
Social Media ◽  
Customer Service ◽  
Social Media Data ◽  
Negative Image ◽  
Content Type ◽  
Safety Standards ◽  
The Uk ◽  
House Building ◽  
Media Data ◽  
Media Forum

Purpose The UK private housebuilding sector is the key supplier of new-build homes for customers, constituting a fifth of the entire UK construction industry. Yet, despite the high average cost of houses, and official reports advocating improvement, the sector remains blighted by criticism and a negative image of its quality. However, social media now offers customers new sources of advice and information. In this context, the purpose of this paper is to analyse social media forum posts from new-build homebuyers to reveal perceptions of the industry and illustrate the value of such data for others. Design/methodology/approach This paper presents and thematically analyses 147 comment posts from nine online Facebook forums under the themes of safety; standards; quality; workmanship; customer service; finance and money; advice; National House Building Council; ombudsman; and page closures. Findings Customers express frustration, anger, feelings of neglect and of an abdication of responsibility by the sector. Fundamentally, change is suggested at a systemic level, and it is urged this occurs through powerful and independent bodies. Originality/value To date, social media data has not been analysed in the context of the housebuilding sector. Yet, such data is key not only for its open and wide-reaching nature but also because it can be incorporated into government reports. It is hoped such data will be used by the new home ombudsman the UK Government hopes to establish in 2020 and help rectify many of the performance issues experienced and protect homebuyers.

scholarly journals Timing matters: crisis severity and occupancy rate forecasts in social unrest periods

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Richard T.R. Qiu ◽  
Anyu Liu ◽  
Jason L. Stienmetz ◽  
Yang Yu
Keyword(s):  
Social Media ◽  
Time Series ◽  
Hospitality Industry ◽  
Demand Forecasting ◽  
Time Series Model ◽  
Social Media Data ◽  
Forecasting Accuracy ◽  
Social Unrest ◽  
Content Type ◽  
Media Data

Purpose The impact of demand fluctuation during crisis events is crucial to the dynamic pricing and revenue management tactics of the hospitality industry. The purpose of this paper is to improve the accuracy of hotel demand forecast during periods of crisis or volatility, taking the 2019 social unrest in Hong Kong as an example. Design/methodology/approach Crisis severity, approximated by social media data, is combined with traditional time-series models, including SARIMA, ETS and STL models. Models with and without the crisis severity intervention are evaluated to determine under which conditions a crisis severity measurement improves hotel demand forecasting accuracy. Findings Crisis severity is found to be an effective tool to improve the forecasting accuracy of hotel demand during crisis. When the market is volatile, the model with the severity measurement is more effective to reduce the forecasting error. When the time of the crisis lasts long enough for the time series model to capture the change, the performance of traditional time series model is much improved. The finding of this research is that the incorporating social media data does not universally improve the forecast accuracy. Hotels should select forecasting models accordingly during crises. Originality/value The originalities of the study are as follows. First, this is the first study to forecast hotel demand during a crisis which has valuable implications for the hospitality industry. Second, this is also the first attempt to introduce a crisis severity measurement, approximated by social media coverage, into the hotel demand forecasting practice thereby extending the application of big data in the hospitality literature.

scholarly journals SUMBANGAN BAHASA INDONESIA TERHADAP PEMEKARAN KOSAKATA BAHASA BUGIS: KASUS PENGGUNAAN BAHASA BUGIS PADA MEDIA SOSIAL FACEBOOK (The Contribution of Indonesian Language on Buginese Vocabulary Development: A Case Study of the Use of Buginese on Facebook Social Media)

SAWERIGADING ◽  
2019 ◽  
Vol 25 (2) ◽  
pp. 107
Author(s):  
Muhammad Darwis ◽  
Kamsinah Kamsinah
Keyword(s):  
Social Media ◽  
Research Strategies ◽  
Loan Words ◽  
Code Mixing ◽  
Word Categorization ◽  
The Social ◽  
Data Source ◽  
Media Data ◽  
Bahasa Indonesia

AbstrakThe aim of this research is: (1) to identify the forms and categories of Indonesian words that are absorbed into Buginese sentences and (2) to reveal the reasons for the use of Indonesian elements into Buginese sentences by Facebookers in the social media. Data on this qualitative research obtained from social media ‘Facebook’. The data source of this research is the Facebookers who are members of the MABBASA UUGIE KU PESBU’ group, November 2013 to April 2014 edition. Data analyzed are Buginese sentences consisting of three to five examples of Buginese sentences containing Indonesian elements in the form of words, phrases or clauses taken purposively. Furthermore, the analysis was carried out with grounded research strategies. The results of this research indicate that (1) Buginese language can survive as a means of communication within Buginese ethnic groups when writing on the social media ‘Facebook’, due to they have obtained vocabulary contributions from Indonesian in the form of the basic word, affixation word, and phrase. In word categorization, the loan words consist of nouns, verbs, adjectives, and conjunctions. Then, (2) the use of the Indonesian language elements has four main reasons, namely (a) filling in the blanks, (b) adding equivalence variations, (c) clarifying the meaning, and (d) interference. Reasons (a) to (c) can take the form of code-mixing and code-switching. AbstrakPenelitian ini bertujuan: (1) mengidentifikasi bentuk dan kategori kata bahasa Indonesia (bI) yang  terserap ke dalam kalimat-kalimat bahasa Bugis (bB) dan (2) mengungkap alasan-alasan penggunaan unsur-unsur bI tersebut ke dalam kalimat bB oleh para Facebooker di media sosial. Data penelitian kualitatif ini diperoleh dari media sosial Facebook. Sumber data penelitian ini ialah para Facebooker yang menjadi anggota grup MABBASA UUGIE KU PESBU’ edisi bulan November 2013 s.d. bulan April 2014. Data yang dianalisis ialah kalimat-kalimat ber-bB yang terdiri atas tiga sampai lima contoh kalimat ber-bB yang berisi unsur-unsur bI, yang berupa kata, frasa, atau klausa, yang diambil secara purposif. Selanjutnya, analisis dilakukan dengan upaya grounded research. Hasil penelitian ini menunjukkan bahwa (1) bB dapat bertahan hidup sebagai sarana perhubungan intern suku Bugis dalam komunikasi tulisan sosial Facebook karena memperoleh sumbangan kosakata bI yang berbentuk kata dasar, kata berimbuhan, dan frasa atau ungkapan. Dari segi kategorisasi kata, unsur-unsur serapan tersebut terdiri atas kata benda, kata kerja, kata sifat, dan kata sambung. Kemudian, (2) penggunaan unsur-unsur bI tersebut memiliki empat alasan utama, yaitu (a) mengisi kekosongan, (b) menambah variasi kesepadanan, (c) memperjelas pemaknaan, dan (d) interferensi. Alasan (a) sampai dengan (c) dapat mengambil bentuk campur kode dan alih kode.   

scholarly journals EXTRACTING AND COMPARING PLACES USING GEO-SOCIAL MEDIA

2015 ◽  
Vol II-3/W5 ◽  
pp. 311-316
Author(s):  
F. O. Ostermann ◽  
H. Huang ◽  
G. Andrienko ◽  
N. Andrienko ◽  
C. Capineri ◽  
...  
Keyword(s):  
Social Media ◽  
Semantic Similarity ◽  
Data Set ◽  
Social Media Data ◽  
Temporal Clustering ◽  
Depth Study ◽  
Data Source ◽  
Spatio Temporal ◽  
Media Data

Increasing availability of Geo-Social Media (e.g. Facebook, Foursquare and Flickr) has led to the accumulation of large volumes of social media data. These data, especially geotagged ones, contain information about perception of and experiences in various environments. Harnessing these data can be used to provide a better understanding of the semantics of places. We are interested in the similarities or differences between different Geo-Social Media in the description of places. This extended abstract presents the results of a first step towards a more in-depth study of semantic similarity of places. Particularly, we took places extracted through spatio-temporal clustering from one data source (Twitter) and examined whether their structure is reflected semantically in another data set (Flickr). Based on that, we analyse how the semantic similarity between places varies over space and scale, and how Tobler's first law of geography holds with regards to scale and places.

scholarly journals Distant Supervision for Mental Health Management in Social Media: Suicide Risk Classification System Development Study

10.2196/26119 ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. e26119
Author(s):  
Guanghui Fu ◽  
Changwei Song ◽  
Jianqiang Li ◽  
Yue Ma ◽  
Pan Chen ◽  
...  
Keyword(s):  
Social Media ◽  
Suicide Risk ◽  
Health Management ◽  
System Development ◽  
Common People ◽  
Web Based ◽  
Social Media Data ◽  
Distant Supervision ◽  
Data Source ◽  
Media Data

Background Web-based social media provides common people with a platform to express their emotions conveniently and anonymously. There have been nearly 2 million messages in a particular Chinese social media data source, and several thousands more are generated each day. Therefore, it has become impossible to analyze these messages manually. However, these messages have been identified as an important data source for the prevention of suicide related to depression disorder. Objective We proposed in this paper a distant supervision approach to developing a system that can automatically identify textual comments that are indicative of a high suicide risk. Methods To avoid expensive manual data annotations, we used a knowledge graph method to produce approximate annotations for distant supervision, which provided a basis for a deep learning architecture that was built and refined by interactions with psychology experts. There were three annotation levels, as follows: free annotations (zero cost), easy annotations (by psychology students), and hard annotations (by psychology experts). Results Our system was evaluated accordingly and showed that its performance at each level was promising. By combining our system with several important psychology features from user blogs, we obtained a precision of 80.75%, a recall of 75.41%, and an F1 score of 77.98% for the hardest test data. Conclusions In this paper, we proposed a distant supervision approach to develop an automatic system that can classify high and low suicide risk based on social media comments. The model can therefore provide volunteers with early warnings to prevent social media users from committing suicide.

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