scholarly journals How social media reshapes action on distant customers: some empirical evidence

2017 ◽  
Vol 30 (4) ◽  
pp. 777-794 ◽  
Author(s):  
Deborah Agostino ◽  
Yulia Sidorova
Keyword(s):  
Social Media ◽  
Joint Control ◽  
Control Activity ◽  
Social Media Data ◽  
Content Type ◽  
Corporate Website ◽  
Social Media Platforms ◽  
The One ◽  
Centres Of Calculation ◽  
Media Data

Purpose The purpose of this paper is to investigate how centres of calculation, now emerging in connection with social media, impact on the process of acting on distant customers. Specifically, the authors are interested in exploring how the distance between the organization and its customer is affected and how knowledge is accumulated within this centre. Design/methodology/approach A case study in an Italian telecommunication company was conducted over a time horizon of two years, analysing data sources in the form of interviews, documents and reports, corporate website, social media platforms and participants’ observations. With the adoption of social media, the company configured a new centre of calculation, called monitoring room, in the attempt to accumulate knowledge about its customers. The authors unpacked the activity of the centre of calculation discussing its ability to perform action upon a distant periphery and the process of knowledge accumulation inside the centre itself. Findings The results highlight the implication of social media for “action at a distance”. On the one hand, social media blurs the distinction between the centre and a periphery giving rise to a de-centring, and stimulating a joint control activity between the customer and the organization. On the other hand, social media was found vulnerable in providing a unique knowledge about customers: accumulation cycles that exploit social media data can be replicated by users with skills in data analytics and the knowledge they provide might conflict with knowledge provided by traditional data. Originality/value The authors contribute to an emergent stream of literature that is investigating accounting implications derived from social media, by underlying the controversial effects connected with centres of calculation enacted by social media data. The authors suggest that, while social media data provide the organization with huge amount of information real time, at the same time, it contributes to de-centring allowing customers and external actors to act upon the organization, rather than improving knowledge inside the centre.

Analyzing Social Media Research: A Data Quality and Research Reproducibility Perspective

2021 ◽  
pp. 227797522110118
Author(s):  
Amit K. Srivastava ◽  
Rajhans Mishra
Keyword(s):  
Social Media ◽  
Data Quality ◽  
Quality Of Data ◽  
Social Media Data ◽  
National Crisis ◽  
Social Media Platforms ◽  
Quality Issues ◽  
The One ◽  
Media Data

Social media platforms have become very popular these days among individuals and organizations. On the one hand, organizations use social media as a potential tool to create awareness of their products among consumers, and on the other hand, social media data is useful to predict the national crisis, election polls, stock prediction, etc. However, nowadays, a debate is going on about the quality of data generated on social media platforms, whether it is relevant for prediction and generalization. The article discusses the relevance and quality of data obtained from social media in the context of research and development. Social media data quality issues may impact the generalizability and reproducibility of the results of the study. The paper explores possible reasons for quality issues in the data generated over social media platforms along with the suggestive measures to minimize them using the proposed social media data quality framework.

Harnessing social media to understand tourist mobility: the role of information technology and big data

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jinyan Chen ◽  
Susanne Becken ◽  
Bela Stantic
Keyword(s):  
Social Media ◽  
Literature Review ◽  
Current Trend ◽  
Social Media Data ◽  
Content Type ◽  
Use Of Data ◽  
Social Media Platforms ◽  
The City ◽  
Media Data

Purpose This paper aims to examine key parameters of scholarly context and geographic focus and provide an assessment of theoretical underpinnings of studies in the field of social media and visitor mobility. This review also summarised the characteristics of social media data, including how data are collected from different social media platforms and their advantages and limitations. The stocktake of research in this field was completed by examining technologies and applied methods that supported different research questions. Design/methodology/approach This literature review applied a mix of methods to conduct a literature review. This review analysed 82 journal articles on using social media to track visitors’ movements between 2014 and November 2020. The literature compared the different social media, discussed current applied theories, available technologies, analysed the current trend and provided advice for future directions. Findings This review provides a state-of-the-art assessment of the research to date on tourist mobility analysed using social media data. The diversity of scales (with a dominant focus on the city-scale), platforms and methods highlight that this field is emerging, but it also reflects the complexity of the tourism phenomenon. This review identified a lack of theory in this field, and it points to ongoing challenges in ensuring appropriate use of data (e.g. differentiating travellers from residents) and the ethics surrounding them. Originality/value The findings guide researchers, especially those with no computer science background, on the different types of approaches, data sources and methods available for tracking tourist mobility by harnessing social media. Depending on the particular research interest, different tools for processing and visualization are available.

A framework for understanding citizens’ political participation in social media

2019 ◽  
Vol 33 (4) ◽  
pp. 1053-1075
Author(s):  
Vidushi Pandey ◽  
Sumeet Gupta ◽  
Manojit Chattopadhyay
Keyword(s):  
Social Media ◽  
Political Participation ◽  
Text Classification ◽  
Consumer Participation ◽  
Classification Algorithms ◽  
Social Media Data ◽  
Content Type ◽  
Social Media Platforms ◽  
Use Of Social Media ◽  
Media Data

Purpose The purpose of this paper is to explore how the use of social media by citizens has impacted the traditional conceptualization and operationalization of political participation in the society. Design/methodology/approach This study is based on Teorell et al.’s (2007) classification of political participation which is modified to suit the current context of social media. The authors classified 15,460 tweets along three parameters suggested in the framework with help of supervised text classification algorithms. Findings The analysis reveals that Activism is the most prominent form of political participation undertaken by people on Twitter. Other activities that were undertaken include Formal Political participation and Consumer participation. The analysis also reveals that identity of participant does not play a classifying role as expected from the theoretical framework. It was found that the social media as a platform facilitates new forms of participation which are not feasible offline. Research limitations/implications The current work considers only the microblogging platform of Twitter as the data source. For a more comprehensive insight, analysis of other social media platforms is also required. Originality/value To the best of the authors’ knowledge, this is one of the few analyses where such a large database covering multiple social media events has been created and analysed using supervised text classification algorithms. A large proportion of previous studies on social media have been based on case study and have limited analysis to only a particular event on social media. Although there exist a few works that have studied a vast and varied collection of social media data (Gaby and Caren, 2012; Shirazi, 2013; Rane and Salem, 2012), such efforts are few in number. This study aims to add to that stream of work where a wider and more generalized set of social media data is studied.

scholarly journals Measuring Americans’ Comfort With Research Uses of Their Social Media Data

2021 ◽  
Vol 7 (3) ◽  
pp. 205630512110338
Author(s):  
Sarah Gilbert ◽  
Jessica Vitak ◽  
Katie Shilton
Keyword(s):  
Social Media ◽  
Data Collection ◽  
Data Use ◽  
Data Types ◽  
Sensitive Data ◽  
Social Media Data ◽  
Content Type ◽  
Social Media Platforms ◽  
Diverse Data ◽  
Media Data

Research using online datasets from social media platforms continues to grow in prominence, but recent research suggests that platform users are sometimes uncomfortable with the ways their posts and content are used in research studies. While previous research has suggested that a variety of contextual variables may influence this discomfort, such factors have yet to be isolated and compared. In this article, we present results from a factorial vignette survey of American Facebook users. Findings reveal that researcher domain, content type, purpose of data use, and awareness of data collection all impact respondents’ comfort—measured via judgments of acceptability and concern—with diverse data uses. We provide guidance to researchers and ethics review boards about the ways that user reactions to research uses of their data can serve as a cue for identifying sensitive data types and uses.

scholarly journals Embed2Detect: temporally clustered embedded words for event detection in social media

2021 ◽  
Author(s):  
Hansi Hettiarachchi ◽  
Mariam Adedoyin-Olowe ◽  
Jagdev Bhogal ◽  
Mohamed Medhat Gaber
Keyword(s):  
Social Media ◽  
Event Detection ◽  
High Volume ◽  
Detection Methods ◽  
Word Embeddings ◽  
Agglomerative Clustering ◽  
Data Set ◽  
Social Media Data ◽  
Social Media Platforms ◽  
Media Data

AbstractSocial media is becoming a primary medium to discuss what is happening around the world. Therefore, the data generated by social media platforms contain rich information which describes the ongoing events. Further, the timeliness associated with these data is capable of facilitating immediate insights. However, considering the dynamic nature and high volume of data production in social media data streams, it is impractical to filter the events manually and therefore, automated event detection mechanisms are invaluable to the community. Apart from a few notable exceptions, most previous research on automated event detection have focused only on statistical and syntactical features in data and lacked the involvement of underlying semantics which are important for effective information retrieval from text since they represent the connections between words and their meanings. In this paper, we propose a novel method termed Embed2Detect for event detection in social media by combining the characteristics in word embeddings and hierarchical agglomerative clustering. The adoption of word embeddings gives Embed2Detect the capability to incorporate powerful semantical features into event detection and overcome a major limitation inherent in previous approaches. We experimented our method on two recent real social media data sets which represent the sports and political domain and also compared the results to several state-of-the-art methods. The obtained results show that Embed2Detect is capable of effective and efficient event detection and it outperforms the recent event detection methods. For the sports data set, Embed2Detect achieved 27% higher F-measure than the best-performed baseline and for the political data set, it was an increase of 29%.

Google Plus as a Contentious Field of Revolutionary Identity

2021 ◽  
Vol 20 (3) ◽  
pp. 402-416
Author(s):  
Amirhossein Teimouri
Keyword(s):  
Social Media ◽  
Iranian Revolution ◽  
Social Media Data ◽  
Social Media Platforms ◽  
New Generation ◽  
Media Data

Abstract Social media platforms have been increasingly reinvigorating extreme movements, especially rightist movements. Utilizing unique Google Plus data, the author shows the rise and fall of the 2015 rightist anti-Nuclear Deal movement in Iran. He argues that the Google Plus platform in 2015 provided the new generation of revolutionary Islamist rightist activists with a contentious space of mobilization, enabling them to develop a new revolutionary rightist identity. This revolutionary identity and its corresponding language and discourse did not fully unfold in Iranian mainstream rightist media, even though rightist groups, compared to liberal groups, are not censored and repressed. The new generation of rightist activists perceived the Nuclear Deal as an existential threat to revolutionary principles of the country, and thus played out their outrage and identity anxieties on Google Plus. The author contends that this online outrage, due to the activists’ identity bond with the regime and the 1979 Iranian Revolution, however, did not translate into any massive offline mobilization against the Nuclear Deal. He also discusses the methodological implications of using social media data, especially the discontinuation of Google Plus.

scholarly journals Using Social Media Data to Plan for Tourism

2020 ◽  
Vol 39 (3) ◽  
pp. 125-138
Author(s):  
Alina Zajadacz ◽  
Aleksandra Minkwitz
Keyword(s):  
Social Media ◽  
Local Level ◽  
Data Sources ◽  
Communication Tool ◽  
Tourism Planning ◽  
Communication Tools ◽  
Social Media Data ◽  
Planning Methods ◽  
The One ◽  
Media Data

AbstractThe purpose of the article is to present the concept of using social media (SM) as data sources and communication tools, useful at the various stages of planning, implementing and monitoring the effects of tourism development on a local level. The first part discusses the stages of planning, then presents the characteristics of SM, along with a discussion of the issues presented in the literature to this date. The next part presents data sources and methods of research on SM and functions that they can perform in tourism. The concept presented, on the one hand, reviews the perspectives of practical use of SM as a communication tool and source of data and, on the other hand, the challenges related to the need to further deepen research on tourism planning methods that are adequate to the continuously changing environment.

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.

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