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This study aims to ascertain the impact of data collecting awareness on perceived information security concerns and information-sharing behavior on social networking sites.

Based on communication privacy management theory, the study forecasted the relationship between information-sharing behavior and awareness of data collecting purposes, data collection tactics and perceived security risk using structural equation modeling analysis and one-way ANOVA. The sample size of 521 young social media users in Vietnam, ages 18 to 34, was made up of 26.7% men and 73.3% women. When constructing the questionnaire survey method with lone source respondents, the individual’s unique awareness and experiences with using online social networks (OSNs) were taken into account.

The results of the investigation demonstrate a significant relationship between information-sharing and awareness of data collecting, perceptions of information security threats and behavior. Social media users have used OSN privacy settings and paid attention to the sharing restriction because they are concerned about data harvesting.

This study was conducted among young Vietnamese social media users, reflecting specific characteristics prevalent in the Vietnamese environment, and hence may be invalid in other nations’ circumstances.

Social media platform providers should improve user connectivity by implementing transparent privacy policies that allow users to choose how their data are used; have clear privacy statements and specific policies governing the use of social media users’ data that respect users’ consent to use their data; and thoroughly communicate how they collect and use user data while promptly detecting any potential vulnerabilities within their systems.

The authors ascertain that the material presented in this manuscript will not infringe upon any statutory copyright and that the manuscript will not be submitted elsewhere while under Journal of Information, Communication and Ethics in Society review.

Warranty service plays a critical role in sustainability and service continuity and influences customer satisfaction. Considering the role of social networks in customer feedback channels, one of the essential sources to examine the reflection of a product/service is social media mining. This paper aims to identify the frequent product failures through social network mining. Focusing on social media data as a comprehensive and online source to detect warranty issues reveals opportunities for improvement, such as user problems and necessities. This model will detect the causes of defects and prioritize improving components in a product-service system based on FMEA results.

Ontology-based methods, text mining and sentiment analysis with machine learning methods are performed on social media data to investigate product defects, symptoms and the relationship between warranty plans and customer behaviour. Also, the authors have incorporated multi-source data collection to cover all the possibilities. Then the authors promote a decision support system to help the decision-makers using the FMEA process have a more comprehensive insight through customer feedback. Finally, to validate the accuracy and reliability of the results, the authors used the operational data of a LENOVO laptop from a warranty service centre and classifier performance metrics to compare the authors’ results.

This study confirms the validity of social media data in detecting customer sentiments and discovering the most defective components and failures of the products/services. In other words, the informative threads are derived through a data preparation process and then are based on analyzing the different features of a failure (issues, symptoms, causes, components, solutions). Using social media data helps gain more accurate online information due to the limitation of warranty periods. In other words, using social media data broadens the scope of data gathering and lets in all feedback from different sources to recognize improvement opportunities.

This work contributes a DSS model using multi-channel social media mining through supervised machine learning for warranty-service improvement based on defect-related discovery to unravel the potential aspects of social networks analysis to predict the most vulnerable components of a product and the main causes of failures that lead to the inputs for the FMEA process and then, a cost optimization. The authors have used social media channels like Twitter, Facebook, Reddit, LENOVO Forums, GitHub, Quora and XDA-Developers to gather data about the LENOVO laptop failures as a case study.

The purposes of this paper are to (1) explore how personality traits pertaining to the dominance influence steadiness compliance model manifest themselves in terms of user interaction behavior on social media and (2) examine whether social interaction data on social media platforms can predict user personality.

Social interaction data was collected from 198 users of Sina Weibo, a popular social media platform in China. Their personality traits were also measured via questionnaire. Machine learning techniques were applied to predict the personality traits based on the social interaction data.

The results demonstrated that the proposed classifiers had high prediction accuracy, indicating that our approach is reliable and can be used with social interaction data on social media platforms to predict user personality. “Reposting,” “being reposted,” “commenting” and “being commented on” were found to be the key interaction features that reflected Weibo users' personalities, whereas “liking” was not found to be a key feature.

The findings of this study are expected to enrich personality prediction research based on social media data and to provide insights into the potential of employing social media data for the purpose of personality prediction in the context of the Weibo social media platform in China.

The purpose of this paper is to discover similarities and differences in the construction industry in China and the United States by using data analytic tools on data crawled from social media platforms.

The method comprised comprehensive data analytics using network link analysis and natural language processing tools to discover similarities and differences of social networks, topics of interests and sentiments and emotions on different social media platforms.

From the research, it showed that all clusters (construction company, construction worker, construction media and construction union) shared similar trends on follower-following ratios and sentiment analysis in both social media platforms. The biggest difference between the two countries is that public accounts (e.g. company, media and union) on Twitter posted more on public interests, including safety and energy.

The research contributes to knowledge about an alternative method of data collection for both academia and industry practitioners. Statistical bias can be introduced by only using social media platform data. The analyzed four clusters can be further divided to reflect more fine-grained groups of construction industries. The results can be integrated into other analyses based on traditional methodologies of data collection such as questionnaire surveys or interviews.

The research provides a comparative study of the construction industries in China and the USA among four clusters using social media platform data.

The purpose of this paper was to examine how small- and medium-sized hospitality organisations engage with social media and how social media data are used by their managers to inform business decisions.

A qualitative approach was used in this research in which interviews were conducted with top management, comprising the owners/directors and other key managers from small- and medium-sized organisations based in Penang, Malaysia. Fan and Gordon's (2014) categorisation of the social media data analysis process and Simon's (1995) concept of the interactive and diagnostic usage of data were used in the analysis of data.

The managers of small- and medium-sized hospitality organisations engage with social media for customer relationship management and the understanding of key main competitors. Social media is used to understand, build and manage relationships with current and potential customers; these activities are also linked to actions taken to protect a company's reputation. Even though, for the companies concerned, data gathering is still at the capture stage with no formal procedures and processes in place, the data are utilised in an interactive way to inform two areas’ major business decisions-making, i.e. those related to pricing and promotion and the strategic formulation and reorientation of the business.

The respondents of this study were mainly from smaller hospitality organisations. Hence, the insights gained are limited to the context of smaller hotels.

A significant number of social media studies within the hospitality sector have focussed on marketing aspects. This study explored the wider use of social media in the case of smaller hospitality organisations and how they compete and position themselves in the competitive hospitality industry.

The aim of this study was to understand how public relations leaders view and use social media analytics (SMA) and the impact of SMA on the public relations function.

The research involved in-depth interviews with chief communication officers (CCOs) from leading multinational corporate brands.

The findings revealed that although CCOs perceive social media analytics as strategically important to the advancement of public relations, the use of social media data is slowed by challenges associated with building SMA capacity.

The research extends public relations theory on public relations as a strategic management function and provides practical insights for building SMA capabilities.

The study is among the first to provide empirical evidence of how companies are using social media analytics to enhance public relations efforts.

Customer knowledge from social media can become an important organizational asset. The purpose of this paper is to identify useful customer knowledge including knowledge for customer, knowledge about customers and knowledge from customers from social media data and facilitate social media-based customer knowledge management.

The authors conducted a case study to analyze people’s online discussion on Twitter regarding laptop brands and manufacturers. After collecting relevant tweets using Twitter search APIs, the authors applied statistical analysis, text mining and sentiment analysis techniques to analyze the social media data set and visualize relevant insights and patterns in order to identify customer knowledge.

The paper identifies useful insights and knowledge from customers and knowledge about customers from social media data. Furthermore, the paper shows how the authors can use knowledge from customers and knowledge about customers to help companies develop knowledge for customers.

This is an original social media analytics study that discusses how to transform large-scale social media data into useful customer knowledge including knowledge for customer, knowledge about customers and knowledge from customers.

The purpose of this paper is to focus on measuring the contribution generated by social media when used for business purposes, distinguishing between metrics and methods for data collection and data analysis. Organizations worldwide have widely endorsed social media, but available studies on the contribution generated by these technologies for organizations are fragmented. A performance measurement system (PMS) framework to monitor social media is theoretically derived, highlighting the methods for data collection and data analysis and metrics to quantify social media impacts in terms of financials, network structure, interactions, conversations and users’ opinion.

This is a qualitative research based on a literature review of papers in management, information technology, marketing and public relations.

A PMS framework to quantify the contribution of social media is theoretically derived, distinguishing between metrics and methods. PMS metrics support the measurement of the financial and relational impact of social media, as well as the impact of social media conversations and users’ opinions. PMS methods comprise different approaches for data collection and data analysis that range from manual to automated data collection and from content to sentiment analysis techniques.

The PMS framework contributes to the academic literature by integrating a unique model of the available approaches for social media measurement that can serve as a basis for future research directions. The framework also supports practitioners that face necessity to quantify financial and relational contributions of social media as well as the contribution of social media conversation and users’ opinion.

The purpose of this paper is to use the Twitter Search Network of the Apache NodeXL data discovery tool to extract over 5,000 data from Twitter accounts that twitted, re-twitted or commented on the hashtag, #NigeriaDecides, to gain insight into the impact of the social media on the politics and administration of developing countries.

Several algorithms like the Fruchterman-Reingold algorithm, Harel-Koren Fast Multiscale algorithm and the Clauset-Newman-Moore algorithms are used to analyse the social media metrics like betweenness, closeness centralities, etc., and visualize the sociograms.

Results from a typical application of this tool, on the Nigeria general election of 2015, show the social media as the major influencer and the contribution of the social media data analytics in predicting trends that may influence developing economies.

With this type of work, stakeholders can make informed decisions based on predictions that can yield high degree of accuracy as this case. It is also important to stress that this work can be reproduced for any other part of the world, as it is not limited to developing countries or Nigeria in particular or it is limited to the field of politics.

Increasingly, during the 2015 general election, citizens have taken over the blogosphere by writing, commenting and reporting about different issues from politics, society, human rights, disasters, contestants, attacks and other community-related issues. One of such instances is the #NigeriaDecides network on Twitter. The effect of these showed in the opinion polls organized by the various interest groups and media houses which were all in favour of GMB.

The case study the authors took on the Nigeria’s general election of 2015 further strengthens the fact that the developing countries have joined the social media race. The major contributions of this work are that policy makers, politicians, business managers, etc. can use the methods shown in this work to harness and gain insights from Big Data, like the social media data.

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.

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.

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.

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.

利用社交媒体了解游客的流动性：信息技术和大数据的作用

本综述审查了学术背景的关键参数和案例调查的地理焦点, 并评估了社交媒体和访客流动领域研究的理论基础。本文章还总结了社交媒体数据的特征, 包括如何从不同社交媒体平台收集数据及其优势和局限。 此外本论文通过研究不同的应用方法和总结相关技术来完成的。

本研究提供了最新的使用社交媒体数据分析游客流动性研究的评估。比如案例分析的地理大小（主要集中在城市尺度）、社交媒体平台和方法的多样性突出了该领域的新兴, 但复杂旅游流动现象。审查发现利用社交媒体进行的研究缺乏理论贡献, 并指出在确保适当使用数据（例如区分旅行者与居民）和围绕他们的道德规范方面存在持续挑战。

研究结果指导研究人员, 尤其是那些没有计算机科学背景的研究人员, 了解不同类型的方法、数据来源和方法, 可用于通过利用社交媒体来跟踪旅游流动性。根据特定的研究兴趣, 可以使用不同的处理和可视化工具。

关键词：旅游模式; 游客流动; 游客轨迹; 社交媒体; 信息技术; 大数据

文章类型： 文献评论

Uso de las redes sociales para comprender la movilidad turística: el papel de la tecnología de la información y los macrodatos

En este estudio se examinan los parámetros clave en el contexto académico y enfoque geográfico y se evalúan los fundamentos teóricos de estudios en el campo de las redes sociales y la movilidad de los visitantes. Se resumen también las características de datos de las redes sociales, incluidos los métodos de recopilación de datos de las diferentes plataformas de redes sociales así como sus ventajas y limitaciones. Finalmente, se examinan tecnologías y métodos aplicados que respaldan las diferentes cuestiones de la investigación.

El estudio proporciona una evaluación avanzada del conocimiento hasta la fecha sobre la movilidad turística analizada utilizando datos de redes sociales. La diversidad de escalas (con un enfoque dominante en la escala de la ciudad), plataformas y métodos indica que este campo está en auge, pero también refleja la complejidad del fenómeno turístico. En este estudio se identifica una falta de teoría en este campo y se señalan los contúnios desafíos para garantizar el uso apropiado de datos (por ejemplo, diferenciar a los viajeros de los residentes) y la ética que los rodea.

los resultados guían a los investigadores, especialmente a aquellos sin formación en informática, sobre los diferentes tipos de enfoques, fuentes de datos y métodos disponibles para rastrear la movilidad turística mediante el uso de las redes sociales. Existen diferentes herramientas de procesamiento y visualización disponibles dependiendo del interés particular de la investigación.

Patrones de viaje; Movilidad turística; Movimientos de visitantes; Redes sociales; Tecnologías de la información; Macrodatos.