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The focus of this chapter is the intersection of social media, publication, data sharing, and research ethics. By now there is an extensive literature on the use of social media in research. There is also excellent work on challenges of postpublication sharing of social media, primarily focused on legal restrictions, technical infrastructure, and documentation. This chapter attempts to build upon and extend this work by using cases to deepen the analysis of ethical issues arising from publishing and sharing social media data. Publishing will refer to the presentation of data extracts, aggregations, or summaries, while sharing refers to the practice of making the underlying data available postpublication for others to use. It will look at the ethical questions that arise both for researchers (or others) sharing data, and those who are using data that has been made available by others, emphasizing the inherently relational nature of data sharing. The ethical challenges researchers face when considering sharing user-generated content collected from social media platforms are the focus of the cases. The chapter begins by summarizing the general principles of research ethics, then identifies the specific ethical challenges from sharing social media data and positions these challenges in the context of these general principles. These challenges are then analyzed in more detail with cases from research projects that drew upon several different genres of social media. The chapter concludes with some recommendations for practical guidance and considers the future of ethical practice in sharing social media data.

The purpose of this paper is to introduce a new viewpoint series, Monitoring the Media: Spotlight on Social Media Research, by providing an overview of the key challenges in social media research and some current initiatives in addressing them.

The paper considers publication output from disciplines dealing with social media studies and summarises the key challenges as discussed in the broader research community.

The paper suggests that challenges originate both from the interdisciplinary nature of social media research and from the ever-changing research landscape. It concludes that, whilst the community is addressing some challenges, others require more attention.

The paper summarises key challenges of social media and will be of interest to researchers in different disciplines, as well as a general audience, wanting to learn about how social media data are used for research.

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 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.

This 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.

The 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.

This 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.

This study aims to use social media data mining to revitalize and support existing urban infrastructure monitoring strategies by extracting valuable insights from public opinion, as current strategies struggle with issues such as adaptability to changing conditions, public engagement and cost effectiveness.

Twitter messages or “Tweets” about public infrastructure in the Philippines were gathered and analyzed to discover reoccurring concerns in public infrastructure, emerging topics in public debates and the people’s general view of infrastructure services.

This study proposes a topic model for extracting dominating subjects from aggregated social media data, as well as a sentiment analysis model for determining public opinion sentiment toward various urban infrastructure components.

The findings of this study highlight the potential of social media data mining to go beyond the limitations of traditional data collection techniques, as well as the importance of public opinion as a key driver for more user-involved infrastructure management and as an important social aspect that can be used to support planning and response strategies in routine maintenance, preservation and improvement of urban infrastructure systems.

This study aims to identify the trending topics, emerging themes and future research directions in supply chain management (SCM) through multiple source of data. The insights would be of use to academics, practitioners and policymakers to leverage latest developments in addressing current and future challenges.

This study uses a multiple source of data such as published literature and social media data including supply chain blogs and forums contents on business-to-business (B2B) firms to identify trending topics, emerging themes and future research directions in SCM. Topic modeling, a machine learning technique, is used to derive the topics and themes. Examining supply chain blogs and forums offer a valuable perspective on current issues and challenges faced by B2B firms. By analyzing the content of these online discussions, the study identifies emerging themes and topics of interest to practitioners and researchers.

The study synthesizes 1,648 published articles and more than 1.3 lakh tweets, discussions and expert views from social media, including various blogs and supply chain forums, and identifies six themes, of which three are trending, and the other three are emerging themes in the supply chain. Rather than aggregate implications, the study integrates findings from two databases and proposes a framework encompassing the drivers, processes and impacts on each theme and derives promising avenues for future research.

Prior literature has majorly used published research articles and reports as a primary source of information to identify the trending theme and emerging topics. To the best of the authors’ knowledge, this is the first study of its kind to examine the potential value of information from social media, such as blogs, websites, forums and published literature to discover new supply chain trends and themes related to B2B firms and derive encouraging possibilities for future research.

This study aims to analyze the available literature on the use of digital marketing in a business-to-business (B2B) context. It identifies gaps in the current research knowledge and proposes a research agenda for scholars and practitioners.

A systematic literature review has been conducted on B2B digital marketing. The various themes have been identified on the basis of the comprehensive analysis of extant literature. Also, semi-structured interviews with B2B marketing experts were also conducted to further refine the emerged digital marketing themes.

Although some B2B firms use digital marketing, most are unable to leverage its full benefits because of the dearth of comprehensive research on the subject. This review provides an insight into the emerging themes by developing a collaborative conceptual framework. The review highlights that few areas such as digital marketing communication and sales management have witnessed steady development while decision support systems, critical success factors, electronic marketing orientation (EMO), etc., were lesser explored. Furthermore, it identifies research gaps and highlights the emerging research themes for future researchers.

The collaborative framework will help organizations to align their digital marketing activities as per the changing market dynamics such as the focus on building social media capability, EMO and value co-creation.

Research on the use of digital marketing by B2B firms is still at the embryonic stage. This study is a pioneering effort to review the use of digital marketing in B2B organizations and identify research priorities for scholars and practitioners.

This study aims to understand the impact of underlying theme in the communication on social media on funding received by early-stage start-ups.

The study is based on empirical testing of data of 849 start-ups and more than 130K tweets. Machine learning (ML) model has been used for text classification of 130K+ tweets. Causal mediation analysis with bootstrapping is carried out for hypothesis testing.

Tweets addressing quality-related uncertainty are a predictor of amount of funds raised. Audience response acts as a mediator between tweets focusing on relational orientation and amount of funds raised.

The authors advance signaling theory by theorizing and investigating the importance of signal content. Endogenous signal of quality directly influences the start-ups outcomes, while exogenous signal helps disseminate information and influence the success.

Entrepreneurs should put in concerted effort to reduce uncertainty about the start-ups. Value creation is a central concept for start-ups; however, communicating value should be the dominant part of social media strategy.

Computer-based language processing techniques have amplified the research focused on content. To the best of the authors’ knowledge, this is the first comprehensive study that explores underlying themes of communication of start-ups and their impact on acquiring funds.