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Food companies and consumers are increasingly interested in healthy food and beverages. Coffee is one of the most commonly consumed beverages worldwide. There is increasing consensus that coffee consumption can have beneficial effects on human body. This paper aims at exploring Twitter messages' content and sentiment towards health attributes of coffee.

The research adopted a utilitarian and hedonic consumer behaviour perspective to analyse online community messages. A sample of 13,000 tweets, from around 4,800 users, that mentions keywords coffee and health was collected on a daily basis for a month in mid-2017. The tweets were categorized with a term frequency analysis, keyword-in-context analysis and sentiment analysis.

Results showed that the majority of tweets are neutral or slightly positive towards coffee’s effects on health. Media and consumers are dynamic Twitter users. Findings support that coffee consumption brings favourable emotions, wellness, energy, positive state of mind and an enjoyable and trendy lifestyle. Many tweets have a positive perception of coffee health benefits, especially relating to mental and physical well-being.

The high number of users and tweets analysed compensates the limited amount of time of data collection, Twitter messages' restricted number of characters and quantitative software analysis limitations.

The research provides valuable suggestions for food and beverage industry managers.

This work adds value to the literature by expanding scholars' research on food product attributes perception analysis by using social media as a source of information. Moreover, it provides valuable information on marketable coffee attributes.

Social networks have changed the communication patterns significantly. Information available from different social networking sites can be well utilized for the analysis of users opinion. Hence, the organizations would benefit through the development of a platform, which can analyze public sentiments in the social media about their products and services to provide a value addition in their business process. Over the last few years, deep learning is very popular in the areas of image classification, speech recognition, etc. However, research on the use of deep learning method in sentiment analysis is limited. It has been observed that in some cases the existing machine learning methods for sentiment analysis fail to extract some implicit aspects and might not be very useful. Therefore, we propose a deep learning approach for aspect extraction from text and analysis of users sentiment corresponding to the aspect. A seven layer deep convolutional neural network (CNN) is used to tag each aspect in the opinionated sentences. We have combined deep learning approach with a set of rule-based approach to improve the performance of aspect extraction method as well as sentiment scoring method. We have also tried to improve the existing rule-based approach of aspect extraction by aspect categorization with a predefined set of aspect categories using clustering method and compared our proposed method with some of the state-of-the-art methods. It has been observed that the overall accuracy of our proposed method is 0.87 while that of the other state-of-the-art methods like modified rule-based method and CNN are 0.75 and 0.80 respectively. The overall accuracy of our proposed method shows an increment of 7–12% from that of the state-of-the-art methods.

This paper aims to expand the emerging literature on COVID-19 and the financial markets by searching for a relationship between the uncertainty of the first phase of the COVID-19 pandemic experienced through social media and the extreme volatility of the Italian stock market.

The authors analyze the relationship between social media and stock market trends during the first phase of the COVID-19 pandemic through the lens of social theory and Baudrillard's simulacra and hyperreality theory. The authors conducted the data analysis in two phases: the emotional and Granger correlation analysis by using the KPI6 software to analyze 3,275,588 tweets for the predominant emotion on each day and observe its relationship with the stock market.

The research results show a significant Granger causality relation between tweets on a particular day and the closing price of the FTSE MIB during the first phase of the COVID-19 epidemic. The results highlight a strong relationship between social media hyperreality and the real world. The study confirms the role of social media in predicting stock market volatility.

The findings have theoretical and practical implications as they reveal the relevance of social media in our society and its relationship with businesses and economies. In an emergency, social media, as an expression of users' feelings and emotions, can generate a state of hyperreality that is strong correlated with reality. Since social media allows users to publish and share messages without any filter and mediation, the hyperreality generated is affected by highly subjective elements.

This research is different from the previous ones on the same topic because unlike previous studies, conducted under normal or simulated scenarios, this study is focused on the first phase of an unpredictable and unforeseen emergency event: the COVID-19 pandemic. This research adopts a multidisciplinary approach and integrates previous studies on the economic and financial effects generated by social media by applying well-known theories to a new and unexplored context. The study reveals the significant impact generated by social media on stock markets during a global pandemic.

This study aims to promote the need for advanced skills acquisition within the LIS and academic libraries. This study focuses on the importance of library management systems and the need for the graduates to be equipped with analytics skills. Combined with basic data, text mining and analytics, knowledge classification and information audit skills would benefit libraries and improve resource allocation. Agile institutional libraries in this big data era success hinge on the ability to perform depth analytics of both data and text to generate useful insight for information literacy training and information governance.

This paper adopted a living-lab methodology to use existing technology to conduct system analysis and LMS audit of an academic library of one of the highly ranked universities in the world. One of the benefits of this approach is the ability to apply technological innovation and tools to carry out research that is relevant to the context of LIS or other research fields such as management, education, humanities and social sciences. The techniques allow us to gain access to publicly available information because of system audits that were performed. The level of responsiveness of the online library was accessed, and basic information audits were conducted.

This study indicated skill gaps in the LIS training and the academic libraries in response to the fourth industrial technologies. This study argued that the role of skill acquisition and how it can foster data-driven library management operations. Hence, data mining, text mining and analytics are needed to probe into such massive, big data housed in the various libraries’ repositories. This study, however, indicated that without retraining of librarians or including this analytics programming in the LIS curriculum, the libraries would not be able to reap the benefits these techniques provided.

This paper covered research within the general and academic libraries and the broader LIS fields. The same principle and concept is very important for both public and private libraries with substantial usage and patrons.

This paper indicated that librarianship training must fill the gaps within the LIS training. This can be done by including data mining, data analytics, text mining and processing in the curriculum. This skill will enable the news graduates to have skills to assist the library managers in making informed decisions based on user-generated content (UGC), LMS system audits and information audits. Thus, this paper provided practical insights and suggested solutions for academic libraries to improve the agility of information services.

The academic librarian can improve institutional and LMS management through insights that are generated from the user. This study indicated that libraries' UGC could serve as robust insights into library management.

This paper argued that the librarian expertise transcends information literacy and knowledge classification and debated the interwoven of LMS and data analytics, text mining and analysis as a solution to improve efficient resources and training.

This study aims to explore which of four chosen factors (i.e. privacy concerns, FoMO, self-disclosure and time cost) induce a feeling of strain among Facebook users in terms of social media fatigue (SMF), and if this occurs, whether it further influences such outcomes as discontinuance of usage (DoU) and interaction engagement decrement (IED).

Through an online structured questionnaire, empirical data were gathered to verify the research model, based on the stressor-strain-outcome (SSO) framework. The SEM technique was employed for assessing the hypothesized relationships.

The findings show that privacy concerns and time cost are strong antecedents of SMF and contribute significantly to its occurrence; while FoMO and self-disclosure do not exhibit any significant influence. Moreover, SMF positively and significantly affects DoU and IED.

This study enhances the existing body of knowledge on SMF and it can help: (1) individuals to be aware of risks and adjust their activities in balance with their well-being, and (2) social media (SM) managers to develop unique strategies to address the specific needs of SM users.

This research contributes to the limited literature on SMF by (1) introducing the concept of IED – as a consequence of SMF, and (2) creating measurement scales for IED.

E-tourism is instilling in the tourism industry with the advancement in the technological infrastructure all over the world and fetching tremendous tourists’ attention. The dynamic changes in the technological aspects unveil varied developments in the tourism industry. This paper attempts to reveal the developments in the field of e-tourism by a systematic review of the literature using bibliometric analysis.

In total,146 research articles were retrieved from the Web of Science data during the period of 2004 – 2020, for further analysis using VOSviewer and Biblioshiny package of R Studio.

Useful insights resulted in the form of most cited papers, contribution in e-tourism research by different authors, countries, institutions, journals and so on, co-occurrence analysis and cluster analysis for major trends or themes of e-tourism. This study solicits an elaborated review of e-tourism research and unveils the future directions for the researchers.

This study adds substantial value to the research of e-tourism by analysing the bibliometric data of the last 16 years, that is, from 2004 – 2020, procured from the Scopus by analysing the significant trends developed in the e-tourism research. It also adds value by indicating the emerging areas of e-tourism.

The role of retailers in influencing consumer attitude during a brand scandal is quite complex, as retailers are in direct contact with both marketers and consumers. The purpose of the exploratory research is to propose a theoretical model to capture the influences retailers exercise on consumers during brand scandals.

A qualitative approach has been adopted in the study. The study employs the grounded theory approach on the data collected by conducting in-depth interviews with 25 retailers.

Four contextual conditions and six behavioral antecedents of the retailer's role in the context of the brand scandal were identified. Then, the study finds that companies tend to follow two broad approaches during a brand scandal to address retailers' queries and apprehensions. On these bases, the study proposes a six-pronged typology to better understand retailers' role in shaping consumers' brand perception.

Existing literature has not paid adequate attention to this aspect of retailers' role in influencing consumer choices during brand scandal. To the best of the authors' knowledge, there is no prior research which investigates the role and influence of retailers in shaping consumer attitude during brand scandals. It is important to underline that the current research advocates retailers' significant role during a performance-based brand scandal. Specifically, the authors explored a health-related defective scandal of a well-known food brand. In addition, the study focuses on traditional grocery retailers, which already have special relationships with their consumers. Based on retailer perspectives, the authors' contribution is also updating the discussion of branding theory in case of scandals. The identified variables and constructs may be used for empirical investigation on the role of retailers in shaping consumer attitudes toward the scandalized brand.

This chapter examines the structure and sentiment of the Twitter response to Nathan Broad's naming as the originator of an image-based sexual abuse incident following the 2017 Australian Football League Grand Final. Employing Social Network Analysis to visualize the hierarchy of Twitter users responding to the incident and Applied Thematic Analysis to trace the diffusion of differing streams of sentiment within this hierarchy, we produced a representation of participatory social media engagement in the context of image-based sexual abuse. Following two streams of findings, a model of social media user engagement was established that hierarchized the interplay between institutional and personal Twitter users. In this model, it was observed that the Broad incident generated sympathetic and compassionate discourses among an articulated network of social media users. This sentiment gradually diffused to institutional Twitter users – or Reference accounts – through the process of intermedia agenda-setting, whereby the narrative of terrestrial media accounts was altered by personal Twitter users over time.