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The present research aimed to identify the motivations, needs, wants, preferences and limitations of corporate professionals with regard to business social analytics.

Online interviews were conducted with 26 professionals the majority of whom work at the management level at 20 reputable corporations in Turkey. Both qualitative and quantitative data was collected during these interviews, which lasted an average of one hour.

The findings shed light on the motivations of corporate professionals for monitoring social media and other digital media, their perceived capability and limitations in doing so, the media that they monitor and wanted to monitor if possible, their criteria and processes for working with service providers in the field of business social analytics, their needs which are not fully met by service providers, their suggestions on service improvement and their reflections on how internal and external customer data can be analyzed with an integrated approach.

This research is an attempt to bridge the gap between the priorities of engineers who generate artificial intelligence for the purposes of social listening and analytics and the end users, e.g. corporate communication professionals. Only by doing so, this field, which is getting more and more important as people spend more time online, will reach its full potential and benefit corporations by providing fruitful insight upon which strategic steps can be taken.

While social media data are used increasingly in studies of social movements, social media evolves far more rapidly than academic research and publication. This chapter argues that researchers should adopt historical and archival approaches to social media data. Treating social media data as an “instant archive” – one that is self-curated, is co-constituted, and changes rapidly – we caution researchers to pay attention to the features of this archive and their implications for working with the data therein. Applying insights from recent discussions of archival methods for social science research to the specific features of social media data, we explore how platform features, repressive effects, and user innovations affect the content of the instant archive. We then offer strategies for researchers' methodological approaches, including how best to select units of analysis and platforms, how to collect and interpret archival materials, and how to identify silences in the data.

This study aimed to examine the mental health information entities and associations between the biomedical, psychological and social domains of bipolar disorder (BD) by analyzing social media data and scientific literature.

Reddit posts and full-text papers from PubMed Central (PMC) were collected. The text analysis was used to create a psychological dictionary. The text mining tools were applied to extract BD entities and their relationships in the datasets using a dictionary- and rule-based approach. Lastly, social network analysis and visualization were employed to view the associations.

Mental health information on the drug side effects entity was detected frequently in both datasets. In the affective category, the most frequent entities were “depressed” and “severe” in the social media and PMC data, respectively. The social and personal concerns entities that related to friends, family, self-attitude and economy were found repeatedly in the Reddit data. The relationships between the biomedical and psychological processes, “afraid” and “Lithium” and “schizophrenia” and “suicidal,” were identified often in the social media and PMC data, respectively.

Mental health information has been increasingly sought-after, and BD is a mental illness with complicated factors in the clinical picture. This paper has made an original contribution to comprehending the biological, psychological and social factors of BD. Importantly, these results have highlighted the benefit of mental health informatics that can be analyzed in the laboratory and social media domains.

The purpose of this study was to analyze the effects of social media analytics on firm performance using a sample of small and medium enterprises (SMEs) in Thailand. This study also investigated whether entrepreneurial orientation (EO) moderated the effects of social media analytics on firm performance.

This study used SMEs listed in the Department of Business Development of Thailand as the sampling frame. Probability sampling was used to draw the sample. A questionnaire survey was used to collect data from 334 firms. The data were analyzed using partial least squares structural equation modeling.

The results supported the positive association between social media analytics practices on firm performance. Moreover, this study found that EO moderated this association significantly. In particular, the positive association between social media analytics practices on firm performance was higher for firms that exhibit a high EO than those that exhibit a low EO. This result indicated that firms that implement social media analytics practices achieved higher performance when they exhibited a high EO.

Social media data analytics should be implemented to strengthen the technological competence of firms. Moreover, firms should integrate EO practices into their implementation of social media analytics to increase their ability to generate substantial improvements in their strategic implementation, thereby enabling them to gain sustainable competitiveness in their market.

Because SMEs are the driving force for economic growth and development in Thailand, their ability to achieve higher performance when they effectively integrate EO practices into their implementation of social media data analytics could be beneficial for the sustainable development of Thailand, especially in the current data-driven era.

The result that EO moderates the effect in enhancing social media analytics practices’ influence on firm performance provides new knowledge that extends the boundary of research on this topic. The authors provided a theoretical explanation to clarify the way the implementation of social media analytics practices should be integrated with EO to increase the level of performance that firms achieve from such practices.

This study aims to explore how relationship trust and different types of influence strategy (i.e., non-coercive and coercive influence strategy) impact green customer integration (GCI), while investigating the moderating mechanisms of big data development and social capital.

Following hierarchical linear regression analysis, the authors examine hypothesized relationships by combining survey data from 206 Chinese manufacturers with secondary data.

The results show that relationship trust positively affects non-coercive influence strategy, while its impact on coercive influence strategy is insignificant. Non-coercive influence strategy has an inverted U-shaped impact on GCI. Furthermore, big data development flattens the inverted U-shaped relationship between non-coercive influence strategy and GCI. Conversely, social capital steepens the inverted U-shaped relationship between non-coercive influence strategy and GCI.

This study sheds light on managers on how to involve customers in GCI through friendly strategies that favor the involvement of customers and the willingness to develop environmentally friendly initiatives.

Although GCI has received widespread attention, how it can be enhanced remains unclear. These findings provide novel insights into the emerging GCI literature and complement social exchange theory.

This study aims to elucidate the dynamics of social media discourse during global health events, specifically investigating how users across different platforms perceive, react to and engage with information concerning such crises.

A mixed-method approach was employed, combining both quantitative and qualitative data collection. Initially, thematic analysis was applied to a data set of social media posts across four major platforms over a 12-month period. This was followed by sentiment analysis to discern the predominant emotions embedded within these communications. Statistical tools were used to validate findings, ensuring robustness in the results.

The results showcased discernible thematic and emotional disparities across platforms. While some platforms leaned toward factual information dissemination, others were rife with user sentiments, anecdotes and personal experiences. Overall, a global sense of concern was evident, but the ways in which this concern manifested varied significantly between platforms.

The primary limitation is the potential non-representativeness of the sample, as only four major social media platforms were considered. Future studies might expand the scope to include emerging platforms or non-English language platforms. Additionally, the rapidly evolving nature of social media discourse implies that findings might be time-bound, necessitating periodic follow-up studies.

Understanding the nature of discourse on various platforms can guide health organizations, policymakers and communicators in tailoring their messages. Recognizing where factual information is required, versus where sentiment and personal stories resonate, can enhance the efficacy of public health communication strategies.

The study underscores the societal reliance on social media for information during crises. Recognizing the different ways in which communities engage with, and are influenced by, platform-specific discourse can help in fostering a more informed and empathetic society, better equipped to handle global challenges.

This research is among the first to offer a comprehensive, cross-platform analysis of social media discourse during a global health event. By comparing user engagement across platforms, it provides unique insights into the multifaceted nature of public sentiment and information dissemination during crises.

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.

Social movement scholars use protest events as a way to quantify social movements and have most often used large, national newspapers to identify those events. This has introduced known and unknown biases into our measurement of social movements. We know that national newspapers tend to cover larger and more contentious events and organizations. Protest events are furthermore a small part of what social movements actually do. Without other readily available options to quantify social movements, however, big-N studies have continued to focus on protest events via a few large newspapers. With advances in digitized data and computational methods, we now no longer have to rely on large newspapers or focus only on protests to quantify important aspects of social movements. In this paper, we use the environmental movement as a case study, analyzing data from a wide range of local, regional, and national newspapers in the United States to quantify multiple facets of social movements. We argue that the incorporation of more data and new methods to quantify information in text has the potential to transform the way we both conceive of and measure social movements in three ways: (1) the type of focal social movement organization included, (2) the type of tactics and issues covered, and (3) the ability to go beyond protest events as the primary unit of analysis. In addition to demonstrating ways that the focus on counting protest events has introduced specific biases in the type of tactics, issues, and organizations covered in social movement research, we argue that computational methods can help us extract and count meaningful aspects of social movements well beyond event counts. In short, the infusion of new data and methods into social movements, peace, and conflict studies could lead us to a substantial shift in the way we quantify social movements, from protest events to everything that occurs outside of them.

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.

Social media data from financial websites contain information related to enterprise credit risk. Mining valuable new features in social media data helps to improve prediction performance. This paper proposes a credit risk prediction framework that integrates social media information to improve listed enterprise credit risk prediction in the supply chain.

The prediction framework includes four stages. First, social media information is obtained through web crawler technology. Second, text sentiment in social media information is mined through natural language processing. Third, text sentiment features are constructed. Finally, the new features are integrated with traditional features as input for models for credit risk prediction. This paper takes Chinese pharmaceutical enterprises as an example to test the prediction framework and obtain relevant management enlightenment.

The prediction framework can improve enterprise credit risk prediction performance. The prediction performance of text sentiment features in social media data is better than that of most traditional features. The time-weighted text sentiment feature has the best prediction performance in mining social media information.

The prediction framework is helpful for the credit decision-making of credit departments and the policy regulation of regulatory departments and is conducive to the sustainable development of enterprises.

The prediction framework can effectively mine social media information and obtain an excellent prediction effect of listed enterprise credit risk in the supply chain.