Big data analytics meets social media: A systematic review of techniques, open issues, and future directions

Examining gender differences in adolescent exposure to food and beverage marketing through go-along interviews

This study explores how adolescents engage with unhealthy food and beverage marketing in online settings, from a gender perspective. Employing an online ethnography approach and using go-along interviews, we explored the experiences of adolescent boys and girls aged 13-17 as they navigated their online experiences with digital food and beverage marketing. Notable themes emerged, including the identification of predatory actions by food companies, the role of protective factors such as family, and the influence of social media influencers in shaping adolescent dietary preferences. Importantly, this research unearthed gender disparities in the participants' responses. Girls, in particular, exhibited a heightened awareness of the protective role played by their families, emphasized the influence of color in marketing strategies, recognized the significance of gender in marketing, and reported exposure to alcohol advertisements-findings that boys less frequently echoed. The study underscores the importance of adolescence as a critical phase in development, during which food companies target these impressionable individuals, driven by their independence and potential for brand loyalty. Moreover, it highlights the potential avenue of gender-specific marketing, offering valuable insights into the gendered dimensions of adolescents' food marketing experiences. By examining the interplay between digital food marketing and gender, this research addresses a critical gap in the literature, shedding light on how gender influences adolescents' perceptions, responses, and behaviors in the context of food marketing strategies. These findings have the potential to inform adolescents of the marketing techniques that target them and guide policymakers in developing and implementing evidence-based regulations aimed at safeguarding adolescents from exposure to unhealthy food marketing.

Social Listening for Product Design Requirement Analysis and Segmentation: A Graph Analysis Approach with User Comments Mining

This study investigates customers' product design requirements through online comments from social media, and quickly translates these needs into product design specifications. First, the exponential discriminative snowball sampling method was proposed to generate a product-related subnetwork. Second, natural language processing (NLP) was utilized to mine user-generated comments, and a Graph SAmple and aggreGatE method was employed to embed the user's node neighborhood information in the network to jointly define a user's persona. Clustering was used for market and product model segmentation. Finally, a deep learning bidirectional long short-term memory with conditional random fields framework was introduced for opinion mining. A comment frequency-invert group frequency indicator was proposed to quantify all user groups' positive and negative opinions for various specifications of different product functions. A case study of smartphone design analysis is presented with data from a large Chinese online community called Baidu Tieba. Eleven layers of social relationships were snowball sampled, with 14,018 users and 30,803 comments. The proposed method produced a more reasonable user group clustering result than the conventional method. With our approach, user groups' dominating likes and dislikes for specifications could be immediately identified, and the similar and different preferences of product features by different user groups were instantly revealed. Managerial and engineering insights were also discussed.

Methods for Analyzing the Contents of Social Media for Health Care: Scoping Review

BACKGROUND

Given the rapid development of social media, effective extraction and analysis of the contents of social media for health care have attracted widespread attention from health care providers. As far as we know, most of the reviews focus on the application of social media, and there is a lack of reviews that integrate the methods for analyzing social media information for health care.

OBJECTIVE

This scoping review aims to answer the following 4 questions: (1) What types of research have been used to investigate social media for health care, (2) what methods have been used to analyze the existing health information on social media, (3) what indicators should be applied to collect and evaluate the characteristics of methods for analyzing the contents of social media for health care, and (4) what are the current problems and development directions of methods used to analyze the contents of social media for health care?

METHODS

A scoping review following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines was conducted. We searched PubMed, the Web of Science, EMBASE, the Cumulative Index to Nursing and Allied Health Literature, and the Cochrane Library for the period from 2010 to May 2023 for primary studies focusing on social media and health care. Two independent reviewers screened eligible studies against inclusion criteria. A narrative synthesis of the included studies was conducted.

RESULTS

Of 16,161 identified citations, 134 (0.8%) studies were included in this review. These included 67 (50.0%) qualitative designs, 43 (32.1%) quantitative designs, and 24 (17.9%) mixed methods designs. The applied research methods were classified based on the following aspects: (1) manual analysis methods (content analysis methodology, grounded theory, ethnography, classification analysis, thematic analysis, and scoring tables) and computer-aided analysis methods (latent Dirichlet allocation, support vector machine, probabilistic clustering, image analysis, topic modeling, sentiment analysis, and other natural language processing technologies), (2) categories of research contents, and (3) health care areas (health practice, health services, and health education).

CONCLUSIONS

Based on an extensive literature review, we investigated the methods for analyzing the contents of social media for health care to determine the main applications, differences, trends, and existing problems. We also discussed the implications for the future. Traditional content analysis is still the mainstream method for analyzing social media content, and future research may be combined with big data research. With the progress of computers, mobile phones, smartwatches, and other smart devices, social media information sources will become more diversified. Future research can combine new sources, such as pictures, videos, and physiological signals, with online social networking to adapt to the development trend of the internet. More medical information talents need to be trained in the future to better solve the problem of network information analysis. Overall, this scoping review can be useful for a large audience that includes researchers entering the field.

Social Media and the Patient - on Education and Empowerment

Social media has unprecedentedly impacted the world, and this includes patients and physicians alike. This article provides a glimpse of the pros and cons of social media to both parties, and how, despite its pitfalls, rheumatologists can put its use in daily practice to help bridge the gap between, and among, rheumatologists and patients to ultimately improve patient outcomes.

Social media analytical CRM: a case study in a bank

The use of the social media (SM) has become more and more widespread during the last two decades, the companies started looking for insights for how they can improve their businesses using the information accumulating therein. In this regard, it is possible to distinguish between two lines of research: those based on anonymous data and those based on customer specific data. Although obtaining customer specific SM data is a challenging task, analysis of such individual data can result in very useful insights. In this study we take up this path for the customers of a bank, analyze their tweets and develop three kinds of analytical models: clustering, sentiment analysis and product propensity. For the latter one, we also develop a version where, besides the text information, the structural information available in the bank databases are also used in the models. The result of the study is a considerably more efficient set of analytical CRM models.

A Normalized Rich-Club Connectivity-Based Strategy for Keyword Selection in Social Media Analysis

In this paper, we present a study on keyword selection behavior in social media analysis that is focused on particular topics, and propose a new effective strategy that considers the co-occurrence relationships between keywords and uses graph-based techniques. In particular, we used the normalized rich-club connectivity considering the weighted degree, closeness centrality, betweenness centrality and PageRank values to measure a subgroup of highly connected “rich keywords” in a keyword co-occurrence network. Community detection is subsequently applied to identify several keyword combinations that are able to accurately and comprehensively represent the researched topic. The empirical results based on four topics and comparing four existing models confirm the performance of our proposed strategy in promoting the quantity and ensuing the quality of data related to particular topics collected from social media. Overall, our findings are expected to offer useful guidelines on how to select keywords for social media-based studies and thus further increase the reliability and validity of their respective conclusions.

How Advanced Technological Approaches Are Reshaping Sustainable Social Media Crisis Management and Communication: A Systematic Review

The end goal of technological advancement used in crisis response and recovery is to prevent, reduce or mitigate the impact of a crisis, thereby enhancing sustainable recovery. Advanced technological approaches such as social media, machine learning (ML), social network analysis (SNA), and big data are vital to a sustainable crisis management decisions and communication. This study selects 28 articles via a systematic process that focuses on ML, SNA, and related technological tools to understand how these tools are shaping crisis management and decision making. The analysis shows the significance of these tools in advancing sustainable crisis management to support decision making, information management, communication, collaboration and cooperation, location-based services, community resilience, situational awareness, and social position. Moreover, the findings noted that managing diverse outreach information and communication is increasingly essential. In addition, the study indicates why big data and language, cross-platform support, and dataset lacking are emerging concerns for sustainable crisis management. Finally, the study contributes to how advanced technological solutions effectively affect crisis response, communication, decision making, and overall crisis management.

Social Media Big Data Analysis: Towards Enhancing Competitiveness of Firms in a Post-Pandemic World

In this paper, we proposed an advanced business intelligence framework for firms in a post-pandemic phase to increase their performance and productivity. The proposed framework utilizes some of the most significant tools in this era, such as social media and big data analysis for business intelligence systems. In addition, we survey the most outstanding related papers to this study. Open challenges based on this framework are described as well, and a proposed methodology to minimize these challenges is given. Finally, the conclusion and further research points that are worth studying are discussed.

A Novel Method of Generating Geospatial Intelligence from Social Media Posts of Political Leaders

Social media platforms such as Twitter have been used by political leaders, heads of states, political parties, and their supporters to strategically influence public opinions. Leaders can post about a location, a state, a country, or even a region in their social media accounts, and the posts can immediately be viewed and reacted to by millions of their followers. The effect of social media posts by political leaders could be automatically measured by extracting, analyzing, and producing real-time geospatial intelligence for social scientists and researchers. This paper proposed a novel approach in automatically processing real-time social media messages of political leaders with artificial intelligence (AI)-based language detection, translation, sentiment analysis, and named entity recognition (NER). This method automatically generates geospatial and location intelligence on both ESRI ArcGIS Maps and Microsoft Bing Maps. The proposed system was deployed from 1 January 2020 to 6 February 2022 to analyze 1.5 million tweets. During this 25-month period, 95K locations were successfully identified and mapped using data of 271,885 Twitter handles. With an overall 90% precision, recall, and F1score, along with 97% accuracy, the proposed system reports the most accurate system to produce geospatial intelligence directly from live Twitter feeds of political leaders with AI.

The derived demand for advertising expenses and implications on sustainability: a comparative study using deep learning and traditional machine learning methods

In recent years, machine learning models based on big data have been introduced into marketing in order to transform customer data into meaningful insights and to make strategic decisions by making more accurate predictions. Although there is a large amount of literature on demand forecasting, there is a lack of research about how marketing strategies such as advertising and other promotional activities affect demand. Therefore, an accurate demand-forecasting model can make significant academic and practical contributions for business sustainability. The purpose of this article is to evaluate machine learning methods to provide accuracy in forecasting demand based on advertising expenses. The study focuses on a prediction mechanism based on several Machine Learning techniques-Support Vector Regression (SVR), Random Forest Regression (RFR) and Decision Tree Regressor (DTR) and deep learning techniques-Artificial Neural Network (ANN), Long Short Term Memory (LSTM),-to deal with demand forecasting based on advertising expenses. Deep learning is a powerful technique that can solve marketing problems based on both classification and regression algorithms. Accordingly, a television manufacturer's real market dataset consisting of advertising expenditures, sales and demand forecasting via chosen machine learning methods was analyzed and compared in terms of the accuracy of demand forecasting. As a result, Long Short Term Memory has been found to be superior to other models in providing highly accurate prediction results for demand forecasting based on advertising expenses.

Understanding the Emotional Intelligence Discourse on Social Media: Insights from the Analysis of Twitter

Social networks have created an information diffusion corpus that provides users with an environment where they can express their views, form a community, and discuss topics of similar or dissimilar interests. Even though there has been an increasingly rising demand for conducting an emotional analysis of the users on social media platforms, the field of emotional intelligence (EI) has been rather slow in exploiting the enormous potential that social media can play in the research and practice of the framework. This study, thus, tried to examine the role that the microblogging platform Twitter plays in enhancing the understanding of the EI community by building on the Twitter Analytics framework of Natural Language Processing to further develop the insights of EI research and practice. An analysis was conducted on 53,361 tweets extracted using the hashtag emotional intelligence through descriptive analytics (DA), content analytics (CA), and network analytics (NA). The findings indicated that emotional intelligence tweets are used mostly by speakers, psychologists (or other medical professionals), and business organizations, among others. They use it for information dissemination, communication with stakeholders, and hiring. These tweets carry strong positive sentiments and sparse connectedness. The findings present insights into the use of social media for understanding emotional intelligence.

Towards effective offloading mechanisms in fog computing

Fog computing is considered a formidable next-generation complement to cloud computing. Nowadays, in light of the dramatic rise in the number of IoT devices, several problems have been raised in cloud architectures. By introducing fog computing as a mediate layer between the user devices and the cloud, one can extend cloud computing's processing and storage capability. Offloading can be utilized as a mechanism that transfers computations, data, and energy consumption from the resource-limited user devices to resource-rich fog/cloud layers to achieve an optimal experience in the quality of applications and improve the system performance. This paper provides a systematic and comprehensive study to evaluate fog offloading mechanisms' current and recent works. Each selected paper's pros and cons are explored and analyzed to state and address the present potentialities and issues of offloading mechanisms in a fog environment efficiently. We classify offloading mechanisms in a fog system into four groups, including computation-based, energy-based, storage-based, and hybrid approaches. Furthermore, this paper explores offloading metrics, applied algorithms, and evaluation methods related to the chosen offloading mechanisms in fog systems. Additionally, the open challenges and future trends derived from the reviewed studies are discussed.

Fog-based healthcare systems: A systematic review

The healthcare system aims to provide a reliable and organized solution to enhance the health of human society. Studying the history of patients can help physicians to consider patients' needs in healthcare system designing and offering service, which leads to an increase in patient satisfaction. Therefore, healthcare is becoming a growing contesting market. With this significant growth in healthcare systems, such challenges as huge data volume, response time, latency, and security vulnerability are raised. Therefore, fog computing, as a well-known distributed architecture, could help to solve such challenges. In fog computing architecture, processing components are placed between the end devices and cloud components, and they execute applications. This architecture is suitable for such applications as healthcare systems that need a real-time response and low latency. In this paper, a systematic review of available approaches in the field of fog-based healthcare systems is proposed; the challenges of its application in healthcare are explored, classified, and discussed. First, the fog computing approaches in healthcare are categorized into three main classes: communication, application, and resource/service. Then, they are discussed and compared based on their tools, evaluation methods, and evaluation metrics. Finally, based on observations, some open issues and challenges are highlighted for further studies in fog-based healthcare.