•Explores connections and patterns created by the aggregated interactions in Facebook pages during disaster responses.•Analyzes social media data from the Facebook page of city of Baton Rouge during ...the 2016 Louisiana flood (Aug 12–Dec 1, 2016).•Analyzes social roles and key players using social network analysis.•Study recommends actions to improve the effectiveness of information diffusion via social media.
Social media, such as Twitter and Facebook, plays a critical role in disaster management by propagating emergency information to a disaster-affected community. It ranks as the fourth most popular source for accessing emergency information. Many studies have explored social media data to understand the networks and extract critical information to develop a pre- and post-disaster mitigation plan.
The 2016 flood in Louisiana damaged more than 60,000 homes and was the worst U.S. disaster after Hurricane Sandy in 2012. Parishes in Louisiana actively used their social media to share information with the disaster-affected community − e.g., flood inundation map, locations of emergency shelters, medical services, and debris removal operation. This study applies social network analysis to convert emergency social network data into knowledge. We explore patterns created by the aggregated interactions of online users on Facebook during disaster responses. It provides insights to understand the critical role of social media use for emergency information propagation. The study results show social networks consist of three entities: individuals, emergency agencies, and organizations. The core of a social network consists of numerous individuals. They are actively engaged to share information, communicate with the city of Baton Rouge, and update information. Emergency agencies and organizations are on the periphery of the social network, connecting a community with other communities. The results of this study will help emergency agencies develop their social media operation strategies for a disaster mitigation plan.
The use of online social networks has made significant progress in recent years as the use of the Internet has become widespread worldwide as the technological infrastructure and the use of ...technological products evolve. It has become more suitable to reach online social networking sites such as Facebook, Twitter, Instagram and LinkedIn via the internet and web 3.0 technologies. Thus, people have shared their views on many different topics and their emotions with other users more widely on these platforms. This means that a huge amount of data is created on platforms where millions of people connect with each other through social networks. Nevertheless, the development of computational paradigms at high speed and complexity with technological possibilities allows analysis of valuable data by means of social network analysis methods. Our goal for this paper is to present a review of novel and popular online social network analysis problems with related applications and a reference work for researchers interested in analyzing online social network data and social network problems. Unlike other individual studies we have gathered 21 online social network problems and defined them with related studies. Thus, this study is original by presenting an important source of research by explaining the problems of online social network and the studies performed in this area.
•A review of novel and popular online social network analysis problems has been presented.•Related applications and a reference work for researchers interested in analyzing online social network data and social network problems have been examined.•Unlike other individual studies 21 online social network problems have been gathered and defined with related studies for the first time.
Retrieving cohesive subgraphs in networks is a fundamental problem in social network analysis and graph data management. These subgraphs can be used for marketing strategies or recommendation ...systems. Despite the introduction of numerous models over the years, a systematic comparison of their performance, especially across varied network configurations, remains unexplored. In this study, we evaluated various cohesive subgraph models using task-based evaluations and conducted extensive experimental studies on both synthetic and real-world networks. Thus, we unveil the characteristics of cohesive subgraph models, highlighting their efficiency and applicability. Our findings not only provide a detailed evaluation of current models but also lay the groundwork for future research by shedding light on the balance between the interpretability and cohesion of the subgraphs. This research guides the selection of suitable models for specific analytical needs and applications, providing valuable insights.
Over the past decade, anti-vaccination rhetoric has become part of the mainstream discourse regarding the public health practice of childhood vaccination. These utilise social media to foster online ...spaces that strengthen and popularise anti-vaccination discourses. In this paper, we examine the characteristics of and the discourses present within six popular anti-vaccination Facebook pages. We examine these large-scale datasets using a range of methods, including social network analysis, gender prediction using historical census data, and generative statistical models for topic analysis (Latent Dirichlet allocation). We find that present-day discourses centre around moral outrage and structural oppression by institutional government and the media, suggesting a strong logic of 'conspiracy-style' beliefs and thinking. Furthermore, anti-vaccination pages on Facebook reflect a highly 'feminised' movement ‒ the vast majority of participants are women. Although anti-vaccination networks on Facebook are large and global in scope, the comment activity sub-networks appear to be 'small world'. This suggests that social media may have a role in spreading anti-vaccination ideas and making the movement durable on a global scale.
Food sharing mobile applications are becoming increasingly popular, but little is known about the new social configurations of people using them, particularly those applications that use consumers as ...voluntary intermediaries in supply chains. This article presents a social network analysis of a food sharing mobile application conducted in partnership with OLIO. The study focuses on longitudinal social network data from 54,913 instances of food sharing between 9054 people and was collected over 10 months. The results challenge existing theories of food sharing (reciprocity, kin selection, tolerated scrounging, and costly signalling) as inadequate by showing that donor-recipient reciprocity and balance are rare, but also show that genuinely novel social relations have formed between organisations and consumers which depart from traditional linear supply chains. The findings have significant implications for managers and policymakers aiming to encourage, measure and understand technology-assisted food sharing practices.
•The study presents a longitudinal social network analysis of 54913 instances of food sharing mediated by mobile applications•Direct and indirect reciprocity is rare, but there are nonetheless emergent ‘communities’ and interdependencies•Network members typically perform different roles (either donors or recipients) and regularity of sharing varies dramatically•The platform blurs different forms of supply chain including B2C, C2C and B2V2C (Business to Volunteer to Consumer)
Wearable sensors and social networking platforms play a key role in providing a new method to collect patient data for efficient healthcare monitoring. However, continuous patient monitoring using ...wearable sensors generates a large amount of healthcare data. In addition, the user-generated healthcare data on social networking sites come in large volumes and are unstructured. The existing healthcare monitoring systems are not efficient at extracting valuable information from sensors and social networking data, and they have difficulty analyzing it effectively. On top of that, the traditional machine learning approaches are not enough to process healthcare big data for abnormality prediction. Therefore, a novel healthcare monitoring framework based on the cloud environment and a big data analytics engine is proposed to precisely store and analyze healthcare data, and to improve the classification accuracy. The proposed big data analytics engine is based on data mining techniques, ontologies, and bidirectional long short-term memory (Bi-LSTM). Data mining techniques efficiently preprocess the healthcare data and reduce the dimensionality of the data. The proposed ontologies provide semantic knowledge about entities and aspects, and their relations in the domains of diabetes and blood pressure (BP). Bi-LSTM correctly classifies the healthcare data to predict drug side effects and abnormal conditions in patients. Also, the proposed system classifies the patients’ health condition using their healthcare data related to diabetes, BP, mental health, and drug reviews. This framework is developed employing the Protégé Web Ontology Language tool with Java. The results show that the proposed model precisely handles heterogeneous data and improves the accuracy of health condition classification and drug side effect predictions.
•Smartphones, wearable sensors, and social networks provide a new approach to collect patient data.•Continuous patient monitoring generates a large amount of unstructured healthcare data.•Existing approaches cannot deal with huge amounts of healthcare data extracted from various sources.•Traditional ML techniques are unable to handle extracted healthcare data for abnormality prediction.•A big data analytics engine is proposed to precisely analyze different sources of healthcare data.
Competitive intelligence is vital for enterprises to survive in the market. Recently, online reviews have gained popularity among enterprises and researchers as a means to acquire timely and precise ...competitive insights. However, extant studies overlook the evolution of competitive information because they do not account for the variation of online reviews and products. In this research, we propose a method for dynamic competitive analysis by concentrating on the changes in products and online reviews. First, products and their related online reviews are analyzed via Dynamic Topic Model to derive product features mentioned in different slices. Second, we use sentiment analysis to estimate product performance and transfer the results into a product competitive relation network. Third, we implement competitive analysis from the perspectives of products and markets based on competitiveness propagation. By tracking the evolution of competitive relations among products, we discover competitors and glean more competitive insights. Lastly, a case study of laptops is used for validation. Experimental results indicate that our method is effective in capturing evolving and potential competitive relations among products.
•We proposed a method for dynamic competitive analysis via online reviews.•Our method tracks the evolution of competitiveness using updated reviews.•Our method reveals potential competitive relations and competitors.•Compared to extant studies, our method provides more informative results.
Big data represents a new technology paradigm for data that are generated at high velocity and high volume, and with high variety. Big data is envisioned as a game changer capable of revolutionizing ...the way businesses operate in many industries. This article introduces an integrated view of big data, traces the evolution of big data over the past 20 years, and discusses data analytics essential for processing various structured and unstructured data. This article illustrates the application of data analytics using merchant review data. The impacts of big data on key business performances are then evaluated. Finally, six technical and managerial challenges are discussed.
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