Effectiveness of Social Media Data in Healthcare Communication

Social online marketing is expanding fast with the evolution and recent development in the information and communication technology. Investigating how companies are exploiting social media for marketing, advertisement, and consumer's engagement is gaining more and more interest. In this paper, brands/companies data on Twitter is collected and analyzed to compute the overall company response on Twitter. Responsiveness of a company is inferred from three features: company popularity, average company replies, and average followers' replies. Twitter network features are used in calculating the posting frequency for companies and their followers. It is shown that the proposed approach can be used in finding the responsiveness of companies and their followers. Furthermore, useful links for a brand consumer is extracted and the posting behavior of brands and their followers is determined with the help of Twitter network features, such as retweet count and geolocation. This paper contributes to the literature on how Twitter data and its network structure features can be exploited in finding the responsiveness and posting behavior of companies and their followers. We believe that this approach can be used effectively in developing prediction and information-filtering systems, particularly the personalized-recommendation systems.

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“…Tweets written in English are kept in the database. We selected twitter as our data source due to the following reasons [5], [10], [33].…”

Section: A Data Collection Form Twittermentioning

confidence: 99%

Quantification of Productivity of the Brands on Social Media With Respect to Their Responsiveness

et al. 2019

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“…Although a large amount of information is thought to be reliable for monitoring and analyzing health-related information, the lack of methodological transparency for data extraction, processing, and analysis has led to inaccurate predictions in detecting disease outbreaks, adverse drug events, etc. As a result, health-related text mining and information extraction are active challenges for the development of useful public health applications for researchers [4,5,6]. One essential part of developing such an information extraction system is the NER process, which defines the boundaries between common words in terminology in a particular text, and assigns the terminology to specific categories based on domain knowledge [7,8,9].…”

Section: Introductionmentioning

confidence: 99%

Ontology-Based Healthcare Named Entity Recognition from Twitter Messages Using a Recurrent Neural Network Approach

Batbaatar

Ryu

2019

IJERPH

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Named Entity Recognition (NER) in the healthcare domain involves identifying and categorizing disease, drugs, and symptoms for biosurveillance, extracting their related properties and activities, and identifying adverse drug events appearing in texts. These tasks are important challenges in healthcare. Analyzing user messages in social media networks such as Twitter can provide opportunities to detect and manage public health events. Twitter provides a broad range of short messages that contain interesting information for information extraction. In this paper, we present a Health-Related Named Entity Recognition (HNER) task using healthcare-domain ontology that can recognize health-related entities from large numbers of user messages from Twitter. For this task, we employ a deep learning architecture which is based on a recurrent neural network (RNN) with little feature engineering. To achieve our goal, we collected a large number of Twitter messages containing health-related information, and detected biomedical entities from the Unified Medical Language System (UMLS). A bidirectional long short-term memory (BiLSTM) model learned rich context information, and a convolutional neural network (CNN) was used to produce character-level features. The conditional random field (CRF) model predicted a sequence of labels that corresponded to a sequence of inputs, and the Viterbi algorithm was used to detect health-related entities from Twitter messages. We provide comprehensive results giving valuable insights for identifying medical entities in Twitter for various applications. The BiLSTM-CRF model achieved a precision of 93.99%, recall of 73.31%, and F1-score of 81.77% for disease or syndrome HNER; a precision of 90.83%, recall of 81.98%, and F1-score of 87.52% for sign or symptom HNER; and a precision of 94.85%, recall of 73.47%, and F1-score of 84.51% for pharmacologic substance named entities. The ontology-based manual annotation results show that it is possible to perform high-quality annotation despite the complexity of medical terminology and the lack of context in tweets.

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“…In order to profile user interests, social recommendation systems have been implemented (Jamali and Ester 2010;Tang et al 2012). Researchers have been using social media data to predict the outcome of the elections, political debates and its influence on the individual, and perspectives into reactions to health and disease outbreaks and spread of news via social networks (Bilal et al 2019b;Nawaz et al 2017;Hermida et al 2012).…”

Section: Social Networkmentioning

confidence: 99%

Smart Cities Data: Framework, Applications, and Challenges

Bilal

Usmani

Tayyab

et al. 2020

Handbook of Smart Cities

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Recent technological developments and the availability of enormous amounts of real-time data have played a vital role in the expansion, evolution, and success of smart city projects. Smart data can be used in a variety of smart city applications, but difficulties in managing such data are pushing smart cities toward the adoption of data management frameworks. Many studies have brought into focus the importance of these frameworks as they combine data collection, processing, analysis, management, and visualization and provide privacy and security features for different smart city applications, i.e., transportation, to promote a better quality of life. This chapter highlights key components of the data management framework, reviews various smart city applications, and discusses privacy and security challenges associated with smart city data. From the perspective of data frameworks, it is seen that the data used in smart city applications is unstructured coming from heterogeneous sources, i.e., sensors and social media, besides others. Therefore, the collection, processing, analysis, management, and visualization of such data are challenging. To perform these tasks, recent technologies, i.e., Internet of Things (IoT), sensor networks, machine learning, etc., have been used. Moreover, the use of smart data for smart government and governance provides several facilities for the public and business. The smart data is revolutionizing the daily communication of users along with their mode of transportation by introducing Social IoT (SIoT) and autonomous vehicles. Lastly, the challenges related to privacy and security of the data in smart cities that needed to be addressed are highlighted. This chapter will guide academics and enterprises to progress in data management framework and its applications in smart cities in the near future.

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Effectiveness of Social Media Data in Healthcare Communication

Cited by 36 publications

References 0 publications

Quantification of Productivity of the Brands on Social Media With Respect to Their Responsiveness

Quantification of Productivity of the Brands on Social Media With Respect to Their Responsiveness

Ontology-Based Healthcare Named Entity Recognition from Twitter Messages Using a Recurrent Neural Network Approach

Smart Cities Data: Framework, Applications, and Challenges

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