Mining User-generated Content of Mobile Patient Portal: Dimensions of User Experience

Patient portals are positioned as a central component of patient engagement through the potential to change the physician-patient relationship and enable chronic disease self-management. The incorporation of patient portals provides the promise to deliver excellent quality, at optimized costs, while improving the health of the population. This study extends the existing literature by extracting dimensions related to the Mobile Patient Portal Use. We use a topic modeling approach to systematically analyze users’ feedback from the actual use of a common mobile patient portal, Epic’s MyChart. Comparing results of Latent Dirichlet Allocation analysis with those of human analysis validated the extracted topics. Practically, the results provide insights into adopting mobile patient portals, revealing opportunities for improvement and to enhance the design of current basic portals. Theoretically, the findings inform the social-technical systems and Task-Technology Fit theories in the healthcare field and emphasize important healthcare structural and social aspects. Further, findings inform the humanization of healthcare framework, support the results of existing studies, and introduce new important design dimensions (i.e., aspects) that influence patient satisfaction and adherence to patient portal

Mining User-generated Content of Mobile Patient Portal: Dimensions of User Experience

Patient portals are positioned as a central component of patient engagement through the potential to change the physician-patient relationship and enable chronic disease self-management. The incorporation of patient portals provides the promise to deliver excellent quality, at optimized costs, while improving the health of the population. This study extends the existing literature by extracting dimensions related to the Mobile Patient Portal Use. We use a topic modeling approach to systematically analyze users’ feedback from the actual use of a common mobile patient portal, Epic\u27s MyChart. Comparing results of Latent Dirichlet Allocation analysis with those of human analysis validated the extracted topics. Practically, the results provide insights into adopting mobile patient portals, revealing opportunities for improvement and to enhance the design of current basic portals. Theoretically, the findings inform the social-technical systems and Task-Technology Fit theories in the healthcare field and emphasize important healthcare structural and social aspects. Further, findings inform the humanization of healthcare framework, support the results of existing studies, and introduce new important design dimensions (i.e., aspects) that influence patient satisfaction and adherence to patient portal

An evaluation of the role of sentiment in second screen microblog search tasks

The recent prominence of the real-time web is proving both challenging and disruptive for information retrieval and web data mining research. User-generated content on the real-time web is perhaps best epitomised by content on microblogging platforms, such as Twitter. Given the substantial quantity of microblog posts that may be relevant to a user's query at a point in time, automated methods are required to sift through this information. Sentiment analysis offers a promising direction for modelling microblog content. We build and evaluate a sentiment-based filtering system using real-time user studies. We find a significant role played by sentiment in the search scenarios, observing detrimental effects in filtering out certain sentiment types. We make a series of observations regarding associations between document-level sentiment and user feedback, including associations with user profile attributes, and users' prior topic sentiment

Temporal pattern mining from user generated content

Faster internet, IoT, and social media have reformed the conventional web into a collaborative web resulting in enormous user-generated content. Several studies are focused on such content; however, they mainly focus on textual data, thus undermining the importance of metadata. Considering this gap, we provide a temporal pattern mining framework to model and utilize user-generated content's metadata. First, we scrap 2.1 million tweets from Twitter between Nov-2020 to Sep-2021 about 100 hashtag keywords and present these tweets into 100 User-Tweet-Hashtag (UTH) dynamic graphs. Second, we extract and identify four time-series in three timespans (Day, Hour, and Minute) from UTH dynamic graphs. Lastly, we model these four time-series with three machine learning algorithms to mine temporal patterns with the accuracy of 95.89%, 93.17%, 90.97%, and 93.73%, respectively. We demonstrate that user-generated content's metadata contains valuable information, which helps to understand the users' collective behavior and can be beneficial for business and research. Dataset and codes are publicly available; the link is given in the dataset section

Text vs. Image: An application of unsupervised multi-modal machine learning to online reviews

Online user-generated reviews provide a unique view into consumer perceptions of a business. Extant research has demonstrated that text mining provides insight from textual reviews. More recently, we haven seen the adoption of image mining techniques to analyze visual content as well. With data comprising of user-generated imagery (UGI) and textual reviews, we propose to perform a combination of text- and image mining techniques to extract relevant attributes from both modalities. The analysis allows for a comparison between textual and visual content in online reviews. For the UGI analysis, we use a Deep Embedded Clustering model and for the User Generated Text Analysis we use a TF-IDF based mechanism to obtain attributes and polarities. The overall goal is to extract maximum information from text and images and compare the insights we gather from both. We analyze if any modality is self-sufficient or better than the other and also if both modalities combine to give similar or contrasting insights

Mining and Managing User-Generated Content and Preferences

Ιn this thesis, we present techniques to manage the results of expressive queries, such as skyline, and mine online content that has been generated by users. Given the numerous scenarios and applications where content mining can be applied, we focus, in particular, to two cases: review mining and social media analysis. More specifically, we focus on preference queries, where users can query a set of items, each associated with an attribute set. For each of the attributes, users can specify their preference on whether to minimize or maximize it, e.g., "minimize price", "maximize performance", etc. Such queries are also know as "pareto optimal", or "skyline queries". A drawback of this query type is that the result may become too large for the user to inspect manually. We propose an approach that addresses this issue, by selecting a set of diverse skyline results. We provide a formal definition of skyline diversification and present efficient techniques to return such a set of points. The result can then be ranked according to established quality criteria. We also propose an alternative scheme for ranking skyline results, following an information retrieval approach

Data Mining Techniques for Complex User-Generated Data

Nowadays, the amount of collected information is continuously growing in a variety of different domains. Data mining techniques are powerful instruments to effectively analyze these large data collections and extract hidden and useful knowledge. Vast amount of User-Generated Data (UGD) is being created every day, such as user behavior, user-generated content, user exploitation of available services and user mobility in different domains. Some common critical issues arise for the UGD analysis process such as the large dataset cardinality and dimensionality, the variable data distribution and inherent sparseness, and the heterogeneous data to model the different facets of the targeted domain. Consequently, the extraction of useful knowledge from such data collections is a challenging task, and proper data mining solutions should be devised for the problem under analysis. In this thesis work, we focus on the design and development of innovative solutions to support data mining activities over User-Generated Data characterised by different critical issues, via the integration of different data mining techniques in a unified frame- work. Real datasets coming from three example domains characterized by the above critical issues are considered as reference cases, i.e., health care, social network, and ur- ban environment domains. Experimental results show the effectiveness of the proposed approaches to discover useful knowledge from different domains

Mining opinions in user-generated contents to improve course evaluation

The purpose of this paper is to show how opinion mining may offer an alternative way to improve course evaluation using students’ attitudes posted on Internet forums, discussion groups and/or blogs, which are collectively called user-generated content. We propose a model to mine knowledge from students’ opinions to improve teaching effectiveness in academic institutes. Opinion mining is used to evaluate course quality in two steps: opinion classification and opinion extraction. In opinion classification, machine learning methods have been applied to classify an opinion as positive or negative for each student’s posts. Then, we used opinion extraction to extract features, such as teacher, exams and resources, from the user-generated content for a specific course. Then we grouped and assigned orientations for each feature