Users of social media often share their feelings or emotional states through their posts. In this study, we developed a deep learning model to identify a user’s mental state based on his/her posting information. To this end, we collected posts from mental health communities in Reddit. By analyzing and learning posting information written by users, our proposed model could accurately identify whether a user’s post belongs to a specific mental disorder, including depression, anxiety, bipolar, borderline personality disorder, schizophrenia, and autism. We believe our model can help identify potential sufferers with mental illness based on their posts. This study further discusses the implication of our proposed model, which can serve as a supplementary tool for monitoring mental health states of individuals who frequently use social media.

Spreading rumors on the Internet has become increasingly pervasive due to the proliferation of online social media. This paper investigates how rumors are amplified by a group of users who share similar interests or views, dubbed as an echo chamber. To this end, we identify and analyze ‘rumor’ echo chambers, each of which is a group of users who have participated in propagating common rumors. By collecting and analyzing 125 recent rumors from six popular fact-checking sites, and their associated 289,202 tweets/retweets generated by 176,362 users, we find that the rumors that are spread by rumor echo chamber members tend to be more viral and quickly propagated than those that are not spread by echo chamber members. We propose the notion of an echo chamber network that represents relations among rumor echo chambers. By identifying the hub rumor echo chambers (in terms of connectivity to other rumor echo chambers) in the echo chamber network, we show that the top 10% of hub rumor echo chambers contribute to propagation of 24% rumors by eliciting more than 36% of retweets, implying that core rumor echo chambers significantly contribute to rumor spreads.

Customer retention has been one of the most recognized research issues in the service industry. The next on the list is predicting customer behaviour or understanding customer intent, which is particularly important for the hospitality and tourism industries. This study investigates the customers' hotel revisiting behaviour using a large-scale customer review data, which can shed light on the potentiality of (i) identifying the factors that are associated with the customer revisiting behaviour and attracting more customers to reuse their services and (ii) predicting future customer revisiting behaviour to a hotel. We analyse the data of 105,126 customers of an online hotel reservation service, and conduct a sentimental analysis on the user feedback reviews. By comparing one-time visitors and re-visitors, our analysis shows that the feedback reviews of re-visitors tend to (i) contain more words in a sentence and (ii) reveal more positive/negative sentiments than those of one-time visitors. On the other hand, the feedback reviews of one-time visitors tended to include more analytical and anxious words than those of re-visitors. The findings can serve as a foundation for the use of big data analysis in hospitality and tourism research.

Purpose: Although the notable and significant role of building information modeling (BIM) technologies in construction industries has gained user attention, only few studies have been examined on the user adoption of the technologies. The purpose of this paper is to introduce an acceptance model for BIM technologies and investigate how external factors which were extracted by in-depth interviews promote the adoption of such technologies. Design/methodology/approach: An on-line survey was conducted by two South Korean survey agencies to test the acceptance model for BIM technologies. Then, the structural equation modeling (SEM) and confirmatory factor analysis (CFA) methods were used. Findings: The results of the SEM and CFA methods from on-site construction employees (n=818) in Korea collected by the online survey indicate that compatibility and organizational support play a core role in positively and significantly affecting both perceived ease of use and usefulness, and that the connections introduced by the origin technology acceptance model are mainly confirmed. Originality/value: Using the findings of the results, both implications and notable limitations are presented. Moreover, practical developers, as well as academic researchers can employ the results when they attempt to conduct future research.

This cross-sectional study aimed to build a deep learning model for detecting neovascular age-related macular degeneration (AMD) and to distinguish retinal angiomatous proliferation (RAP) from polypoidal choroidal vasculopathy (PCV) using a convolutional neural network (CNN). Patients from a single tertiary center were enrolled from January 2014 to January 2020. Spectral-domain optical coherence tomography (SD-OCT) images of patients with RAP or PCV and a control group were analyzed with a deep CNN. Sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUROC) were used to evaluate the model’s ability to distinguish RAP from PCV. The performances of the new model, the VGG-16, Resnet-50, Inception, and eight ophthalmologists were compared. A total of 3951 SD-OCT images from 314 participants (229 AMD, 85 normal controls) were analyzed. In distinguishing the PCV and RAP cases, the proposed model showed an accuracy, sensitivity, and specificity of 89.1%, 89.4%, and 88.8%, respectively, with an AUROC of 95.3% (95% CI 0.727–0.852). The proposed model showed better diagnostic performance than VGG-16, Resnet-50, and Inception-V3 and comparable performance with the eight ophthalmologists. The novel model performed well when distinguishing between PCV and RAP. Thus, automated deep learning systems may support ophthalmologists in distinguishing RAP from PCV.