Popularity Score Research Articles

Evaluating YouTube as a Good Resource for Enhancing Patient Understanding of Heart Failure.

The aim of our study was to evaluate the content and quality of heart failure posts on the video-sharing site YouTube, which is an easily accessible source of information and is becoming increasingly popular in society for obtaining health information. In December 2023, we evaluated 162 videos after applying the exclusion criteria as a result of our search with the keyword "Heart Failure" on English-sharing sites. In addition to the technical data of the videos, such as views, duration, upload day, likes, and dislikes, we also used indices such as power index and popularity score in the analyses. We evaluated the quality of the videos using the DISCERN and global quality score scales, and the content using our content score scale. We classified the videos in the study into three quality subgroups according to the scores they received on all three scales. The median number of views of the videos included in our study was 31092 (interquartile range (IQR): 3929-127758) and the median video duration was 336 (IQR: 189-843) seconds. The median popularity score was 28.25 (IQR: 4.7-143) and the median power index was 35139 (2061-308128). Group 1 (low quality) included 54 videos with a total score between 10 and 14, group 2 (medium quality) included 54 videos with a total score between 15 and 22, and group 3 (high quality) included 54 videos with a total score between 23 and 30. Views, upload day, and video duration were significantly higher for group 3 videos (p = 0.008, p = 0.001, and p < 0.001, respectively). Likes and dislikes were not significantly different between groups. The popularity score was significantly higher for group 1 videos (39.5 (IQR: 6.5-200), p = 0.023), while the power index was significantly higher for group 3 videos (74206 (IQR: 9477-221408), p = 0.006). Our findings confirm that YouTube, a video-sharing website, is essential for easily sharing and spreading health-related information to a broad audience. Increased attention to videos with scientific content and high-quality scores suggests that YouTube provides accurate and quality information about heart failure. While the number of quality posts tends to increase daily, healthcare professionals should be encouraged to share high-quality scientific videos more frequently.

A Semi-Automated Solution Approach Recommender for a Given Use Case: a Case Study for AI/ML in Oncology via Scopus and OpenAI

Nowadays, literature review is a necessary task when trying to solve a given problem. However, an exhaustive literature review is very time-consuming in today’s vast literature landscape. It can take weeks, even if looking only for abstracts or surveys. Moreover, choosing a method among others, and targeting searches within relevant problem and solution domains, are not easy tasks. These are especially true for young researchers or engineers starting to work in their field. Even if surveys that provide methods used to solve a specific problem already exist, an automatic way to do it for any use case is missing, especially for those who don’t know the existing literature. Our proposed tool, SARBOLD-LLM, allows discovering and choosing among methods related to a given problem, providing additional information about their uses in the literature to derive decision-making insights, in only a few hours. The SARBOLD-LLM comprises three modules: (1: Scopus search) paper selection using a keyword selection scheme to query Scopus API; (2: Scoring and method extraction) relevancy and popularity scores calculation and solution method extraction in papers utilizing OpenAI API (GPT 3.5); (3: Analyzes) sensitivity analysis and post-analyzes which reveals trends, relevant papers and methods. Comparing the SARBOLD-LLM to manual ground truth using precision, recall, and F1-score metrics, the performance results of AI in the oncology case study are 0.68, 0.9, and 0.77, respectively. SARBOLD-LLM demonstrates successful outcomes across various domains, showcasing its robustness and effectiveness. The SARBOLD-LLM addresses engineers more than researchers, as it proposes methods and trends without adding pros and cons. It is a useful tool to select which methods to investigate first and comes as a complement to surveys. This can limit the global search and accumulation of knowledge for the end user. However, it can be used as a director or recommender for future implementation to solve a problem.

“Will I be replaced?” Assessing ChatGPT's effect on software development and programmer perceptions of AI tools

ChatGPT is a language model with artificial intelligence (AI) capabilities that has found utility across various sectors. Given its impact, we conducted two empirical studies to assess the potential and limitations of ChatGPT and other AI tools in software development. In the first study, we evaluated ChatGPT 3.5′s effectiveness in generating code for 180 coding problems from LeetCode, an online coding interview preparation platform. Our findings suggest that ChatGPT 3.5 is more effective in solving easy and medium coding problems but less reliable for harder problems. Further, ChatGPT 3.5 is somewhat more effective at coding problems with higher popularity scores. In the second study, we administered a questionnaire (N = 99) to programmers to gain insights into their views on ChatGPT and other AI tools. Our findings indicate that programmers use AI tools for various tasks, such as generating boilerplate code, explaining complex code, and conducting research. AI tools also help programmers to become more productive by creating better-performing, shorter, and more readable code, among other benefits. However, AI tools can sometimes misunderstand requirements and generate erroneous code. While most programmers are not currently concerned about AI tools replacing them, they are apprehensive about what the future may hold. Our research has also revealed associations between AI tool usage, trust, perceived productivity, and job security threats caused by the tools.

(382) Online Trends for Vasectomy Inquiries following Dobbs v. Jackson Women’s Health Organization Supreme Court Decision

Abstract Introduction On June 24th, 2022, the United States Supreme Court issued its decision in Dobbs v. Jackson Women’s Health Organization that overturned Roe v. Wade, the constitutional right to abortion access in the U.S. There have been reports of increased interest in vasectomies at individual centers following this Supreme Court ruling, though it is unclear what changes have occurred on the national level. Objective We sought to use Google Trends to evaluate online interest in vasectomies in the U.S. in relation to the Dobbs v. Jackson decision. Methods We used Google Trends to evaluate the popularity scores (generated as a relative value amongst search terms from 0-100) of the search term “vasectomy” from January 2022 to January 2023. We calculated average rate of change (ROC) of scores for the time periods immediately surrounding the Dobbs V. Jackson court decision on June 24th, 2022 and the decision’s early leak to press on May 2nd, 2022 to determine if there was change in interest in immediate time surrounding these events. We then compared the baseline popularity scores in the 4 months before and after the decision. Additionally, we assessed the top 10 states searching for “vasectomy” and correlated with abortion access following the Dobbs v. Jackson decision. Results The national average rate of change of searches for “vasectomy” was significantly higher immediately after the Dobbs v. Jackson decision (0.283 vs. -0.210, p=0.003) (Figure 1). There was not a statistically significantly difference in searches of “vasectomy” after the press leak of the decision (0.001 vs. -0.102, p=0.745) (Figure 1). There was not a statistically significant long-term change in search trends pre- and post- Dobbs v. Jackson decision (p=0.506). Among the 10 states with the highest searches for “vasectomy” after the decision, 7 had abortion bans that went into effect following the Dobbs v. Jackson decision (Table 1). Conclusions There were significantly increased Google searches for “vasectomy” following the Dobbs v. Jackson Supreme Court decision to repeal the constitutional right to abortion and most of the states with the highest interest had abortion bans that went into effect following the decision. Urologists should be aware that abortion policies can influence the reproductive interests of their male patients. Disclosure No.

Predicting and analyzing the popularity of false rumors in Weibo

Malicious online rumors with high popularity, if left undetected, can spread very quickly with damaging societal implications. The development of reliable computational methods for early prediction of the popularity of false rumors is very much needed, as a complement to related work on automated rumor detection and fact-checking. Besides, detecting false rumors with higher popularity in the early stage allows social media platforms to timely deliver fact-checking information to end users. To this end, we (1) propose a new regression task to predict the future popularity of false rumors given both post and user-level information; (2) introduce a new publicly available dataset in Chinese that includes 19,256 false rumor cases from Weibo, the corresponding profile information of the original spreaders and a rumor popularity score as a function of the shares, replies and reports it has received; (3) develop a new open-source domain adapted pre-trained language model, i.e., BERT-Weibo-Rumor and evaluate its performance against several supervised classifiers using post and user-level information. Our best performing model (KG-Fusion) achieves the lowest RMSE score (1.54) and highest Pearson’s r (0.636), outperforming competitive baselines by leveraging textual information from both the post and the user profile. Our analysis unveils that popular rumors consist of more conjunctions and punctuation marks, while less popular rumors contain more words related to the social context and personal pronouns. Our dataset is publicly available: https://github.com/YIDAMU/Weibo_Rumor_Popularity.

The influence of mass media on the popularity of politicians

Politicians compete with each other for votes, political positions and popularity in an attempt to translate their ideological ideas into policy. Due to a lack of face-to-face interactions, voters base their vote mainly on what they see in the media. To measure the influence of media on political success during routine periods, this paper links popularity polls to media coverage of individual politicians. Using automated content analysis on longitudinal newspaper data (2003–2019), the visibility of individual politicians and the used tone was analyzed. We find that media visibility has an impact on popularity. This media effect is especially important for MPs seeing that the function of higher-ranking politicians already affects their popularity without media visibility. A significant effect is also found for tone on popularity scores. We find a negativity bias in which negative news affects the popularity of politicians, whereas positive news does not make a difference.

Reddit and Google Activity Related to Non-COVID Epidemic Diseases Surged at Start of COVID-19 Pandemic: Retrospective Study.

Resources such as Google Trends and Reddit provide opportunities to gauge real-time popular interest in public health issues. Despite the potential for these publicly available and free resources to help optimize public health campaigns, utilization for this purpose has been limited. To determine whether early public awareness of COVID-19 correlated with elevated public interest in other infectious diseases of public health importance. Google Trends search data and Reddit comment data were analyzed from 2018 through 2020 for the frequency of keywords "Chikungunya", "Ebola", "H1N1", "MERS", "SARS", and "Zika", six highly publicized epidemic diseases in recent decades. After collecting Google Trends relative popularity scores for each of these six terms, unpaired T-tests were used to compare the 2020 weekly scores for each term to its average level over the 3-year study period. The number of Reddit comments per month with each of these six terms was collected and then adjusted for total estimated Reddit monthly comment volume to derive a measure of relative usage, analogous to the Google Trends popularity score. The relative monthly incidence of comments with each search term was then compared to the corresponding search term's pre-COVID monthly comment data, again using unpaired T-tests. P-value cutoffs for statistical significance were determined a priori with a Bonferroni correction. Google Trends and Reddit data both demonstrate large and statistically significant increases in the usage of each evaluated disease term through at least the initial months of the pandemic. Google searches and Reddit comments that included any of the evaluated infectious disease search terms rose significantly in the first months of 2020 above their baseline usage, all peaking in March 2020. Google searches for "SARS" and "MERS" remained elevated for the entirety of the 2020 calendar year, as did Reddit comments with the words "Ebola", "H1N1", "MERS" and "SARS" (P<.001, for each weekly or monthly comparison, respectively). Google Trends and Reddit can readily be used to evaluate real-time general interest levels in public health related topics, providing a tool to better time and direct public health initiatives that require a receptive target audience. The start of the COVID-19 pandemic correlated with increased public interest in other epidemic infectious diseases. We have demonstrated that for six distinct infectious causes of epidemics over the last two decades, public interest rose substantially and rapidly with the outbreak of COVID-19. Our data suggests that for at least several months after the initial outbreak, the public may have been particularly receptive to dialogue on these topics. Public health officials should consider utilizing Google Trends and social media data to identify to patterns of engagement with public health topics in real-time, and to optimize timing of public health campaigns.

Thalassemia patients in transfussion dependent period and after hematopoietic stem cell transplantation: how are the psychiatric status and life quality of these patients?

Although hematopoietic stem cell transplantation (HSCT) has been widely used to treat patients with beta-thalassemia major, evidence showing whether this treatment improves mental health, self esteem and health-related quality of life (HRQoL) is limited. We aimed to describe psychiatric problems, HRQoL and self-esteem scores of patients who have thalassemia and compared with patients who underwent HSCT in the current study. A total of 24 patients with thalassemia major and 13 patients who underwent HSCT at least 2 years ago aged between 7-37 years were included. We used The Children’s Depression Inventory, The Spielberger State-Trait Anxiety Inventory, and Pediatric Quality of LifeTM (PedsQL™) for assesment of children and Beck Depression Inventory (BDI), Beck Anxiety Inventory (BAI), World Health Organization Quality of Life Scale Brief Version (WHOQOL-BREF) for assessment of adults. We also used Piers Harris Self Concept Scale for children and adults. Psychopathologies are common in both groups (50% in Thalassemia group and 69.2% in HSCT group). Popularity scores in Piers Haris scale of patients in HSCT group were significantly higher compared to thalassemia group (p = 0.03). Additionally, HSCT group had higher scores in physical health subscales of HRQoL in both children and parents’(p = 0.02, p = 0.03 respectively). Our findings suggest improved HRQoL and self-esteem in thalassemia patients after HSCT. However, due to the high prevalence of mental disorders in both groups, we would like to emphasize that clinicians should examine not only the physical but also the psychological state of the patients with thalessemia during the their treatment and follow-up period after HSCT.

Rett syndrome severity estimation with the BioStamp nPoint using interactions between heart rate variability and body movement.

Rett syndrome, a rare genetic neurodevelopmental disorder in humans, does not have an effective cure. However, multiple therapies and medications exist to treat symptoms and improve patients' quality of life. As research continues to discover and evaluate new medications for Rett syndrome patients, there remains a lack of objective physiological and motor activity-based (physio-motor) biomarkers that enable the measurement of the effect of these medications on the change in patients' Rett syndrome severity. In our work, using a commercially available wearable chest patch, we recorded simultaneous electrocardiogram and three-axis acceleration from 20 patients suffering from Rett syndrome along with the corresponding Clinical Global Impression-Severity score, which measures the overall disease severity on a 7-point Likert scale. We derived physio-motor features from these recordings that captured heart rate variability, activity metrics, and the interactions between heart rate and activity. Further, we developed machine learning (ML) models to classify high-severity Rett patients from low-severity Rett patients using the derived physio-motor features. For the best-trained model, we obtained a pooled area under the receiver operating curve equal to 0.92 via a leave-one-out-patient cross-validation approach. Finally, we computed the feature popularity scores for all the trained ML models and identified physio-motor biomarkers for Rett syndrome.

Google Trends™ and Quality of Information Analyses of Google™ Searches Pertaining to Concussion.

Sports-related concussions occur with high incidence in the United States. Google Trends™ (GT) analyses indicate changes of public interest in a topic over time, and can be correlated with incidence of health events such as concussion. Internet searches represent a primary means of patient education for many health topics, including concussion; however, the quality of medical information yielded by internet searches is variable and frequently of an inappropriate reading level. This study therefore aims to describe GT over time and evaluate the quality and readability of information produced by Google™ searches of the term "concussion." We identified a strong negative correlation from 2009 to 2016 between GT scores and total number of American high school football participants (R 2 = 0.8553) and participants per school (R 2 = 0.9533). Between 2004 and 2020, the monthly GT popularity score were variable (p = 3.193E-08), with September having the greatest scores, correlating with the height of American tackle football season. Applying five validated quality assessment scoring systems at two time points, it was confirmed that different sources yielded varying quality of information. Academic and non-profit healthcare sources demonstrated the highest quality metrics across two time points. There was significant variability of scores among the different scoring systems, however. The majority of searches at both time points yielded information that was rated as "fair" to "poor" in quality. Applying six readability tests, we revealed that only a single commercial website offered information written at or below the American Medical Association- recommended 6th-grade level for healthcare information. In summary, GT data analyses suggest that searches correlate with the American tackle football season and increased between 2009 and 2016, given that public interest in concussion increased and annual participation in football decreased. The quality of information yielded by Google™ searches and readability are inadequate, indicating the need for significant improvement.

Exploring visual relationship for social media popularity prediction

Social media popularity prediction is an important channel to explore content sharing and communication on social networks. It aims to capture informative cues by analyzing multi-type data (such as images, user profiles, and text) to decide the popularity of a specified post. Intuitively, given an image, humans can volitionally focus on salient objects and relationships that are associated with their interests. For example, when we see the image including the relationship “elephant-attack-van”, it is more natural to increase our interest than the image with “elephant-near-van”. Therefore, exploiting such structural relationships is expected to help the prediction model search for evidence in support of the popularity of posts. However, most current works only focus on the global representation or the isolated objects, while ignoring the structure knowledge contained in images. To address this problem, we propose the relationship-aware social media popularity predictor. First, we extract inter-object relationships via a pre-trained scene graph generator. Then, we design a content-based filtering module to filter redundant relationships and capture the key 〈subject–predicate–object〉 information. Finally, we integrate relationship information with multi-type heterogeneous data and feed them into the CatBoost model for regression. Moreover, our predictor is capable of generating more intuitive interpretations by analyzing visual relationships in images to reasonably infer popularity scores. Extensive experiments conducted on the Social Media Prediction Dataset demonstrate that the proposed method can outperform other state-of-the-art models. Additional ablation studies and visualizations further validate the effectiveness and interpretability.

Impact of the Coronavirus Disease on Micronutrient Search Trends and Interest in Global Population: A Worldwide Google Trend Analysis

Background: Globally, micronutrient use has increased over the last few decades for disease prevention and holistic health. This article has analyzed the worldwide search trends of micronutrients in the Google Trends (GT) platform to understand the significance and correlation of search trends, comparing the search interests across countries and recognizing the factors involved. Methods: Specific search terms were used to identify the search trends of micronutrients in the GT platform. Statistical analysis was done to evaluate the outcomes of significance and correlation of search terms from 2004 to 2021 by using statistical softwares. Results: There was a rise in searches for micronutrients vitamin D, vitamin C, zinc, & iron from 2004 to June 2021 with a steep increase during 2020 correlating with the COVID-19 pandemic. A stable search has been reported in iron searches. Significance and correlation of vitamin D, vitamin C, zinc, & iron over the years and search interest popularity score of countries for micronutrient searches have been described. Conclusions: The GT tool can be a necessary tool for healthcare communities and government sectors to understand people’s interests, necessities and respond to their concerns accordingly, thus helping us promote population-centric awareness and achieve better evidence-based outcomes.

YouTube as a patient-information source for hypothyroidism.

Hypothyroidism is a very common disease that requires life-long treatment. In our study, we analyze the quality of the YouTube videos concerning hypothyroidism as a "source of health information" for the patients, and the (possible) correlation between video quality and video popularity. We included 96 YouTube videos obtained by using the following search terms: "hypothyroidism", "Hashimoto's disease", "thyroid insufficiency", and "low thyroid hormone". We evaluated video quality by using the DISCERN criteria, and video popularity by using the Video Power Index. The mean DISCERN score for both raters was 1.995, indicating poor YouTube videos' quality. Sixty-eight videos achieved a high score in the video power index (VPI). The mean popularity score for videos with misleading information was higher than the mean score for all evaluated videos. The overall quality of YouTube videos regarding hypothyroidism was poor. Videos frequently lack the source of information presented. Besides, content is often incomplete, and sometimes includes misleading statements. Physicians dealing with hypothyroid patients should be aware of the possibility that information and instruction they give to patients could be "modulated" by the availability of both low quality and popular alternative "sources of medical knowledge".

Personalized Travel Recommendation Based on the Fusion of TGI and POI Algorithms

On the way to travel, the public expect to get a tourism experience with low cost, convenient travel, and high comfort. At the same time, they also have different tourism needs such as history and culture, natural landscape, and food shopping. To address the problem that traditional travel route recommendation algorithms have limited accuracy and only analyze text or pictures alone, we propose a personalized travel route recommendation algorithm that integrates text and photo information from travelogues and obtain the historical tourism footprint of tourists by analyzing travel notes. According to the frequency and cooccurrence of scenic spots in the travel notes and the number of photos taken by each scenic spot, the popularity of scenic spots and the interest preferences of various types of tourists are analyzed. Under the given starting and ending points or passing points, the optimal tourism route generation method is designed. Experiments on the real data set of Ctrip Travel website show that the recommendation accuracy of this algorithm is significantly improved compared with the traditional algorithm which only uses travel notes text or photos. Compared with the algorithm that only considers the popularity of interest points or tourists’ interest preferences, the accuracy of the route recommended by the algorithm is improved. Compared with the algorithm that only considers the cooccurrence of scenic spots or only considers the influence of photos, this algorithm can obtain a better popularity score of scenic spots. This method integrates the two kinds of information including picture and text, fully considering the interest of users with high practicability.

Employing Google Trends to Assess Concussion Search Popularity, Seasonality, and Association With High School Sports Participation.

This investigation employs 2004-2020 Google Trends data to correlate concussion search popularity and high school sports participation while assessing search seasonality and factors associated with search escalation. Events linked to concussions, from news to entertainment, altered public perception of concussions and discouraged high-risk activities. Such sentiment likely promoted high school football's 2004-2018 8% decrease in participation, despite simultaneous 8% increase for sports overall. Google Trends (GT) analysis calculates distinct subjects' popularity scores (0-100) by normalizing Google search frequency with other subjects in the same topic. With 35% of patients researching their conditions using Google, tracking concussion popularity scores allows robust analysis of event-driven fluctuation, seasonal variation, and sports impact. Worldwide GT analysis was conducted for the disease topic "Concussion" with the categorical filter "Health" from 2004 to 2020. This period's monthly popularity scores with annual averages and annual high school sports participation data were subsequently obtained from Google Trends and the National Federation of State High School Associations, respectively. Concurrence of significant score changes with CTE studies' publication, release of 2015 film Concussion, and the COVID-19 pandemic validates GT's measurement of public interest. From 2006 to 2016, Pearson's correlation coefficients demonstrated strong negative correlation between GT popularity scores for "Concussion" with total number of high school football participants overall (R2 = 0.8553) and participants per school (R2 = 0.9533). Confirming football-related seasonality, one-way ANOVA regression analysis concluded 2004-2020's mean change in month-specific popularity score is not the same (p = 3.193E-08), and months during football season had statistically significant variability (September: p = 4.389E-05) with elevated average z-scores. Coupled with ANOVA regression and normalized analyses, strong negative correlations between concussion popularity score and high school football participation produce quantitative measures of a long-suspected relationship. Tracking concussion popularity and seasonality with Google Trends provides insight into how, when, and why patients are educating themselves online.

Anomaly Detection in Large-Scale Networks With Latent Space Models

We develop a real-time anomaly detection method for directed activity on large, sparse networks. We model the propensity for future activity using a dynamic logistic model with interaction terms for sender- and receiver-specific latent factors in addition to sender- and receiver-specific popularity scores; deviations from this underlying model constitute potential anomalies. Latent nodal attributes are estimated via a variational Bayesian approach and may change over time, representing natural shifts in network activity. Estimation is augmented with a case-control approximation to take advantage of the sparsity of the network and reduces computational complexity from to O(E), where N is the number of nodes and E is the number of observed edges. We run our algorithm on network event records collected from an enterprise network of over 25,000 computers and are able to identify a red team attack with half the detection rate required of the model without latent interaction terms.

Seasonal Variation and Global Public Interest in the Internet Searches for Osteoporosis.

Background To ascertain the seasonal pattern and global public interest in osteoporosis by evaluating search term popularity changes of the disease over a decade. Methods We applied Google Trends to retrieve search popularity scores for the term “osteoporosis” between January 01, 2004, and December 31, 2019. Cosinor analyses were conducted to examine the seasonality of osteoporosis, and analysis on osteoporosis-related topics including hot topics and rising-related topics was also performed. Results The cosinor analyses demonstrated a statistically significant seasonal variation in relative search volume of the “osteoporosis” in the world (p = 0.0083), USA (p < 0.001), UK (p < 0.001), Canada (p < 0.001), Ireland (p < 0.001), Australia (p < 0.001), and New Zealand (p < 0.001), with a peak in the late winter months and trough in the summer months. The peaks in late winter and valley in summer presented an approximately 6-month difference between hemispheres. The top 11 rising topics were denosumab, FRAX, hypocalcaemia, zoledronic acid, ibandronic acid, osteomyelitis, osteopenia, osteoarthritis, bone, calcium, and bone density. Conclusions Google search query volumes related to osteoporosis follow strong seasonal patterns with late winter peaks and summer troughs. Further studies aimed at elucidating the possible mechanisms behind seasonality in osteoporosis are needed. Moreover, Internet data including the top rising topics may alert physicians to strengthen the propaganda of osteoporosis timely, so as to further promote the development of public health interventions.

Combining deep neural network and bibliometric indicator for emerging research topic prediction

Predicting emerging research topics is important to researchers and policymakers. In this study, we propose a two-step solution to the problem of emerging topic prediction. The first step forecasts the future popularity score, a novel indicator reflecting the impact and growth, of candidate topics in a time-series manner. The second step selects novel topics from the candidates predicted to be popular in the first step. Terms with domain characteristics are used as candidate topics. Deep neural networks, specifically LSTM and NNAR, are applied with nine features of topics to predict popularity score. We evaluated the models and five baselines on two datasets from two perspectives, i.e., the ability to (1) predict the correct indicator value and (2) reconstruct the optimal ranking order. Two types of training strategies were compared, including a global strategy that trains a model with all topics and two local strategies that train separate models with different groups of topics. Our results show that LSTM and NNAR outperform other models in predicting the value of popularity score measured by MAE and RMSE, while LightGBM is a competitive baseline in ranking the topics in terms of NDCG@20. The performance difference of global and local strategies is not significant. Emerging topics predicted by our approach are compared with those by other methods. A qualitative assessment on nominated emerging topics suggests topics nominated by machine learning methods are more alike than those by the rule-based model. Some important topics are nominated according to a preliminary literature analysis. This study exploited the strengths of both machine learning and bibliometric indicator approaches for emerging topic prediction. Deep neural networks are applied where objective optimization target can be defined and measured. Bibliometric indicator offers an efficient way to select novel topics from candidates. The hybrid approach shows promise in considering various characteristics of emerging topics when making predictions.

Multimodal Deep Learning Framework for Image Popularity Prediction on Social Media

Billions of photos are uploaded to the Web daily through various types of social networks. Some of these images receive millions of views and become popular, whereas others remain completely unnoticed. This raises the problem of predicting image popularity on social media. The popularity of an image can be affected by several factors, such as visual content, aesthetic quality, user, post metadata, and time. Thus, considering all these factors is essential for accurately predicting image popularity. In addition, the efficiency of the predictive model also plays a crucial role. In this study, motivated by multimodal learning, which uses information from various modalities, and the current success of convolutional neural networks (CNNs) in various fields, we propose a deep learning model, called visual-social CNN (VSCNN), which predicts the popularity of a posted image by incorporating various types of visual and social features into a unified network model. VSCNN first learns to extract high-level representations from the input visual and social features by utilizing two individual CNNs. The outputs of these two networks are then fused into a joint network to estimate the popularity score in the output layer. We assess the performance of the proposed method by conducting extensive experiments on a data set of approximately 432K images posted on Flickr. The simulation results demonstrate that the proposed VSCNN model significantly outperforms state-of-the-art models, with a relative improvement of greater than 2.33%, 7.59%, and 14.16% in terms of Spearman's Rho, mean absolute error, and mean-squared error, respectively.

Online interest in surgical treatment for benign prostatic hyperplasia using Google trends.

Online health information-seeking behavior has increased over the past 15years; however, little is known about the online interest for surgical treatment of men with benign prostatic hyperplasia. We used Google Trends to evaluate online interest for various surgical procedures for benign prostatic hyperplasia in the US and worldwide. Transurethral resection of the prostate, photoselective vaporization of the prostate, Holmium laser enucleation of the prostate, Urolift, and Rezum were selected as search terms of interest within Google Trends. Google Trends normalizes the popularity of search terms on a 0-100 scale. ANOVA and the average rate of change in popularity scores were conducted from July 2015 to February 2019. From 2004 to 2019, online interest in transurethral resection of the prostate and photoselective vaporization of the prostate remained stable in the US and worldwide; meanwhile, there was an upward trend for Urolift and Rezum. There was a statistically significant increase in the online interest for Holmium laser enucleation of the prostate in the US and worldwide; however, the mean popularity score was significantly higher worldwide. A shift in online interest towards minimally invasive surgical therapies for benign prostatic hyperplasia was demonstrated in the US and worldwide and parallels clinical practice patterns, such as HoLEP and Urolift surgical volumes in Indiana and Australia, respectively. Google Trends can be used in real-time to gauge online interest for surgical procedures and help guide physician-patient counseling.