Future Popularity Research Articles

Predicting Question Popularity for Community Question Answering

In this paper, we study the problem of predicting popularities of questions in Community Question Answering (CQA). To address this problem, we propose a Posterior Attention Recurrent Point Process Model (PARPP) to take both the interaction of users and the Matthew effect into account for question popularity prediction. Our PARPP uses long short-term memory (LSTM) to encode the observed history and another LSTM network to record each step of decoding information. At each decoding step, it uses prior attention to capture answers that have a greater impact on the problem. When a new answer is observed, it uses Bayes’ rule to modify prior attention and obtain posterior attention. Then, the posterior attention is used to update the decoding status. We further introduce a convergence strategy to capture the Matthew effect in CQA. We conduct experiments on a Zhihu dataset crawled from a famous Chinese CQA forum. The experimental results show that our model outperforms several state-of-the-art methods. We further analyze the attention mechanism in our model. Our analysis shows that the proposed attention mechanism can better capture the impact of each answer on the future popularity of the question, which makes our model more interpretable. Our study would shed light on other similar studies such as answer ranking in response to the question and finding experts who have expertise on the topics of the questions.

Predicting popularity of online products via collective recommendations

Predicting the future popularity of commodities has always been a significant issue in information filtering research. Existing methods predominantly rely on the historical popularity of products, assuming that historically popular items will continue to be popular in the future due to preferential attachment. However, this method has limitations as it neglects the intricate structural information within the bipartite networks connecting users and items. The prediction method based on preferential attachment fails for commodities with the same degree of popularity. In this paper, we propose a popularity prediction method that aggregates user recommendation results to forecast item popularity. The method is general and applicable to any recommendation algorithm. For simplicity, we validate the method using the classic collaborative filtering algorithm. Experiments demonstrate that this method significantly outperforms the preferential attachment predictor in accurately predicting the future popularity of niche commodities.

Structural-topic aware deep neural networks for information cascade prediction.

It is critical to accurately predict the future popularity of information cascades for many related applications, such as online opinion warning or academic influence evaluation. Despite many efforts devoted to developing effective prediction approaches, especially the recent presence of deep learning-based model, the structural information of the cascade network is ignored. Thus, to make use of the structural information in cascade prediction task, we propose a structural-topic aware deep neural networks (STDNN), which firstly learns the structure topic distribution of each node in the cascade, feeds it to a sequential neural network, and finally predicts the future popularity of the cascades. It can inherit the high interpretability of Hawkes process and possesses the high predictive power of deep learning methods, bridging the gap between prediction and understanding of information cascades by capturing indicative graph structures. We evaluate our model through quantitative experiments, where our model exhibits promising performance, efficiency higher than the baselines.

Willingness to pay for autonomous vehicles in China based on SEM-Logit model

Autonomous vehicles (AVs) are expected to be a new way of traveling in the future. The future popularity of AVs has a lot to do with people’s attitudes towards them. This study aims to investigate people’s willingness to pay for AVs, providing practical insights into the value of AVs in the market. Through the analysis of 1,543 questionnaires collects in China, we finds that the willingness to pay for AVs is significantly affected by psychological factors (perceived benefit, perceived safety and trust), demographic determinants (e.g., gender, age, education, income and daily travel time) and road conditions (bike lane). People who are male, younger, more educated and have higher-income are more willing to spend more money on AVs. Our study could provide recommendations for AV developers and policymakers to further promote AVs.

The Positive Effects and Potential Mechanisms of Plant-Based Diet on Improving Children Asthma

Past studies have shown that existing treatment options, other than medications, are ineffective in treating asthma in children. However, according to the findings, the influence of children asthma disease can be effectively decreased by using a Mediterranean diet that is similar to a plant-based diet. This paper summarized the positive effects, mechanisms, side effects and application prospects of plant-based diet on children asthma, starting from two perspectives of increasing intake of plant-based foods and controlling intake of dairy products. According to research, plant-based diets have practical positive effects on reducing childhood asthma through the nutrients they contain, antioxidants, and benefits for the gut microbiota, but children are also at risk for rickets, osteoporosis, low blood pressure, and hyponatremia. In terms of application prospects, this diet needs further support from consumers. The limitations of this study are that there is not data to support the effect of reducing red meat in a plant-based diet on children asthma, and there is a lack of statistics on the direct perceptions of child consumers about this dietary pattern. It is believed that in the future, further research on these two limitations can more strongly prove the function of plant-based diet on reducing and preventing children asthma and enhance the future popularity of plant-based diet.

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.

DanceTrend: An Integration Framework of Video-Based Body Action Recognition and Color Space Features for Dance Popularity Prediction

Background: With the rise of user-generated content (UGC) platforms, we are witnessing an unprecedented surge in data. Among various content types, dance videos have emerged as a potent medium for artistic and emotional expression in the Web 2.0 era. Such videos have increasingly become a significant means for users to captivate audiences and amplify their online influence. Given this, predicting the popularity of dance videos on UGC platforms has drawn significant attention. Methods: This study postulates that body movement features play a pivotal role in determining the future popularity of dance videos. To test this hypothesis, we design a robust prediction framework DanceTrend to integrate the body movement features with color space information for dance popularity prediction. We utilize the jazz dance videos from the comprehensive AIST++ street dance dataset and segment each dance routine video into individual movements. AlphaPose was chosen as the human posture detection algorithm to help us obtain human motion features from the videos. Then, the ST-GCN (Spatial Temporal Graph Convolutional Network) is harnessed to train the movement classification models. These pre-trained ST-GCN models are applied to extract body movement features from our curated Bilibili dance video dataset. Alongside these body movement features, we integrate color space attributes and user metadata for the final dance popularity prediction task. Results: The experimental results endorse our initial hypothesis that the body movement features significantly influence the future popularity of dance videos. A comprehensive evaluation of various feature fusion strategies and diverse classifiers discern that a pre–post fusion hybrid strategy coupled with the XGBoost classifier yields the most optimal outcomes for our dataset.

Forecasting the future popularity of the anti-vax narrative on Twitter with machine learning

Forecasting the future popularity of the anti-vax narrative on Twitter with machine learning

Predicting Popularity of Viral Content in Social Media through a Temporal-Spatial Cascade Convolutional Learning Framework

The viral spread of online content can lead to unexpected consequences such as extreme opinions about a brand or consumers’ enthusiasm for a product. This makes the prediction of viral content’s future popularity an important problem, especially for digital marketers, as well as for managers of social platforms. It is not surprising that conventional methods, which heavily rely on either hand-crafted features or unrealistic assumptions, are insufficient in dealing with this challenging problem. Even state-of-art graph-based approaches are either inefficient to work with large-scale cascades or unable to explain what spread mechanisms are learned by the model. This paper presents a temporal-spatial cascade convolutional learning framework called ViralGCN, not only to address the challenges of existing approaches but also to try to provide some insights into actual mechanisms of viral spread from the perspective of artificial intelligence. We conduct experiments on the real-world dataset (i.e., to predict the retweet popularity of micro-blogs on Weibo). Compared to the existing approaches, ViralGCN possesses the following advantages: the flexible size of the input cascade graph, a coherent method for processing both structural and temporal information, and an intuitive and interpretable deep learning architecture. Moreover, the exploration of the learned features also provides valuable clues for managers to understand the elusive mechanisms of viral spread as well as to devise appropriate strategies at early stages. By using the visualization method, our approach finds that both broadcast and structural virality contribute to online content going viral; the cascade with a gradual descent or ascent-then-descent evolving pattern at the early stage is more likely to gain significant eventual popularity, and even the timing of users participating in the cascade has an effect on future popularity growth.

Symphony in the Latent Space: Provably Integrating High-Dimensional Techniques with Non-linear Machine Learning Models

This paper revisits building machine learning algorithms that involve interactions between entities, such as those between financial assets in an actively managed portfolio, or interactions between users in a social network. Our goal is to forecast the future evolution of ensembles of multivariate time series in such applications (e.g., the future return of a financial asset or the future popularity of a Twitter account). Designing ML algorithms for such systems requires addressing the challenges of high-dimensional interactions and non-linearity. Existing approaches usually adopt an ad-hoc approach to integrating high-dimensional techniques into non-linear models and recent studies have shown these approaches have questionable efficacy in time-evolving interacting systems. To this end, we propose a novel framework, which we dub as the additive influence model. Under our modeling assumption, we show that it is possible to decouple the learning of high-dimensional interactions from the learning of non-linear feature interactions. To learn the high-dimensional interactions, we leverage kernel-based techniques, with provable guarantees, to embed the entities in a low-dimensional latent space. To learn the non-linear feature-response interactions, we generalize prominent machine learning techniques, including designing a new statistically sound non-parametric method and an ensemble learning algorithm optimized for vector regressions. Extensive experiments on two common applications demonstrate that our new algorithms deliver significantly stronger forecasting power compared to standard and recently proposed methods.

Russian beneficiaries: challenges in relation to foundations in 2022 and prospects for Russian Private Foundations

Abstract The lifetime (inter vivos) private foundation is a second attempt by the Russian legislator to introduce private foundations. The first (and practically stillborn) attempt was the post-mortem foundation of 2016, of which exactly one has been established. In stark contrast, there are already six lifetime private foundations, although their legal framework only came into force a year ago, and in a rather turbulent year at that. In this article, which follows on from the article in the 2022 issue, the authors review the main features and weaknesses of this new vehicle, examine its suitability for wealth management and succession planning, and attempt to predict its future popularity.

Morphological and structural modification of CaGe2 by annealing with MgCl2/Mg

Ge-based nanosheets and GeH phases were synthesized in the vapor phase using CaGe2 crystals as templates. Ge-based nanosheets have been synthesized in the liquid-phase, but not in the vapor-phase. Compared with liquid-phase synthesis, vapor-phase synthesis is more useful for nanostructure control and impurity addition technologies. These technologies will increase the future popularity of nanosheets. And it is important to apply these technologies after clarifying the effects of certain parameters, such as annealing temperature and changes in the mole fractions. As an introduction to the future development of vapor-phase synthesized Ge-based nanosheets, this study proposes the thermal annealing of CaGe2 powders with MgCl2. Furthermore, the effects of changing the molar fraction containing Mg atoms and the behavior of HCl were investigated from 100 °C–600 °C.

Predicting the Future Popularity of Academic Publications Using Deep Learning by Considering It as Temporal Citation Networks

Predicting the Future Popularity of Academic Publications Using Deep Learning by Considering It as Temporal Citation Networks

Predicting the Popularity of Online Content by Modeling the Social Influence and Homophily Features

Predicting the popularity of online content on social network can bring considerable economic benefits to companies and marketers, and it has wide application in viral marketing, content recommendation, early warning of social unrest, etc. The diffusion process of online contents is often a complex combination of both social influence and homophily; however, existing works either only consider the social influence or homophily of early infected users and fail to model the joint effect of social influence and homophily when predicting future popularity. In this study, we aim to develop a framework to unify the social influence and homophily in popularity prediction. We use an unsupervised graph neural network framework to model nondirectional social homophily and integrate the attention mechanism with the graph neural network framework to learn the directional and heterogeneous social relationship for generating social influence representation. On the other hand, existing research studies often overlook the social group characteristics of early infected users, and we try to divide users into different social groups based on user interest and learn the social group representation from clusters. We integrate the social influence, homophily, and social group representation of early infected users to make popularity predictions. Experiments on real datasets show that the proposed method significantly improves the prediction accuracy compared with the latest methods, which confirms the importance of joint model social influence and homophily and shows that social group characteristic is an important predictor in the popularity prediction task.

Multi-neural network based tiled 360°video caching with Mobile Edge Computing

It is challenging to stream 360° videos over the mobile network for its stringent latency and high bandwidth requirements. Although edge-based viewport adaptive tiled 360° video streaming solutions alleviate the bandwidth demand, the backhaul congestion and low latency concern remain persistent when data is served from the Content Delivery Network over the Internet. Edge caching can help mitigate these issues by storing the content at the edge of the cellular networks on the base station. However, caching 360° videos is challenging because of the large file size, which is further convoluted by tile selection in caching decisions. In this work, we propose a Mobile Edge Computing (MEC) based tiled 360° caching solution that uses Long–Short-Term-Memory (LSTM) and Convolutional Neural Network (CNN) in conjunction to address the challenges associated with 360° video caching. Specifically, the LSTM model predicts the future popularity of the videos, assisting in cache replacement decisions. For the selected videos, the CNN model, which is trained using the saliency map of the video, identifies the most engaging tiles in the videos for caching using the video content itself. The caching of tiles instead of the whole 360° videos improves the caching efficiency of the resource-constrained MEC server. The LSTM model is optimized based on the loss value of different hyperparameters, and AUROC (Ares Under ROC Curve) is used to evaluate the accuracy of the CNN model. Both the models produce highly accurate results. The results from extensive simulations show that the proposed solution significantly outperforms the existing methods. It improves the cache hit rate by at least 10% and reduces the backhaul usage by at least 35% with significant improvement in end-to-end latency, which is crucial for the quality of experience in 360° video streaming.

Bibliometric Analysis of HIV and Exercise Literature based on Scientific Studies from 1990-2020.

Background:Exercise is believed to play an important role in maintaining functionality in patients with HIV and it is thought that researchers are increasingly interested in this field. We aimed to shed light on the historical development of research on HIV and exercise by utilizing visual mapping method.Methods:Overall, 1051 articles retrieved from Web of Science (WoS) core database were analyzed according to the publication year and language, number of issues, citation, country collaborations, co-citation networks and concept–topic trends by using CiteSpace software.Results:The United States played a key role in country collaborations, and had the highest citation burst. The most cited studies were meta-analysis studies. The studies gathered mainly around the clusters named “physical activity” and “metabolic abnormalities” meanwhile, the recent topics of research were heart failure, metabolism, comorbidity, Ethiopia, muscle, cardiovascular event and drug user.Conclusion:The reason why USA was found to be one of the key actors in the network is supposed to be the financial resources it can allocate for the studies conducted. It appears that the majority of the studies in the field dwell upon the impact of exercise on the physical parameters in HIV patients, whereas there are only a limited number of studies focusing on the impact of exercise on HIV-induced psychological and cognitive problems. Recent studies on neurocognitive impairment, on the other hand, are predictive of possible future popularity of such topics among researchers.

Topics emerged in the biomedical field and their characteristics

This study aims to reveal what kind of topics emerged in the biomedical domain by retrospectively analyzing newly added MeSH (Medical Subject Headings) terms from 2001 to 2010 and how they have been used for indexing since their inclusion in the thesaurus. The goal is to investigate if the future trend of a new topic depends on what kind of topic it is without relying on external indicators such as growth, citation patterns, or word co-occurrences. This topic perspective complements the traditional publication perspective in studying emerging topics. Results show that topic characteristics, including topic category, clinical significance, and if a topic has any narrower terms at the time of inclusion, influence future popularity of a new MeSH. Four emergence trend patterns are identified, including emerged and sustained, emerged not sustained, emerged and fluctuated, and not yet emerged. Predictive models using topic characteristics for emerging topic prediction show promise. This suggests that the characteristics of topics and domain should be considered when predicting future emergence of research topics. This study bridges a gap in emerging topic prediction by offering a topic perspective and advocates for considering topic and domain characteristics as well as economic, medical, and environmental impact when studying emerging topics in the biomedical domain.

Intelligent Resource Management at the Edge for Ubiquitous IoT: An SDN-Based Federated Learning Approach

The ubiquitous nature of Internet of Things (IoT) devices has posited many challenges that need innovative solutions in the 5G era. Software defined networks (SDNs) are becoming indispensable in managing several aspects of next-generation IoT networking that arise from the need to control highly heterogeneous, geographically dispersed, mobile IoT devices. One such aspect is cache management at the edge. Recently, multiple forms of edge resources, including mobile device clouds and micro-edge data centers have emerged to provide scalable cache placement locations that reduce the costs for the mobile network operator (MNO). As all of these service locations are registered with the MNO (or links established after registration with the 5G base station, BS), content should be placed according to the user's demand and the cost the user is willing to pay to receive the desired level of QoS. To this end, it is important to understand the future popularity of the content for its optimal placement considering the highly dynamic user mobility. In this article, we address two key aspects of a mobile IoT network: security and seamless connectivity for data delivery. We rely on the federated learning (FL) architecture, which enables harnessing data and computational capabilities at end-user devices to train machine learning models. We study FL concepts in the domain of edge computing for IoT use cases, such as caching. We draw conclusions from various state-of-the-art models and posit several challenges that can be overcome via a novel proposed control algorithm.

Predictability of Popularity: Gaps between Prediction and Understanding

Can we predict the future popularity of a song, movie or tweet? Recent work suggests that although it may be hard to predict an item's popularity when it is first introduced, peeking into its early adopters and properties of their social network makes the problem easier. We test the robustness of such claims by using data from social networks spanning music, books, photos, and URLs. We find a stronger result: not only do predictive models with peeking achieve high accuracy on all datasets, they also generalize well, so much so that models trained on any one dataset perform with comparable accuracy on items from other datasets. Though practically useful, our models (and those in other work) are intellectually unsatisfying because common formulations of the problem, which involve peeking at the first small-k adopters and predicting whether items end up in the top half of popular items, are both too sensitive to the speed of early adoption and too easy. Most of the predictive power comes from looking at how quickly items reach their first few adopters, while for other features of early adopters and their networks, even the direction of correlation with popularity is not consistent across domains. Problem formulations that examine items that reach k adopters in about the same amount of time reduce the importance of temporal features, but also overall accuracy, highlighting that we understand little about why items become popular while providing a context in which we might build that understanding.

On the Interplay between Social and Topical Structure

People’s interests and people’s social relationships are intuitively connected, but understanding their interplay and whether they can help predict each other has remained an open question. We examine the interface of two decisive structures forming the backbone of online social media: the graph structure of social networks — who connects with whom — and the set structure of topical affiliations — who is interested in what. In studying this interface, we identify key relationships whereby each of these structures can be understood in terms of the other. The context for our analysis is Twitter, a complex social network of both follower relationships and communication relationships. On Twitter, “hashtags” are used to label conversation topics, and we examine hashtag usage alongside these social structures. We find that the hashtags that users adopt can predict their social relationships, and also that the social relationships between the initial adopters of a hashtag can predict the future popularity of that hashtag. By studying weighted social relationships, we observe that while strong reciprocated ties are the easiest to predict from hashtag structure, they are also much less useful than weak directed ties for predicting hashtag popularity. Importantly, we show that computationally simple structural determinants can provide remarkable performance in both tasks. While our analyses focus on Twitter, we view our findings as broadly applicable to topical affiliations and social relationships in a host of diverse contexts, including the movies people watch, the brands people like, or the locations people frequent.