Popularity Of Online Content Research Articles

Internet as a Source of Public Health Information on Acupuncture for Pain Relief: Credibility Assessment and Readability Analysis.

In recent years, there has been a notable increase in the number of patients seeking information from online health websites. As the information available on these websites can significantly impact the overall health of individuals in a society, it is vital for online health information to be presented in a manner that is readable and credible to the general public. To address this concern, the objective of the study was to examine and assess the credibility and readability of websites about acupuncture as a pain management approach. The research population was represented by websites containing information about pain management using acupuncture found through Google, Yahoo, and Bing search engines and with the keywords acupuncture and pain. The websites were evaluated in terms of confirming HON and JAMA criteria. FRE, GFI, FKGL, CLI, SMOG, and ARI indexes were used to assess their readability, and Mann-Whitney and Kruskal-Wallis tests were used to compare the data. This study evaluated 52 websites that appeared on the first three pages of search engine results. Out of the 52 websites analyzed, 40 websites (76.9%) met the validity criteria set by JAMA, while only 21 websites (40.4%) were deemed valid based on the HONcode standards. According to the average readability index (12.38), the contents presented on the websites were at the Difficult to Read level. The mean readability scores of websites across various domains exhibit no statistically significant difference (p > 0.05). The majority of websites that provide acupuncture-related information do meet the verification set by HONcode. Additionally, the scientific and educational content on these websites tends to be more complex than what is recommended by the AMA and NIH. This is concerning given the increasing popularity of online content and the impact it has on public health. Providers of this information must prioritize making it readable to a wide audience.

Predicting the Popularity of Information on Social Platforms without Underlying Network Structure.

The ability to predict the size of information cascades in online social networks is crucial for various applications, including decision-making and viral marketing. However, traditional methods either rely on complicated time-varying features that are challenging to extract from multilingual and cross-platform content, or on network structures and properties that are often difficult to obtain. To address these issues, we conducted empirical research using data from two well-known social networking platforms, WeChat and Weibo. Our findings suggest that the information-cascading process is best described as an activate-decay dynamic process. Building on these insights, we developed an activate-decay (AD)-based algorithm that can accurately predict the long-term popularity of online content based solely on its early repost amount. We tested our algorithm using data from WeChat and Weibo, demonstrating that we could fit the evolution trend of content propagation and predict the longer-term dynamics of message forwarding from earlier data. We also discovered a close correlation between the peak forwarding amount of information and the total amount of dissemination. Finding the peak of the amount of information dissemination can significantly improve the prediction accuracy of our model. Our method also outperformed existing baseline methods for predicting the popularity of information.

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.

TCSE: Trend and cascade based spatiotemporal evolution network to predict online content popularity

TCSE: Trend and cascade based spatiotemporal evolution network to predict online content popularity

Exploring Spillover Effects for COVID-19 Cascade Prediction.

An information outbreak occurs on social media along with the COVID-19 pandemic and leads to an infodemic. Predicting the popularity of online content, known as cascade prediction, allows for not only catching in advance information that deserves attention, but also identifying false information that will widely spread and require quick response to mitigate its negative impact. Among the various information diffusion patterns leveraged in previous works, the spillover effect of the information exposed to users on their decisions to participate in diffusing certain information has not been studied. In this paper, we focus on the diffusion of information related to COVID-19 preventive measures due to its special role in consolidating public efforts to slow down the spread of the virus. Through our collected Twitter dataset, we validate the existence of the spillover effects. Building on this finding, we propose extensions to three cascade prediction methods based on Graph Neural Networks (GNNs). Experiments conducted on our dataset demonstrated that the use of the identified spillover effects significantly improves the state-of-the-art GNN methods in predicting the popularity of not only preventive measure messages, but also other COVID-19 messages.

Popularity Prediction of Online Contents via Cascade Graph and Temporal Information

Predicting the popularity of online content is an important task for content recommendation, social influence prediction and so on. Recent deep learning models generally utilize graph neural networks to model the complex relationship between information cascade graph and future popularity, and have shown better prediction results compared with traditional methods. However, existing models adopt simple graph pooling strategies, e.g., summation or average, which prone to generate inefficient cascade graph representation and lead to unsatisfactory prediction results. Meanwhile, they often overlook the temporal information in the diffusion process which has been proved to be a salient predictor for popularity prediction. To focus attention on the important users and exclude noises caused by other less relevant users when generating cascade graph representation, we learn the importance coefficient of users and adopt sample mechanism in graph pooling process. In order to capture the temporal features in the diffusion process, we incorporate the inter-infection duration time information into our model by using LSTM neural network. The results show that temporal information rather than cascade graph information is a better predictor for popularity. The experimental results on real datasets show that our model significantly improves the prediction accuracy compared with other state-of-the-art methods.

Distributed Learning-Based Cache Replacement in Collaborative Edge Networks

In this letter, distributed content caching is considered in a collaborative edge caching system where a central infostation broadcasts information about the content migration to all edge nodes. Each edge node is equipped with a small base station for fetching the requested contents from its neighbouring edge nodes and with a storage unit for caching the contents. To achieve efficient content caching and collaboration, an online decision-making problem of maximizing the cache-hit-ratio whilst ensuring the end users’ quality-of-experience (QoE) is formulated. Furthermore, it is assumed that the content popularity knowledge is not available in advance and has to be leaned regularly over time in an online manner. To this end, we propose a distributed online content-popularity leaning algorithm based on Thompson sampling for updating the cache storage units in real-time. Simulation results demonstrate that the proposed algorithm outperforms the benchmarks in terms of the cache-hit-ratio and QoE in the long run.

Exploring Trends and Patterns of Popularity Stage Evolution in Social Media

The popularity of online contents in social media frequently experiences ebb and flow, and thus its evolution often involves different stages, such as burst and valley. Exploring the patterns of popularity evolution, especially how burst forms and decays, and even further, predicting the trends of popularity evolution is both an important research topic and beneficial to support decision making for many applications, such as emergency management, business intelligence, and public security. Previous work on popularity prediction has focused on predicting the popularity volume of online contents, and at most, popularity burst and ignored the exploration of popularity evolution and the prediction of its stages. To fill this gap, in this paper, we propose our method for the popularity stage prediction problem both at the microscopic level and macroscopic level. At the microscopic level, we first extract multiple dynamic factors and infer future evolution stage by considering the contributions of different dynamic factors. At the macroscopic level, we extract the overall evolution patterns of popularity stages and adopt a pattern matching-based method to predict future popularity stages. We evaluate the proposed approach using tweets in SinaWeibo, the most popular Twitter-like social media platform in China. The experimental results show the effectiveness of our proposed approach in predicting popularity evolution stages.

Predicting the security threats of internet rumors and spread of false information based on sociological principle

With the fast-growing IoT, regular connectivity through a range of heterogeneous intelligent devices across the Social Online Networks (SON) is feasible and effective to analyze sociological principles. Therefore, Increased user contributions, including web posts, videos and reviews slowly impact the lives of people in the recent past, which triggers volatile knowledge dissemination and undermine protection through gossip dissemination, disinformation, and offensive online debate. Based on the early diffusion status, the goal of this research is to forecast the popularity of online content reliably in the future. Though conventional prediction models are focused primarily on the discovery or integration of a network functionality into a changing time mechanism has been considered as unresolved issues and it has been resolved using Predicting The Security Threats of Internet Rumors (PSTIR) and Spread of False Information Based On Sociological (SFIBS) model with sociology concept. In this paper, the proportion of trustworthy Facebook fans who post regularly in early and future popularity has been analyzed linearly using PSTIR and SFIBS methods. Facebook statistics remind us that mainstream fatigue is an important prediction principle and The mainstream fatigue principle, Besides, it shows the effectiveness of the PSTIR and SFIBS based on experimental study.

Discovering patterns of online popularity from time series

How is popularity gained online? Is being successful strictly related to rapidly becoming viral in an online platform, or is it possible to acquire popularity in a steady and disciplined fashion? What are other temporal characteristics that can unveil the popularity of online content? To answer these questions, we leverage a multifaceted temporal analysis of the evolution of popular online content. We present dipm-SC: a multidimensional shape-based time-series clustering algorithm with a heuristic to find the optimal number of clusters. First, we validate the accuracy of our algorithm on synthetic datasets generated from benchmark time series models. Second, we show that dipm-SC can uncover meaningful clusters of popularity behaviors in real-world GitHub and Twitter datasets. By clustering the multidimensional time-series of the popularity of contents coupled with other domain-specific dimensions, we discover two main patterns of popularity: bursty and steady temporal behaviors. Furthermore, we find that the way popularity is gained over time has no significant impact on the final cumulative popularity.

Generalized Nash equilibrium and dynamics of popularity of online contents

This paper develops a dynamic model of competition for the diffusion of online contents in a two-layer network consisting of content providers and viewers. Each content provider seeks to maximize the profit by determining the optimal views and quality levels. We assume that there is a known and fixed limit to the number of times each viewer can access a content. This requirement generates shared constraints for all the providers. The problem is expressed as a Generalized Nash equilibrium with shared constraints that is then formulated via a variational inequality. We construct the locally projected dynamical system model, which provides a continuous-time evolution of views and quality levels, and whose set of stationary points coincides with the set of solutions to the variational inequality. We discuss some stability conditions using a monotonicity approach, and, finally, we present some numerical examples.

Popularity Prediction on Online Articles with Deep Fusion of Temporal Process and Content Features

Predicting the popularity of online article sheds light to many applications such as recommendation, advertising and information retrieval. However, there are several technical challenges to be addressed for developing the best of predictive capability. (1) The popularity fluctuates under impacts of external factors, which are unpredictable and hard to capture. (2) Content and meta-data features, largely determining the online content popularity, are usually multi-modal and nontrivial to model. (3) Besides, it also needs to figure out how to integrate temporal process and content features modeling for popularity prediction in different lifecycle stages of online articles. In this paper, we propose a Deep Fusion of Temporal process and Content features (DFTC) method to tackle them. For modeling the temporal popularity process, we adopt the recurrent neural network and convolutional neural network. For multi-modal content features, we exploit the hierarchical attention network and embedding technique. Finally, a temporal attention fusion is employed for dynamically integrating all these parts. Using datasets collected from WeChat, we show that the proposed model significantly outperforms state-of-the-art approaches on popularity prediction.

Predicting popularity via a generative model with adaptive peeking window

Predicting the popularity of online content is an important issue for uncovering rules governing collective human behaviors in networks. Also, content popularity prediction finds application in an array of areas. In this paper, we propose a generative approach using a Hawkes process, the purpose of which is to predict the eventual popularity of online content from the observed initial period of the information cascade. Our approach is distinguished from existing approaches via its ability to explore an adaptive peeking window and its added remarkable predictive power that was inherited from the feature-driven method. Further, we proposed a procedure for exploring an adaptive peeking window, which allows us to answer the question, “How can we obtain the most effective part of the history to make an accurate prediction?” And the added predictive layer bridges the gap between the generative method and the feature-driven method. Empirical studies on real world datasets demonstrate that the proposed method significantly outperformed the existing approaches.

User Preference Learning-Based Edge Caching for Fog Radio Access Network

In this paper, the edge caching problem in fog radio access network (F-RAN) is investigated. By maximizing the overall cache hit rate, the edge caching optimization problem is formulated to find the optimal policy. Content popularity in terms of time and space is considered from the perspective of regional users. We propose an online content popularity prediction algorithm by leveraging the content features and user preferences, and an offline user preference learning algorithm by using the online gradient descent (OGD) method and the follow the (proximally) regularized leader (FTRL-Proximal) method. Our proposed edge caching policy not only can promptly predict the future content popularity in an online fashion with low complexity, but also can track the content popularity with spatial and temporal popularity dynamic in time without delay. Furthermore, we design two learning-based edge caching architectures. Moreover, we theoretically derive the upper bound of the popularity prediction error, the lower bound of the cache hit rate, and the regret bound of the overall cache hit rate of our proposed edge caching policy. Simulation results show that the overall cache hit rate of our proposed policy is superior to those of the traditional policies and asymptotically approaches the optimal performance.

A Social-Aware Framework for Efficient Information Dissemination in Wireless Ad Hoc Networks

In wireless ad hoc networks, each node participates in routing by forwarding data to other nodes without a pre-existing infrastructure. Particularly, with the wide adoption of smart devices, the concept of smartphone ad hoc networks (SPANs) has evolved to enable alternate means for information sharing. Using unlicensed frequency spectrum and short-range wireless technologies, a SPAN enables a new paradigm of applications and thus is seen as an attractive component in future wireless networks. In a SPAN, smartphones form local peer-to-peer networks to cooperate and share information efficiently. Recent studies have shown that if the users’ social relations are considered while designing cooperation schemes and protocols in SPANs, the cooperation initialization and content dissemination can be notably improved to increase the overall network efficiency and communications reliability. In this article, we present a social-aware framework for optimizing SPANs by exploiting two layers: users’ relationships in the online social network layer and users’ offline connections and interactions in the physical wireless network layer. The online content popularity distribution is also studied as a result of the users’ online interaction profiles. In the end, we integrate both online and offline layers, and discuss possible applications to further enhance the network performance.

The role of network setting and gender in online content popularity

ABSTRACTIn this study, we explore the role of specific network structures that enhance social capital and assess the extent to which gender, social ties, and communication interaction relate to content popularity within online social networks (OSNs). Our results are based on an extensive OSN data set, containing over 100,000 members, connected by over 1.7 million links. The findings indicate that content popularity inference is more accurate when considering activity interaction among users and that network structures known as advantageous for amassing social capital in the offline environment are relevant online as well. We conclude by discussing how gender mediates the correlation between some network measures and the growth of users’ content popularity and provide a potential explanation for the emergence of gender differences.

Predicting the popularity growth of online content: Model and algorithm

The evolution of the popularity of online content is analyzed, and two characteristic patterns pertaining to linear and non-linear growth periods are detected. While the former characterizes the propagation of online content through a dynamical process in a state of statistical equilibrium, the latter appears when this state is perturbed by exogenous intervention events. Such episodes increase the susceptibility of higher threshold individuals who opportunistically adopt the propagating content. To capture the dynamics of both diffusion modes, the popularity of online content is modeled by interlacing linear and non-linear growth terms, reduced to 1st-degree polynomial and logistic functions corresponding respectively to stationary and non-stationary adoption phases. The precise fit of the model to empirical popularity patterns verifies its suitability as prediction tool. The proposed model is employed to generate forecasts about the popularity of online content through extrapolation. Highly accurate prediction results surpassing existing methods in terms of precision and predictive capacity are demonstrated. The prediction method is formulated into an algorithm, applicable to real time forecasting of the popularity of online content without training, using minimal, macroscopic, publicly available information.

On-line popularity monitoring method based on bloom filters and hash tables for differentiated traffic

Towards line speed and accurateness on-line content popularity monitoring on Content Centric Networking (CCN) routers, we propose a three-stage scheme based on Bloom filters and hash tables for differentiated traffic. At the first stage, we decide whether to deliver the content to the next stage depending on traffic types. The second stage consisting of Standard Bloom filters (SBF) and Counting Bloom filters (CBF) identifies the popular content. Meanwhile, a scalable sliding time window based monitoring scheme for different traffic types is proposed to implement frequent and real-time updates by the change of popularities. Hash tables according with sliding window are used to record the popularity at the third stage. Simulation results reveal that this method reaches a 40Gbps processing speed at lower error probability with less memory, and it is more sensitive to the change of popularity. Additionally, the architecture which can be implemented in CCN router is flexible and scalable.

A Point Process Framework for Predicting Popularity of Content in Online Social Networks

In this paper, we extend previous models to predict the popularity of user-generated content for online social networks (OSNs). We focus on Twitter as the most popular and widely used micro-blogging online social network, and specifically measure the popularity of a brand’s tweet by analyzing the time-series path of its subsequent activities (i.e. retweets, replies and marks as favorite) using multivariate marked Hawkes process models. Self and mutually exciting point process models as a multivariate class are adopted to simulate popularity growth patterns of brand post contents on Twitter. While previous self-exciting point process models aggregate the three types of events into one stream of information and therefore ignore exciting effects among different types of events, this study focuses on incorporating the event type into the predictive models for content popularity, explicitly looking at the effects of various types of users’ activities on Twitter. We aim to develop a mutually exciting point process model in order to incorporate such effects among different types of events. Our results suggest that the mutual-exciting point process model outperforms the self-exciting point process model to predict the popularity of online content, as it is able to capture mutual excitement effects and cross interactions between a sequence of events from one type to another. These results are useful in developing and executing a plan for online activities by the brand owners.

Impact of Bursty Human Activity Patterns on the Popularity of Online Content

The dynamics of online content popularity has attracted more and more researches in recent years. In this paper, we provide a quantitative, temporal analysis about the dynamics of online content popularity in a massive system: Sina Microblog. We use time‐stamped data to investigate the impact of bursty human comment patterns on the popularity of online microblog news. Statistical results indicate that the number of news and comments exhibits an exponential growth. The strength of forwarding and comment is characterized by bursts, displaying fat‐tailed distribution. In order to characterize the dynamics of popularity, we explore the distribution of the time interval Δt between consecutive comment bursts and find that it also follows a power‐law. Bursty patterns of human comment are responsible for the power‐law decay of popularity. These results are well supported by both the theoretical analysis and empirical data.