The online social platforms, like Twitter, Facebook, LinkedIn and WeChat, have grown really fast in last decade and have been one of the most effective platforms for people to communicate and share information with each other. Due to the "word of mouth" effects, information usually can spread rapidly on these social media platforms. Therefore, it is important to study the mechanisms driving the information diffusion and quantify the consequence of information spread. A lot of efforts have been focused on this problem to help us better understand and achieve higher performance in viral marketing and advertising. On the other hand, the development of neural networks has blossomed in the last few years, leading to a large number of graph representation learning (GRL) models. Compared to traditional models, GRL methods are often shown to be more effective. In this paper, we present a comprehensive review for existing works using GRL methods for popularity prediction problem, and categorize related literatures into two big classes, according to their mainly used model and techniques: embedding-based methods and deep learning methods. Deep learning method is further classified into six small classes: convolutional neural networks, graph convolutional networks, graph attention networks, graph neural networks, recurrent neural networks, and reinforcement learning. We compare the performance of these different models and discuss their strengths and limitations. Finally, we outline the challenges and future chances for popularity prediction problem. 

Graph representation learning for popularity prediction problem: A survey

The online social platforms, like Twitter, Facebook, LinkedIn and WeChat, have grown really fast in last decade and have been one of the most effective platforms for people to communicate and share information with each other. Due to the"word of mouth"effects, information usually can spread rapidly on these social media platforms. Therefore, it is important to study the mechanisms driving the information diffusion and quantify the consequence of information spread. A lot of efforts have been focused on this problem to help us better understand and achieve higher performance in viral marketing and advertising. On the other hand, the development of neural networks has blossomed in the last few years, leading to a large number of graph representation learning (GRL) models. Compared to traditional models, GRL methods are often shown to be more effective. In this paper, we present a comprehensive review for existing works using GRL methods for popularity prediction problem, and categorize related literatures into two big classes, according to their mainly used model and techniques: embedding-based methods and deep learning methods. Deep learning method is further classified into six small classes: convolutional neural networks, graph convolutional networks, graph attention networks, graph neural networks, recurrent neural networks, and reinforcement learning. We compare the performance of these different models and discuss their strengths and limitations. Finally, we outline the challenges and future chances for popularity prediction problem.

Graph Representation Learning for Popularity Prediction Problem: A Survey

Social media platforms have transformed into vast reservoirs of intricate data, accumulating unprecedented volumes of user interactions and behaviors. Advanced big data analytics techniques have enabled a deeper understanding of this data treasure trove, shedding light on the intricate landscape of social media user conduct. This comprehensive guide provides insights into the realm of big data analytics for examining user behavior on diverse social media platforms. In our digital age, users generate staggering data sharing thoughts, opinions, preferences, and emotions online, spanning text-based posts, comments, multimedia content, and network interactions. The report delves into the analytical techniques driving this field, including natural language processing, sentiment analysis, machine learning, and network analysis. It scrutinizes significant research findings from these methods, such as identifying influencer networks, sentiment trends, and predictors of user engagement. However, the field confronts challenges, emphasizing the necessity of ethical data usage and user privacy protection. It also acknowledges complexities linked to real-time data streams, unstructured content, and nuanced interpretation to mitigate errors and biases. The report explores future directions, like countering misinformation, predicting social trends, and delivering highly personalized user experiences. This overview is a valuable resource for researchers, businesses, and policymakers, providing insights into big data analytics for understanding user behavior on social media.

Analyzing User Behavior in Social Media through Big Data Analytics

Data have become a valuable digital resource. It has in turn precipitated the emergence of big data marketplaces. For social network date in the marketplaces, each node should be priced according to its influence. The key challenge is that deep learning based pricing models require initial cascade graphs as inputs to predict influence, which cannot be obtained while pricing nodes. Furthermore, node pricing must enhance purchase intentions while being consistent with their influence. To address these challenges, a node-pricing framework is proposed, in which market price is determined based on the predicted influence. In this framework, corrections are performed by using a graph autoencoder (GAE). The corrections are used to augment the neighborhood subgraph and facilitate the extraction of valid sequence features, which are then used to predict influence. An approximate Shapley value for node influence is used to evaluate the price of the nodes. A multi-perspective pricing approach is further investigated, where consumer utility and the approximate Shapley value for influence are the objectives. An inflection point is chosen on the Pareto frontier to select a price that enhances consumer utility. Extensive experiments were conducted on two real-world social network datasets. The results indicate that our performance is higher than DeepCas by 10.38% in Twitter and 9.64% in Weibo . The price output by our framework is consistent with the nodes’ social marketing value while maximizing consumer utility. 

Social network node pricing based on graph autoencoder in data marketplaces

Streamers are the core competency of the crowd-sourced live streaming (CLS) platform. However, little work has explored how different factors relate to their popularity evolution patterns. In this paper, we will investigate a critical problem, i.e., how to discover the promising streamers in their early stage? To tackle this problem, we first conduct large-scale measurement on a real-world CLS dataset. We find that streamers can indeed be clustered into two evolution types (i.e., rising type and normal type), and these two types of streamers will show differences in some inherent properties. Traditional time-sequential models cannot handle this problem, because they are unable to capture the complicated interactivity and extensive heterogeneity in CLS scenarios. To address their shortcomings, we further propose Niffler, a novel heterogeneous attention temporal graph framework (HATG) for predicting the evolution types of CLS streamers. Specifically, through the graph neural network (GNN) and gated-recurrent-unit (GRU) structure, Niffler can capture both the interactive features and the evolutionary dynamics. Moreover, by integrating the attention mechanism in the model design, Niffler can intelligently preserve the heterogeneity when learning different levels of node representations. We systematically compare Niffler against multiple baselines from different categories, and the experimental results show that our proposed model can achieve the best prediction performance. 

Who is the Rising Star? Demystifying the Promising Streamers in Crowdsourced Live Streaming

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.

Popularity Prediction of Online Contents via Cascade Graph and Temporal Information

The invention provides a false news prediction method, system and device based on deep learning and a medium, which can increase the prediction speed and can avoid the defect of long-term dependence.The method comprises the following steps: 1, preprocessing news text data; 2, mapping the preprocessed news text data into word vectors by using a BERT pre-training model; 3, constructing a predictionmodel based on the deep learning neural network, wherein the constructed prediction model comprises a feature extraction layer, an attention layer and a classifier layer; 4, obtaining an optimized prediction model through adversarial training; 5, inputting the training data into the optimization prediction model, and training the prediction model to obtain a trained prediction model; 6, inputtingnews text data to be predicted into the trained prediction model, and outputting a prediction result.

False news prediction method, system and device based on deep learning, and medium

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Popularity Prediction of Online Contents via Cascade Graph and Temporal Information

False news prediction method, system and device based on deep learning, and medium