Microblog Dataset Research Articles

Retweeting behavior prediction based on dynamic Bayesian network classifier in microblogging networks

Nowadays, predicting user retweeting behavior in microblogging networks has gained considerable attention. Solving this problem allows for understanding the underlying mechanism of information diffusion and analyzing diffusion's evolution concerning a particular original microblog over time. Time-series data from microblogging networks are ubiquitous, and user retweeting behavior prediction is primarily considered a classification task. Consequently, there is a significant demand for accurate classification of time-series data. Several influential factors affect user retweeting behavior prediction in microblogging networks. However, much of the existing research focuses on studies that neglect the impact of users' indirect social influence strength. This neglect leads to inaccurate forecasts of indirect retweeting behavior for a particular original microblog. To address this, this paper presents a new time-sensitive measure of social influence strength based on a combination of users' temporal behavior patterns and the shortest cascade path length. Second, it proposes a prediction model based on Dynamic Bayesian Network derivative classifiers called V-DBNC to accurately predict user retweeting behavior over time. The proposed time-sensitive social influence strength factor and other factors, such as individual behavior and similarity-based driving factors, are applied to the V-DBNC model to achieve accurate user retweeting behavior prediction in the microblogging network. The joint density of factors in this classifier is estimated using a multivariate Gaussian kernel function with smoothing parameters. The V-DBNC model is empirically optimized by splitting the smoothing parameters into different-scale intervals and using the model averaging method to select the optimal classifier. Additionally, the proposed model's relatively simple structure helps overcome the overfitting problem and allows it to accumulate classification information through iterative evolution, promoting generalization. The real Twitter microblogging dataset is used to evaluate the performance of V-DBNC. The experimental results demonstrated that the proposed model outperforms other compared approaches when dealing with the classification of time-series data.

Airline Attitudes: Mining Data for Service Sentiment Insights

Published user reviews and opinions underlie sentiment analysis, thereby enabling extraction of essential data about service providers and indicating their market status. This study investigates smell sentimental grouping precision of standard information mining systems against six Indian carriers related social media microblog datasets. The sentiment analysis with classifiers, namely Bayes Net and SVM, achieved good results in their accuracies. The analysis centers on how well passenger sentiments can be separated out by the six airlines. On WEKA tool, Bayes Net gained the highest accuracy whereas on Rapid Miner tool, SVM yields better classification than all others. A look at passengers experience with the airlines According to reviews, Go Air has been one of the top-knotch in terms of service received by customers and turns out to be highest rated airline based on passenger feedback.

A large-scale microblog dataset and stock movement prediction based on Supervised Contrastive Learning model

The integration of Deep Neural Networks (DNN) with Natural Language Processing (NLP) technologies has opened new avenues in financial market prediction, particularly through the utilization of textual information. This study represents a significant advancement, which offers two primary contributions to stock trend prediction: (i) the exploitation of textual data (news, comments, microblogs) using advanced DNN architectures, enhancing market information utilization; (ii) significant improvement of the accuracy of predicting the direction of stock volatility by integrating textual and neural network technologies. Meanwhile, we have crawled, filtered, and constructed a large-scale microblog dataset. This dataset includes approximately 114,992 microblog textual data from 40 Science and Technology Innovation Board (STIB) companies in China during 2021. We conducted a comprehensive analysis using various DNN techniques, including Feedback Neural Networks (FNN), Supervised Contrastive Learning (SCL), Cross Entropy (CE), and Dual Contrastive Learning (DualCL), in conjunction with bag of words models, BERT, and Roberta compilers. Our findings reveal that the SCL method, when combined with microblog data, significantly increases prediction accuracy, particularly during the COVID-19 period. Furthermore, we discovered that using a cross-stock dataset enhances the accuracy of all prediction methods, and random allocation of microblog data leads to better results than sequential allocation. Additionally, we compared the efficacy of traditional models like the CAPM, three-factor, and five-factor models against neural network-based methods. Our results suggest a notable superiority of the SCL method in increasing prediction accuracy. Finally, applying our findings to real-world trading strategies, we demonstrated the practical advantages of using the SCL method in trading, evidenced by significant improvements across all performance indicators.

Sentiment analysis and prediction model based on Chinese government affairs microblogs

Existing sentiment analysis research on Chinese government affairs microblogs primarily focuses on the task of sentiment classification on microblogs. There has been a lack of investigation into the correlation of each government affairs microblog with the sentiment values of the corresponding comments below it. This study constructs a large-scale government affairs microblog dataset and explore the correlation of each microblog with the sentiment values of the corresponding comments below it. We proposed a new framework that includes data collection, sentiment analysis and sentiment prediction model training. This sentiment analysis framework is crucial in the government's understanding of the public's real-time sentiments toward policies. It also helps monitor the Internet public sentiment and actively guide the Internet public opinion. We first analyzed the sentiment distribution of government affairs microblogs and the sentiment values on meaningful words. We also discussed the discrepancy in text similarity and sentiment values between microblogs. Furthermore, we investigated the extreme emotional content and discussed the factors influencing the sentiment values of comments. Finally, we designed a collaborative attention regression model to predict the sentiments of microblogs. The sentiment prediction model performed well in the sentiment prediction regression task. The sentiment analysis and the prediction framework for government affairs microblogs in this study can be used as a reference for government-related Internet opinion monitoring.

Learning Users Inner Thoughts and Emotion Changes for Social Media Based Suicide Risk Detection

Suicide has become a serious problem, hurting the well-being of human society. Thanks to social media, from peopleslinguistic posts, suicide risk detection has achieved good performance. The aim of this study is to investigate whether more significantaccuracy could be achieved. Motivated by the observation that the prior solutions strived to detect suicide risk based on users explicitouter post expressions on social media, and no attempt was made to infer users inner true thoughts and emotion changes from theirnormal open posts for suicide risk detection, we propose to firstly learn the correlations between users normal open posts and hiddencomments, trying to understand users inner true thoughts and emotion changes from his open posts, and then detect users suiciderisk upon the generated intermediate results. The performance study on the microblog dataset (3,652 at-risk microblog users and 3,652ordinary microblog users) and forum dataset (392 at-risk forum users and 108 ordinary forum users) shows that, based on the inferredhidden thoughts and inner emotion changes, the proposed approach can achieve 95% and 90% detection accuracy, respectively.

Rumour identification on Twitter as a function of novel textual and language-context features.

Social microblogs are one of the popular platforms for information spreading. However, with several advantages, these platforms are being used for spreading rumours. At present, the majority of existing approaches identify rumours at the topic level instead of at the tweet/post level. Moreover, prior studies used the sentiment and linguistic features for rumours identification without considering discrete positive and negative emotions and effective part-of-speech features in content-based approaches. Similarly, the majority of prior studies used content-based approaches for feature generation, and recent context-based approaches were not explored. To cope with these challenges, a robust framework for rumour detection at the tweet level is designed in this paper. The model used word2vec embeddings and bidirectional encoder representations from transformers method (BERT) from context-based and discrete emotions, linguistic, and metadata characteristics from content-based approaches. According to our knowledge, we are the first ones who used these features for rumour identification at the tweet/post level. The framework is tested on four real-life twitter microblog datasets. The results show that the detection model is capable of detecting 97%, 86%, 85%, and 80% of rumours on four datasets respectively. In addition, the proposed framework outperformed the three latest state-of-the-art baselines. BERT model presented the best performance among context-based approaches, and linguistic features are best performing among content-based approaches as a stand-alone model. Moreover, the utilization of two-step feature selection further improves the detection model performance.

An emotion role mining approach based on multiview ensemble learning in social networks

Emotion is a status that combines people’s feelings, thoughts, and behaviors, and plays a crucial role in communication among people. Large studies suggest that human emotions can also be conveyed through online interactions. Previous studies have addressed the mechanism of emotional contagion; however, emotional contagion, through users of online social networks, has not yet been thoroughly researched. Therefore, in this study, initially, the definition of emotion roles, which may play an important role in emotional contagion, is introduced. On this basis, an emotion role mining approach based on multiview ensemble learning (ERM-ME) is proposed to detect emotion roles in social networks by fusing the information contained in different features. The ERM-ME approach includes three stages: detection of emotional communities, local fusion, and global fusion. First, ERM-ME divides emotional communities based on user emotional preferences. Then, emotional features are employed to train basic classifiers, which are then combined into meta-classifiers. Finally, an accuracy-based weighted voting scheme is used to integrate the results of meta-classifiers to achieve a more accurate and comprehensive classification. Experiments and evaluations are performed using Flickr and Microblog datasets to verify the practicability and effectiveness of the proposed method. Extensive experimental results show that the proposed approach outperforms alternative methods. The micro F-score is used as an evaluation indicator. Using the ERM-ME approach, the indicator is improved by approximately 1.09%–14.57% on Flickr and 5.19%–8.95% on Microblog, compared with Graph Convolutional Network, random forest, AdaBoost, bagging, and stacking.

A Hybrid Approach for Network Rumor Detection Based on Attention Mechanism and Bidirectional GRU Model in Big Data Environment

In view of the low accuracy of Internet rumor detection in traditional machine learning detection methods due to the influence of comment directivity and incomplete features caused by too long sequences, an Internet rumor detection method combining attention mechanism and bidirectional gated recurrent unit (GRU) model in big data environment is proposed. First, the continuous bag of words (CBOW) model is modified by defining the context window and introducing the weighting module, and it is used to obtain the word vectors of the text. Then, the word vectors are used as the word embedding layer which is fed into a bidirectional GRU combined with an attention mechanism through the convolutional layer and pooling layer of the convolutional neural network (CNN) to fully extract the features of the text. Finally, the text feature vectors are input into the Softmax classifier for classification so as to realize the detection of Internet rumors. Based on the selected microblog dataset, the experimental results of the proposed model show that when the learning rate is set to 0.015, Accuracy, Precision, Recall, and F1 values are about 95.8%, 93.1%, 92.9% and 93.5%, respectively, which are better than other comparison models.

Identification and Classification of Depressed Mental State for End-User over Social Media.

In researching social network data and depression, it is often necessary to manually label depressed and non-depressed users, which is time-consuming and labor-intensive. The aim of this study is that it explores the relationship between social network data and depression. It can also contribute to detecting and identifying depression. Through collecting and analyzing college students' microblog social data, a preliminary screening algorithm for college students' suspected depression microblogs based on depression keywords, and semantic expansion is researched; a comprehensive lexical grammar was proposed. This research provided has a preliminary screening method based on depression keywords and semantic expansion for college students' suspected depression microblogs, with a screening accuracy. This method forms a depression keyword table by constructing the basic keyword table and the semantic expansion based on the word embedding learning model Word2Vec. Finally, the word table is used to calculate the semantic similarity of the tested microblogs and then identify whether it is a suspected depression microblog. The experimental results on the microblog dataset of college students show that the comprehensive lexical method is better than the SDS questionnaire segmentation method and the expert lexical method in terms of screening accuracy; the comprehensive lexical approach can quickly and automatically screen out a tiny proportion of suspected doubts from a large number of college students' microblogs. Depression Weibo can reduce the workload of experts' annotation, improve annotation efficiency, and provide a suitable data processing basis for the subsequent accurate identification (classification problem) of patients with depression.

Event Graph Neural Network for Opinion Target Classification of Microblog Comments

Opinion target classification of microblog comments is one of the most important tasks for public opinion analysis about an event. Due to the high cost of manual labeling, opinion target classification is generally considered as a weak-supervised task. This article attempts to address the opinion target classification of microblog comments through an event graph convolution network (EventGCN) in a weak-supervised manner. Specifically, we take microblog contents and comments as document nodes, and construct an event graph with three typical relationships of event microblogs, including the co-occurrence relationship of event keywords extracted from microblogs, the reply relationship of comments, and the document similarity. Finally, under the supervision of a small number of labels, both word features and comment features can be represented well to complete the classification. The experimental results on two event microblog datasets show that EventGCN can significantly improve the classification performance compared with other baseline models.

Hybrid node‐based tensor graph convolutional network for aspect‐category sentiment classification of microblog comments

SummaryAspect‐category sentiment classification of microblog comments aims to identify the sentiment polarity of different opinion aspects in microblog comments, which is meaningful for the analysis of public opinion. At present, most of aspect‐category sentiment classification methods need much annotation data, and regard comments as independent samples, without using of the relationship between comments. This article proposes an aspect‐category sentiment classification method based on tensor graph convolutional networks. First, the combination of a comment and its aspect category is regarded as a hybrid node, and the original representation of a hybrid node is encoded by the Bert model. Second, sentiment graph and semantic graph are constructed according to the semantic similarity and sentimental relevance between hybrid nodes, and they are stacked into a tensor. Then two convolution operations, including intra‐graph convolution and inter‐graph convolution, are performed for each layer of graph tensor. In this way, hybrid nodes can learn and merge the heterogeneous information of different graphs. Finally, under the supervision of few labeled comments, the sentiment classification can be completed based on the features of the hybrid nodes. Experimental results on two microblog datasets show that the proposed model can significantly improve the performance of sentiment classification compared with other baseline models.

Research on CO-word network topic mining and topic differences based on haze microblog data

Some studies have shown that haze not only poses a threat to people’s health, but also affects the secretion of human hormones, making people depressed and endangering mental health. Microblog has the advantages of short content, rapid communication and convenient interaction. When the haze comes, a large number of topic microblogs related to the haze will be generated. Mining the topics of concern and psychological reactions contained in these microblogs is helpful for resource allocation and public opinion publicity in the case of haze. At present, the research of microblog topic mining in haze situation only involves a single research area, and few studies discuss the spatial differences of different regions. Based on this, this study collected the microblog data of seven provincial capitals in the severe haze areas in 2017, and used the community-based co word network method to complete a series of experimental steps, such as keyword extraction, co-occurrence matrix construction, co-word network construction and topic community detection. On this basis, we detected the topic community in the microblog data set, and analyzed the horizontal differences of topics in different cities. The results show that different cities have not only the same but also different concerns about haze. The results can provide theoretical guidance for the healthy development of cities.

Enhancing keyphrase extraction from microblogs using human reading time

AbstractThe premise of manual keyphrase annotation is to read the corresponding content of an annotated object. Intuitively, when we read, more important words will occupy a longer reading time. Hence, by leveraging human reading time, we can find the salient words in the corresponding content. However, previous studies on keyphrase extraction ignore human reading features. In this article, we aim to leverage human reading time to extract keyphrases from microblog posts. There are two main tasks in this study. One is to determine how to measure the time spent by a human on reading a word. We use eye fixation durations (FDs) extracted from an open source eye‐tracking corpus. Moreover, we propose strategies to make eye FD more effective on keyphrase extraction. The other task is to determine how to integrate human reading time into keyphrase extraction models. We propose two novel neural network models. The first is a model in which the human reading time is used as the ground truth of the attention mechanism. In the second model, we use human reading time as the external feature. Quantitative and qualitative experiments show that our proposed models yield better performance than the baseline models on two microblog datasets.

RNe2Vec: information diffusion popularity prediction based on repost network embedding

With the rapid development in artificial intelligence and mobile networks, the past decade has witnessed the flourish of social media, and information diffusion popularity prediction in social media has attracted wide attention in both academics and industrials. However, existing popularity prediction methods either rely heavily on human experience to handcraft the features, designate the generative model, or largely depend on the underlying user relation network for embedding learning. Motivated by the above observation, this paper studies the precise prediction of the information diffusion popularity only based on early repost information, given that the underlying user relation network is unknown. To solve this problem, we propose RNe2Vec (repost network to vector), a repost network embedding-based diffusion popularity prediction algorithm. Specifically, we first build a repost network from the early repost data, and then use biased random walks to generate node sequences, in which we elaborately design walking rules to capture different repost behaviors. After that we employ the skip-gram method to learn low dimensional node vectors from the node sequences. Finally, we apply PCA (principal component analysis) algorithm on the node vectors for dimensionality reduction, and combine the embedding features with handcrafted features to train the downstream machine learning models. Experimental results on a microblog dataset show that incorporating network embedding features can significantly improve the overall prediction accuracy.

EagleMine: Vision-guided Micro-clusters recognition and collective anomaly detection

Given a graph with millions of nodes, what patterns exist in the distributions of node characteristics? How can we detect them and separate anomalous nodes in a way similar to human visual perception? More generally, how can we identify micro-clusters in a histogram and spot some interesting patterns?In this paper, we propose a vision-guided algorithm, EagleMine, to recognize and summarize node groups in a histogram constructed from some correlated features. EagleMine hierarchically discovers node groups, which form internally connected dense areas in the histogram, by utilizing a water-level tree with multiple resolutions according to the rule of the visual recognition. EagleMine uses the statistical hypothesis test to determine the optimal groups while exploring the tree and simultaneously performs vocabulary-based summarization. Moreover, EagleMine can identify anomalous micro-clusters, consisting of nodes that exhibit very similar and suspicious behavior, deviate away from the majority. Experiments on the real-world datasets show that our method can recognize intuitive node groups as human vision does; it achieves the best summarization performance compared to baselines. In terms of anomaly detection, EagleMine also outperforms the state-of-the-art graph-based methods with significantly improving accuracy in a micro-blog dataset. Moreover, EagleMine can be used for other applications, e.g., to detect the synchronized patterns in the temporal retweet event.

A Measurement Model of Mutual Influence for Information Dissemination.

The recent development of the mobile Internet and the rise of social media have significantly enriched the way people access information. Accurate modeling of the probability of information propagation between users is essential for studying information dissemination issues in social networks. As the dissemination of information is inseparable from the interactions between users, the probability of propagation can be characterized by such interactions. In general, there are differences in the dissemination modes of information that carry different topics in a real social network. Using these factors, we propose a method (TMIVM) to measure the mutual influence between users at the topic level. The method associates two vectorization parameters for each user—an influence vector and a susceptibility vector—where the dimensions of the vector represent different topic categories. The magnitude of the mutual influence between users on different topics can be obtained by the product of the corresponding elements of the vectors. Specifically, in this article, we fit a social network historical information cascade data through Survival Analysis to learn the parameters of the influence and susceptibility vectors. The experimental results on a synthetic data set and a real Microblog data set show that this method better measures the propagation probability and information cascade predictions compared to other methods.

Discerning Influence Patterns with Beta-Poisson Factorization in Microblogging Environments

Social influence analysis in microblogging services has attracted much attention in recent years. However, most previous studies were focused on measuring users’ (topical) influence. Little effort has been made to discern and quantify how a user is influenced. Specifically, the fact that user $i$ i retweets a tweet from author $j$ j could be either because $i$ i is influenced by $j$ j (i.e., $j$ j is a topical authority), or simply because he is “influenced” by the content (interested in the content). To mine such influence patterns, we propose a novel Bayesian factorization model, dubbed Influence Beta-Poisson Factorization (IBPF). IBPF jointly factorizes the retweet data and tweet content to quantify latent topical factors of user preference, author influence and content influence. It generates every retweet record according to the sum of two causing terms: one representing author influence, and the other one derived from content influence. To control the impact of the two terms, for each user IBPF generates a probability for each latent topic by Beta distribution, indicating how strongly the user cares about the topical authority of the author. We develop an efficient variational inference algorithm for IBPF. We demonstrate the efficacy of IBPF on two public microblogging datasets.

Hypergraph Label Propagation Network

In recent years, with the explosion of information on the Internet, there has been a large amount of data produced, and analyzing these data is useful and has been widely employed in real world applications. Since data labeling is costly, lots of research has focused on how to efficiently label data through semi-supervised learning. Among the methods, graph and hypergraph based label propagation algorithms have been a widely used method. However, traditional hypergraph learning methods may suffer from their high computational cost. In this paper, we propose a Hypergraph Label Propagation Network (HLPN) which combines hypergraph-based label propagation and deep neural networks in order to optimize the feature embedding for optimal hypergraph learning through an end-to-end architecture. The proposed method is more effective and also efficient for data labeling compared with traditional hypergraph learning methods. We verify the effectiveness of our proposed HLPN method on a real-world microblog dataset gathered from Sina Weibo. Experiments demonstrate that the proposed method can significantly outperform the state-of-the-art methods and alternative approaches.

A Novel Sentence Embedding Based Topic Detection Method for Microblogs

Topic detection is a difficult challenging task, especially when the exact number of topics is unknown. In this article, we present a novel topic detection approach based on neural computing to detect topics in a microblogging dataset. We use an unsupervised neural sentence embedding model to map blogs to an embedding space. The proposed model is a weighted power mean sentence embedding model in which weights are calculated by a targeted attention mechanism. The experimental results show that our embedding model performs better than baseline in sentence clustering. In addition, we propose a clustering algorithm, referred to as Relationship-Aware DBSCAN (RADBSCAN), to discover topics from a microblogging dataset in which the number of topics is automatically determined by the characteristics of the dataset. Moreover, to provide parameter insensibility, we use the forwarding relationship in the blogs as a bridge of two independent clusters. Finally, we validate the proposed method on a dataset from the Sina microblog. The results show that our approach can detect all topics successfully and can extract the keywords of each topic.

Research on Mining Key Nodes of Complex Web-based Communities Based on Mining Algorithm

Mining key nodes in complex web-based communities is of great value for controlling network public opinion and maintaining network safety. This paper introduced two mining algorithms, PageRank and LeaderRank. In order to better realise the mining of nodes, background nodes were introduced to improve LeaderRank, and an improved LeaderRank (ILR) algorithm combining with the average performance of nodes was designed based on the micro-blog web communities. In the case study, taking a micro-blog dataset as an example, PageRank, LeaderRank and ILR algorithms were used to mine key nodes (which has special influence on the structure of function of networks) of micro-blog web community. The results showed that the improved ILR algorithm designed in this paper could combine with the network's topological attributes and average performance and the mined key nodes reflected the number of followers, likes and forwarding, which indicated the influence of nodes better and showed the quality of the mining results from the algorithm. The experimental results prove the reliability of the mining algorithm designed in this paper and provide some theoretical basis for the key node mining work of complex web-based communities such as micro-blog.