Complex network models are frequently employed for simulating and studyingdiverse real-world complex systems.Among these models,scale-free networks typically exhibit greater fragility to malicious attacks.Consequently...Complex network models are frequently employed for simulating and studyingdiverse real-world complex systems.Among these models,scale-free networks typically exhibit greater fragility to malicious attacks.Consequently,enhancing the robustness of scale-free networks has become a pressing issue.To address this problem,this paper proposes a Multi-Granularity Integration Algorithm(MGIA),which aims to improve the robustness of scale-free networks while keeping the initial degree of each node unchanged,ensuring network connectivity and avoiding the generation of multiple edges.The algorithm generates a multi-granularity structure from the initial network to be optimized,then uses different optimization strategies to optimize the networks at various granular layers in this structure,and finally realizes the information exchange between different granular layers,thereby further enhancing the optimization effect.We propose new network refresh,crossover,and mutation operators to ensure that the optimized network satisfies the given constraints.Meanwhile,we propose new network similarity and network dissimilarity evaluation metrics to improve the effectiveness of the optimization operators in the algorithm.In the experiments,the MGIA enhances the robustness of the scale-free network by 67.6%.This improvement is approximately 17.2%higher than the optimization effects achieved by eight currently existing complex network robustness optimization algorithms.展开更多
Topic modeling is a fundamental technique of content analysis in natural language processing,widely applied in domains such as social sciences and finance.In the era of digital communication,social scientists increasi...Topic modeling is a fundamental technique of content analysis in natural language processing,widely applied in domains such as social sciences and finance.In the era of digital communication,social scientists increasingly rely on large-scale social media data to explore public discourse,collective behavior,and emerging social concerns.However,traditional models like Latent Dirichlet Allocation(LDA)and neural topic models like BERTopic struggle to capture deep semantic structures in short-text datasets,especially in complex non-English languages like Chinese.This paper presents Generative Language Model Topic(GLMTopic)a novel hybrid topic modeling framework leveraging the capabilities of large language models,designed to support social science research by uncovering coherent and interpretable themes from Chinese social media platforms.GLMTopic integrates Adaptive Community-enhanced Graph Embedding for advanced semantic representation,Uniform Manifold Approximation and Projection-based(UMAP-based)dimensionality reduction,Hierarchical Density-Based Spatial Clustering of Applications with Noise(HDBSCAN)clustering,and large language model-powered(LLM-powered)representation tuning to generate more contextually relevant and interpretable topics.By reducing dependence on extensive text preprocessing and human expert intervention in post-analysis topic label annotation,GLMTopic facilitates a fully automated and user-friendly topic extraction process.Experimental evaluations on a social media dataset sourced from Weibo demonstrate that GLMTopic outperforms Latent Dirichlet Allocation(LDA)and BERTopic in coherence score and usability with automated interpretation,providing a more scalable and semantically accurate solution for Chinese topic modeling.Future research will explore optimizing computational efficiency,integrating knowledge graphs and sentiment analysis for more complicated workflows,and extending the framework for real-time and multilingual topic modeling.展开更多
User-Generated Content(UGC)provides a potential data source which can help us to better describe and understand how places are conceptualized,and in turn better represent the places in Geographic Information Science(G...User-Generated Content(UGC)provides a potential data source which can help us to better describe and understand how places are conceptualized,and in turn better represent the places in Geographic Information Science(GIScience).In this article,we aim at aggregating the shared meanings associated with places and linking these to a conceptual model of place.Our focus is on the metadata of Flickr images,in the form of locations and tags.We use topic modeling to identify regions associated with shared meanings.We choose a grid approach and generate topics associated with one or more cells using Latent Dirichlet Allocation.We analyze the sensitivity of our results to both grid resolution and the chosen number of topics using a range of measures including corpus distance and the coherence value.Using a resolution of 500 m and with 40 topics,we are able to generate meaningful topics which characterize places in London based on 954 unique tags associated with around 300,000 images and more than 7000 individuals.展开更多
This paper deals with the statistical modeling of latent topic hierarchies in text corpora. The height of the topic tree is assumed as fixed, while the number of topics on each level as unknown a priori and to be infe...This paper deals with the statistical modeling of latent topic hierarchies in text corpora. The height of the topic tree is assumed as fixed, while the number of topics on each level as unknown a priori and to be inferred from data. Taking a nonpara-metric Bayesian approach to this problem, we propose a new probabilistic generative model based on the nested hierarchical Dirichlet process (nHDP) and present a Markov chain Monte Carlo sampling algorithm for the inference of the topic tree structure as well as the word distribution of each topic and topic distribution of each document. Our theoretical analysis and experiment results show that this model can produce a more compact hierarchical topic structure and captures more fine-grained topic rela-tionships compared to the hierarchical latent Dirichlet allocation model.展开更多
Topic modeling is a probabilistic model that identifies topics covered in text(s). In this paper, topics were loaded from two implementations of topic modeling, namely, Latent Semantic Indexing (LSI) and Latent Dirich...Topic modeling is a probabilistic model that identifies topics covered in text(s). In this paper, topics were loaded from two implementations of topic modeling, namely, Latent Semantic Indexing (LSI) and Latent Dirichlet Allocation (LDA). This analysis was performed in a corpus of 1000 academic papers written in English, obtained from PLOS ONE website, in the areas of Biology, Medicine, Physics and Social Sciences. The objective is to verify if the four academic fields were represented in the four topics obtained by topic modeling. The four topics obtained from Latent Semantic Indexing (LSI) and Latent Dirichlet Allocation (LDA) did not represent the four academic fields.展开更多
The Analects, Mengzi and Xunzi are the top-three classical works of pre-Qin Confucianism, which epitomized thoughts and ideas of Confucius, Mencius and XunKuang1. There have been lots of spirited and in-depth discussi...The Analects, Mengzi and Xunzi are the top-three classical works of pre-Qin Confucianism, which epitomized thoughts and ideas of Confucius, Mencius and XunKuang1. There have been lots of spirited and in-depth discussions on their ideological inheritance and development from all kinds of academics. This paper tries to cast a new light on these discussions through “machine reading2”.展开更多
Modeling topics in short texts presents significant challenges due to feature sparsity, particularly when analyzing content generated by large-scale online users. This sparsity can substantially impair semantic captur...Modeling topics in short texts presents significant challenges due to feature sparsity, particularly when analyzing content generated by large-scale online users. This sparsity can substantially impair semantic capture accuracy. We propose a novel approach that incorporates pre-clustered knowledge into the BERTopic model while reducing the l2 norm for low-frequency words. Our method effectively mitigates feature sparsity during cluster mapping. Empirical evaluation on the StackOverflow dataset demonstrates that our approach outperforms baseline models, achieving superior Macro-F1 scores. These results validate the effectiveness of our proposed feature sparsity reduction technique for short-text topic modeling.展开更多
Background:An estimated 10 to 30 percent of people who become infected with Severe acute respiratory syndrome coronavirus 2 will experience persistent symptoms after recovering from Coronavirus Disease 2019(COVID-19),...Background:An estimated 10 to 30 percent of people who become infected with Severe acute respiratory syndrome coronavirus 2 will experience persistent symptoms after recovering from Coronavirus Disease 2019(COVID-19),which is known as Long COVID.Social media platforms like Facebook and Twitter are the primary sources to gather and examine people’s opinion and sentiments towards various topics.Methods:In this paper,we aimed to examine sentiments,discover key themes and associated topics in Long COVID-related messages posted by Twitter users in the US between March 2022 and April 2022 using sentiment analysis and topic modeling.Results:A total of 117,789 tweets were examined,of which three dominant themes were identified,ranging from symptoms to social and economic impacts,and preventive measures.We also found that more negative sentiments were expressed in the tweets by users toward long-term COVID-19.Conclusions:Our research throws light on dominant themes,topics and sentiments surrounding the ongoing public health crisis.From the insights gained,we discuss the major implications of this study for health practitioners and policymakers.展开更多
Microblogs have become an important platform for people to publish,transform information and acquire knowledge.This paper focuses on the problem of discovering user interest in microblogs.In this paper,we propose a to...Microblogs have become an important platform for people to publish,transform information and acquire knowledge.This paper focuses on the problem of discovering user interest in microblogs.In this paper,we propose a topic mining model based on Latent Dirichlet Allocation(LDA) named user-topic model.For each user,the interests are divided into two parts by different ways to generate the microblogs:original interest and retweet interest.We represent a Gibbs sampling implementation for inference the parameters of our model,and discover not only user's original interest,but also retweet interest.Then we combine original interest and retweet interest to compute interest words for users.Experiments on a dataset of Sina microblogs demonstrate that our model is able to discover user interest effectively and outperforms existing topic models in this task.And we find that original interest and retweet interest are similar and the topics of interest contain user labels.The interest words discovered by our model reflect user labels,but range is much broader.展开更多
Topic models such as Latent Dirichlet Allocation(LDA) have been successfully applied to many text mining tasks for extracting topics embedded in corpora. However, existing topic models generally cannot discover bursty...Topic models such as Latent Dirichlet Allocation(LDA) have been successfully applied to many text mining tasks for extracting topics embedded in corpora. However, existing topic models generally cannot discover bursty topics that experience a sudden increase during a period of time. In this paper, we propose a new topic model named Burst-LDA, which simultaneously discovers topics and reveals their burstiness through explicitly modeling each topic's burst states with a first order Markov chain and using the chain to generate the topic proportion of documents in a Logistic Normal fashion. A Gibbs sampling algorithm is developed for the posterior inference of the proposed model. Experimental results on a news data set show our model can efficiently discover bursty topics, outperforming the state-of-the-art method.展开更多
Most research on anomaly detection has focused on event that is different from its spatial-temporal neighboring events.It is still a significant challenge to detect anomalies that involve multiple normal events intera...Most research on anomaly detection has focused on event that is different from its spatial-temporal neighboring events.It is still a significant challenge to detect anomalies that involve multiple normal events interacting in an unusual pattern.In this work,a novel unsupervised method based on sparse topic model was proposed to capture motion patterns and detect anomalies in traffic surveillance.scale-invariant feature transform(SIFT)flow was used to improve the dense trajectory in order to extract interest points and the corresponding descriptors with less interference.For the purpose of strengthening the relationship of interest points on the same trajectory,the fisher kernel method was applied to obtain the representation of trajectory which was quantized into visual word.Then the sparse topic model was proposed to explore the latent motion patterns and achieve a sparse representation for the video scene.Finally,two anomaly detection algorithms were compared based on video clip detection and visual word analysis respectively.Experiments were conducted on QMUL Junction dataset and AVSS dataset.The results demonstrated the superior efficiency of the proposed method.展开更多
基金National Natural Science Foundation of China(11971211,12171388).
文摘Complex network models are frequently employed for simulating and studyingdiverse real-world complex systems.Among these models,scale-free networks typically exhibit greater fragility to malicious attacks.Consequently,enhancing the robustness of scale-free networks has become a pressing issue.To address this problem,this paper proposes a Multi-Granularity Integration Algorithm(MGIA),which aims to improve the robustness of scale-free networks while keeping the initial degree of each node unchanged,ensuring network connectivity and avoiding the generation of multiple edges.The algorithm generates a multi-granularity structure from the initial network to be optimized,then uses different optimization strategies to optimize the networks at various granular layers in this structure,and finally realizes the information exchange between different granular layers,thereby further enhancing the optimization effect.We propose new network refresh,crossover,and mutation operators to ensure that the optimized network satisfies the given constraints.Meanwhile,we propose new network similarity and network dissimilarity evaluation metrics to improve the effectiveness of the optimization operators in the algorithm.In the experiments,the MGIA enhances the robustness of the scale-free network by 67.6%.This improvement is approximately 17.2%higher than the optimization effects achieved by eight currently existing complex network robustness optimization algorithms.
基金funded by the Natural Science Foundation of Fujian Province,China,grant No.2022J05291.
文摘Topic modeling is a fundamental technique of content analysis in natural language processing,widely applied in domains such as social sciences and finance.In the era of digital communication,social scientists increasingly rely on large-scale social media data to explore public discourse,collective behavior,and emerging social concerns.However,traditional models like Latent Dirichlet Allocation(LDA)and neural topic models like BERTopic struggle to capture deep semantic structures in short-text datasets,especially in complex non-English languages like Chinese.This paper presents Generative Language Model Topic(GLMTopic)a novel hybrid topic modeling framework leveraging the capabilities of large language models,designed to support social science research by uncovering coherent and interpretable themes from Chinese social media platforms.GLMTopic integrates Adaptive Community-enhanced Graph Embedding for advanced semantic representation,Uniform Manifold Approximation and Projection-based(UMAP-based)dimensionality reduction,Hierarchical Density-Based Spatial Clustering of Applications with Noise(HDBSCAN)clustering,and large language model-powered(LLM-powered)representation tuning to generate more contextually relevant and interpretable topics.By reducing dependence on extensive text preprocessing and human expert intervention in post-analysis topic label annotation,GLMTopic facilitates a fully automated and user-friendly topic extraction process.Experimental evaluations on a social media dataset sourced from Weibo demonstrate that GLMTopic outperforms Latent Dirichlet Allocation(LDA)and BERTopic in coherence score and usability with automated interpretation,providing a more scalable and semantically accurate solution for Chinese topic modeling.Future research will explore optimizing computational efficiency,integrating knowledge graphs and sentiment analysis for more complicated workflows,and extending the framework for real-time and multilingual topic modeling.
基金funded by the Swiss National Science Foundation Project PlaceGen[grant number 200021_149823].
文摘User-Generated Content(UGC)provides a potential data source which can help us to better describe and understand how places are conceptualized,and in turn better represent the places in Geographic Information Science(GIScience).In this article,we aim at aggregating the shared meanings associated with places and linking these to a conceptual model of place.Our focus is on the metadata of Flickr images,in the form of locations and tags.We use topic modeling to identify regions associated with shared meanings.We choose a grid approach and generate topics associated with one or more cells using Latent Dirichlet Allocation.We analyze the sensitivity of our results to both grid resolution and the chosen number of topics using a range of measures including corpus distance and the coherence value.Using a resolution of 500 m and with 40 topics,we are able to generate meaningful topics which characterize places in London based on 954 unique tags associated with around 300,000 images and more than 7000 individuals.
基金Project (No. 60773180) supported by the National Natural Science Foundation of China
文摘This paper deals with the statistical modeling of latent topic hierarchies in text corpora. The height of the topic tree is assumed as fixed, while the number of topics on each level as unknown a priori and to be inferred from data. Taking a nonpara-metric Bayesian approach to this problem, we propose a new probabilistic generative model based on the nested hierarchical Dirichlet process (nHDP) and present a Markov chain Monte Carlo sampling algorithm for the inference of the topic tree structure as well as the word distribution of each topic and topic distribution of each document. Our theoretical analysis and experiment results show that this model can produce a more compact hierarchical topic structure and captures more fine-grained topic rela-tionships compared to the hierarchical latent Dirichlet allocation model.
文摘Topic modeling is a probabilistic model that identifies topics covered in text(s). In this paper, topics were loaded from two implementations of topic modeling, namely, Latent Semantic Indexing (LSI) and Latent Dirichlet Allocation (LDA). This analysis was performed in a corpus of 1000 academic papers written in English, obtained from PLOS ONE website, in the areas of Biology, Medicine, Physics and Social Sciences. The objective is to verify if the four academic fields were represented in the four topics obtained by topic modeling. The four topics obtained from Latent Semantic Indexing (LSI) and Latent Dirichlet Allocation (LDA) did not represent the four academic fields.
文摘The Analects, Mengzi and Xunzi are the top-three classical works of pre-Qin Confucianism, which epitomized thoughts and ideas of Confucius, Mencius and XunKuang1. There have been lots of spirited and in-depth discussions on their ideological inheritance and development from all kinds of academics. This paper tries to cast a new light on these discussions through “machine reading2”.
文摘Modeling topics in short texts presents significant challenges due to feature sparsity, particularly when analyzing content generated by large-scale online users. This sparsity can substantially impair semantic capture accuracy. We propose a novel approach that incorporates pre-clustered knowledge into the BERTopic model while reducing the l2 norm for low-frequency words. Our method effectively mitigates feature sparsity during cluster mapping. Empirical evaluation on the StackOverflow dataset demonstrates that our approach outperforms baseline models, achieving superior Macro-F1 scores. These results validate the effectiveness of our proposed feature sparsity reduction technique for short-text topic modeling.
文摘Background:An estimated 10 to 30 percent of people who become infected with Severe acute respiratory syndrome coronavirus 2 will experience persistent symptoms after recovering from Coronavirus Disease 2019(COVID-19),which is known as Long COVID.Social media platforms like Facebook and Twitter are the primary sources to gather and examine people’s opinion and sentiments towards various topics.Methods:In this paper,we aimed to examine sentiments,discover key themes and associated topics in Long COVID-related messages posted by Twitter users in the US between March 2022 and April 2022 using sentiment analysis and topic modeling.Results:A total of 117,789 tweets were examined,of which three dominant themes were identified,ranging from symptoms to social and economic impacts,and preventive measures.We also found that more negative sentiments were expressed in the tweets by users toward long-term COVID-19.Conclusions:Our research throws light on dominant themes,topics and sentiments surrounding the ongoing public health crisis.From the insights gained,we discuss the major implications of this study for health practitioners and policymakers.
基金This work was supported by the National High Technology Research and Development Program of China（No. 2010AA012505, 2011AA010702, 2012AA01A401 and 2012AA01A402）, Chinese National Science Foundation （No. 60933005, 91124002,61303265）, National Technology Support Foundation （No. 2012BAH38B04） and National 242 Foundation （No. 2011A010）
文摘Microblogs have become an important platform for people to publish,transform information and acquire knowledge.This paper focuses on the problem of discovering user interest in microblogs.In this paper,we propose a topic mining model based on Latent Dirichlet Allocation(LDA) named user-topic model.For each user,the interests are divided into two parts by different ways to generate the microblogs:original interest and retweet interest.We represent a Gibbs sampling implementation for inference the parameters of our model,and discover not only user's original interest,but also retweet interest.Then we combine original interest and retweet interest to compute interest words for users.Experiments on a dataset of Sina microblogs demonstrate that our model is able to discover user interest effectively and outperforms existing topic models in this task.And we find that original interest and retweet interest are similar and the topics of interest contain user labels.The interest words discovered by our model reflect user labels,but range is much broader.
基金Supported by the National High Technology Research and Development Program of China(No.2012AA011005)
文摘Topic models such as Latent Dirichlet Allocation(LDA) have been successfully applied to many text mining tasks for extracting topics embedded in corpora. However, existing topic models generally cannot discover bursty topics that experience a sudden increase during a period of time. In this paper, we propose a new topic model named Burst-LDA, which simultaneously discovers topics and reveals their burstiness through explicitly modeling each topic's burst states with a first order Markov chain and using the chain to generate the topic proportion of documents in a Logistic Normal fashion. A Gibbs sampling algorithm is developed for the posterior inference of the proposed model. Experimental results on a news data set show our model can efficiently discover bursty topics, outperforming the state-of-the-art method.
基金Project(50808025)supported by the National Natural Science Foundation of ChinaProject(20090162110057)supported by the Doctoral Fund of Ministry of Education,China
文摘Most research on anomaly detection has focused on event that is different from its spatial-temporal neighboring events.It is still a significant challenge to detect anomalies that involve multiple normal events interacting in an unusual pattern.In this work,a novel unsupervised method based on sparse topic model was proposed to capture motion patterns and detect anomalies in traffic surveillance.scale-invariant feature transform(SIFT)flow was used to improve the dense trajectory in order to extract interest points and the corresponding descriptors with less interference.For the purpose of strengthening the relationship of interest points on the same trajectory,the fisher kernel method was applied to obtain the representation of trajectory which was quantized into visual word.Then the sparse topic model was proposed to explore the latent motion patterns and achieve a sparse representation for the video scene.Finally,two anomaly detection algorithms were compared based on video clip detection and visual word analysis respectively.Experiments were conducted on QMUL Junction dataset and AVSS dataset.The results demonstrated the superior efficiency of the proposed method.
