The“on hold”event of IEEE Transactions on Intelligent Vehicles(TIV)is a typical case of misunderstanding of how a journal runs,and misleading public opinions from online media seriously harm a journal.Public opinion...The“on hold”event of IEEE Transactions on Intelligent Vehicles(TIV)is a typical case of misunderstanding of how a journal runs,and misleading public opinions from online media seriously harm a journal.Public opinions on this kind of case had never been investigated.The paper proposes a multidimensional quantitative regression framework(MQRF)that demonstrates how to quantify and predict public opinion influences for such cases through the collaboration of large language model and small model.The framework leverages large language models’comprehensive analytical capabilities for contextual understanding while employing specialized small models for precise time-series prediction,creating a synergistic approach that significantly outperforms single-model solutions.We analyze the evolving relationships among public opinions and examine how these changes prompt the journal to disclose and clarify its operational practices.Public misunderstandings cause lasting damage to the journal.Understanding how different types of papers run in the journal can eliminate misunderstanding though with a time lag.Through in-depth analysis of the TIV event and comparative experiments with six small models(Linear,DLinear,NLinear,TimesNet,Transformer,and PatchTST)and six large language models(ChatGPT-4.5,Claude3.7sonnet,Grok3,Deepseek-R1,Doubao,and Qwen2.5-72B-Instruct),the effectiveness of the MQRF is verified.展开更多
The opinion research on traditional Chinese medicine during the coronavirus disease 2019(COVID-19)pandemic on microblog,a social network,took into account the national people’s fight against COVID-19—the research ba...The opinion research on traditional Chinese medicine during the coronavirus disease 2019(COVID-19)pandemic on microblog,a social network,took into account the national people’s fight against COVID-19—the research background—the strength of traditional Chinese medicine during the pandemic—the research topic—and the public opinion—the research object.The timeline was divided into three stages according to the overall heat change.In order to explore and compare people’s emotion and topics of concern on traditional Chinese medicine during the different stages of the pandemic,deep learning analysis methods such as emotional analysis and Latent Dirichlet Allocation analysis were used.This study found that the public’s positive“emotional composition”on traditional Chinese medicine significantly improved within the timeline,while the public’s autonomy was enhanced and the overall public opinion started to show an increased trend.展开更多
As the COVID-19 pandemic sweeps across the globe, police forces are charged with new roles as they engage and enforce new policies and laws governing societal behaviours. However, how the police exercise these powers ...As the COVID-19 pandemic sweeps across the globe, police forces are charged with new roles as they engage and enforce new policies and laws governing societal behaviours. However, how the police exercise these powers is an important factor in shaping public opinion and confidence concerning their activities across space and time. This research developed an analytical framework for measuring the inequality in the public opinion towards policing efforts during the pandemic using Twitter data. We demonstrate the utility of our framework using 3-months of tweets across 42 police force areas (Pfas) of England and Wales (UK). The results reveal that public opinions on policing is overwhelmingly negative across space and time, and that these opinions have been most exacerbated by the COVID-19 pandemic in three specific Pfas, namely Staffordshire, Thames Valley, and North Wales. We provided the link to the open-source script by which this research could be replicated and adapted to other study areas. This research has the potential to help law enforcement understand the dynamics of public confidence and trust in policing and facilitate action towards improved police services.展开更多
At present, the emotion classification method of Weibo public opinions based on graph neural network cannot solve the polysemy problem well, and the scale of global graph with fixed weight is too large. This paper pro...At present, the emotion classification method of Weibo public opinions based on graph neural network cannot solve the polysemy problem well, and the scale of global graph with fixed weight is too large. This paper proposes a feature fusion network model Bert-TextLevelGCN based on BERT pre-training and improved TextGCN. On the one hand, Bert is introduced to obtain the initial vector input of graph neural network containing rich semantic features. On the other hand, the global graph connection window of traditional TextGCN is reduced to the text level, and the message propagation mechanism of global sharing is applied. Finally, the output vector of BERT and TextLevelGCN is fused by interpolation update method, and a more robust mapping of positive and negative sentiment classification of public opinion text of “Tangshan Barbecue Restaurant beating people” is obtained. In the context of the national anti-gang campaign, it is of great significance to accurately and efficiently analyze the emotional characteristics of public opinion in sudden social violence events with bad social impact, which is of great significance to improve the government’s public opinion warning and response ability to public opinion in sudden social security events. .展开更多
This article presents the formal definition and description of popular topics on the Internet,analyzes the relationship between popular words and topics,and finally introduces a method that uses statistics and correla...This article presents the formal definition and description of popular topics on the Internet,analyzes the relationship between popular words and topics,and finally introduces a method that uses statistics and correlation of the popular words in traffic content and network flow characteristics as input for extracting popular topics on the Internet.Based on this,this article adapts a clustering algorithm to extract popular topics and gives formalized results.The test results show that this method has an accuracy of 16.7%in extracting popular topics on the Internet.Compared with web mining and topic detection and tracking(TDT),it can provide a more suitable data source for effective recovery of Internet public opinions.展开更多
基金supported by the Shandong Provincial Natural Science Foundation Major Basic Research Program(No.ZR2024ZD20)the Shandong Data Open Innovative Application Laboratory,and the National Natural Science Foundation of China(No.62072469).
文摘The“on hold”event of IEEE Transactions on Intelligent Vehicles(TIV)is a typical case of misunderstanding of how a journal runs,and misleading public opinions from online media seriously harm a journal.Public opinions on this kind of case had never been investigated.The paper proposes a multidimensional quantitative regression framework(MQRF)that demonstrates how to quantify and predict public opinion influences for such cases through the collaboration of large language model and small model.The framework leverages large language models’comprehensive analytical capabilities for contextual understanding while employing specialized small models for precise time-series prediction,creating a synergistic approach that significantly outperforms single-model solutions.We analyze the evolving relationships among public opinions and examine how these changes prompt the journal to disclose and clarify its operational practices.Public misunderstandings cause lasting damage to the journal.Understanding how different types of papers run in the journal can eliminate misunderstanding though with a time lag.Through in-depth analysis of the TIV event and comparative experiments with six small models(Linear,DLinear,NLinear,TimesNet,Transformer,and PatchTST)and six large language models(ChatGPT-4.5,Claude3.7sonnet,Grok3,Deepseek-R1,Doubao,and Qwen2.5-72B-Instruct),the effectiveness of the MQRF is verified.
文摘The opinion research on traditional Chinese medicine during the coronavirus disease 2019(COVID-19)pandemic on microblog,a social network,took into account the national people’s fight against COVID-19—the research background—the strength of traditional Chinese medicine during the pandemic—the research topic—and the public opinion—the research object.The timeline was divided into three stages according to the overall heat change.In order to explore and compare people’s emotion and topics of concern on traditional Chinese medicine during the different stages of the pandemic,deep learning analysis methods such as emotional analysis and Latent Dirichlet Allocation analysis were used.This study found that the public’s positive“emotional composition”on traditional Chinese medicine significantly improved within the timeline,while the public’s autonomy was enhanced and the overall public opinion started to show an increased trend.
文摘As the COVID-19 pandemic sweeps across the globe, police forces are charged with new roles as they engage and enforce new policies and laws governing societal behaviours. However, how the police exercise these powers is an important factor in shaping public opinion and confidence concerning their activities across space and time. This research developed an analytical framework for measuring the inequality in the public opinion towards policing efforts during the pandemic using Twitter data. We demonstrate the utility of our framework using 3-months of tweets across 42 police force areas (Pfas) of England and Wales (UK). The results reveal that public opinions on policing is overwhelmingly negative across space and time, and that these opinions have been most exacerbated by the COVID-19 pandemic in three specific Pfas, namely Staffordshire, Thames Valley, and North Wales. We provided the link to the open-source script by which this research could be replicated and adapted to other study areas. This research has the potential to help law enforcement understand the dynamics of public confidence and trust in policing and facilitate action towards improved police services.
文摘At present, the emotion classification method of Weibo public opinions based on graph neural network cannot solve the polysemy problem well, and the scale of global graph with fixed weight is too large. This paper proposes a feature fusion network model Bert-TextLevelGCN based on BERT pre-training and improved TextGCN. On the one hand, Bert is introduced to obtain the initial vector input of graph neural network containing rich semantic features. On the other hand, the global graph connection window of traditional TextGCN is reduced to the text level, and the message propagation mechanism of global sharing is applied. Finally, the output vector of BERT and TextLevelGCN is fused by interpolation update method, and a more robust mapping of positive and negative sentiment classification of public opinion text of “Tangshan Barbecue Restaurant beating people” is obtained. In the context of the national anti-gang campaign, it is of great significance to accurately and efficiently analyze the emotional characteristics of public opinion in sudden social violence events with bad social impact, which is of great significance to improve the government’s public opinion warning and response ability to public opinion in sudden social security events. .
基金was supported by the National Natural Science Foundation of China (Grant No.60574087)the Hi-Tech Research and Development Program of China (2007AA01Z475,2007AA01Z480,2007A-A01Z464)the 111 International Collaboration Program of China.
文摘This article presents the formal definition and description of popular topics on the Internet,analyzes the relationship between popular words and topics,and finally introduces a method that uses statistics and correlation of the popular words in traffic content and network flow characteristics as input for extracting popular topics on the Internet.Based on this,this article adapts a clustering algorithm to extract popular topics and gives formalized results.The test results show that this method has an accuracy of 16.7%in extracting popular topics on the Internet.Compared with web mining and topic detection and tracking(TDT),it can provide a more suitable data source for effective recovery of Internet public opinions.
