Social media has become increasingly significant in modern society,but it has also turned into a breeding ground for the propagation of misleading information,potentially causing a detrimental impact on public opinion...Social media has become increasingly significant in modern society,but it has also turned into a breeding ground for the propagation of misleading information,potentially causing a detrimental impact on public opinion and daily life.Compared to pure text content,multmodal content significantly increases the visibility and share ability of posts.This has made the search for efficient modality representations and cross-modal information interaction methods a key focus in the field of multimodal fake news detection.To effectively address the critical challenge of accurately detecting fake news on social media,this paper proposes a fake news detection model based on crossmodal message aggregation and a gated fusion network(MAGF).MAGF first uses BERT to extract cumulative textual feature representations and word-level features,applies Faster Region-based ConvolutionalNeuralNetwork(Faster R-CNN)to obtain image objects,and leverages ResNet-50 and Visual Geometry Group-19(VGG-19)to obtain image region features and global features.The image region features and word-level text features are then projected into a low-dimensional space to calculate a text-image affinity matrix for cross-modal message aggregation.The gated fusion network combines text and image region features to obtain adaptively aggregated features.The interaction matrix is derived through an attention mechanism and further integrated with global image features using a co-attention mechanism to producemultimodal representations.Finally,these fused features are fed into a classifier for news categorization.Experiments were conducted on two public datasets,Twitter and Weibo.Results show that the proposed model achieves accuracy rates of 91.8%and 88.7%on the two datasets,respectively,significantly outperforming traditional unimodal and existing multimodal models.展开更多
In recent years,how to efficiently and accurately identify multi-model fake news has become more challenging.First,multi-model data provides more evidence but not all are equally important.Secondly,social structure in...In recent years,how to efficiently and accurately identify multi-model fake news has become more challenging.First,multi-model data provides more evidence but not all are equally important.Secondly,social structure information has proven to be effective in fake news detection and how to combine it while reducing the noise information is critical.Unfortunately,existing approaches fail to handle these problems.This paper proposes a multi-model fake news detection framework based on Tex-modal Dominance and fusing Multiple Multi-model Cues(TD-MMC),which utilizes three valuable multi-model clues:text-model importance,text-image complementary,and text-image inconsistency.TD-MMC is dominated by textural content and assisted by image information while using social network information to enhance text representation.To reduce the irrelevant social structure’s information interference,we use a unidirectional cross-modal attention mechanism to selectively learn the social structure’s features.A cross-modal attention mechanism is adopted to obtain text-image cross-modal features while retaining textual features to reduce the loss of important information.In addition,TD-MMC employs a new multi-model loss to improve the model’s generalization ability.Extensive experiments have been conducted on two public real-world English and Chinese datasets,and the results show that our proposed model outperforms the state-of-the-art methods on classification evaluation metrics.展开更多
“The Era of Foreign Newspapers”refers to the period from the emergence of the first modern newspaper in Hankow in 1866 to 1900 when Wuhan’s newspaper industry was dominated by foreign newspapers.The well-known fore...“The Era of Foreign Newspapers”refers to the period from the emergence of the first modern newspaper in Hankow in 1866 to 1900 when Wuhan’s newspaper industry was dominated by foreign newspapers.The well-known foreign newspapers in Wuhan during this period mainly included Hankow Times,The New Edition of Tan Dao,and Han Bao.The subjective purpose of foreigners’early endeavors of running newspapers in Wuhan was mainly to use newspapers to convey business information,spread religion,or influence public opinion in order to safeguard their own interests in China.However,foreign newspapers in this period played a constructive role in the development of Wuhan’s local society:It gave birth to the emergence and development of the first private and official newspapers in Wuhan and shaped the local social,cultural,and political changes in Wuhan in the late Qing Dynasty.Sorting out and explaining the constructive influence of Hankow’s foreign newspaper in this period has certain significance for restoring the social and political landscape of Wuhan at that time and better understanding the context of historical development.展开更多
As social networks become increasingly complex, contemporary fake news often includes textual descriptionsof events accompanied by corresponding images or videos. Fake news in multiple modalities is more likely tocrea...As social networks become increasingly complex, contemporary fake news often includes textual descriptionsof events accompanied by corresponding images or videos. Fake news in multiple modalities is more likely tocreate a misleading perception among users. While early research primarily focused on text-based features forfake news detection mechanisms, there has been relatively limited exploration of learning shared representationsin multimodal (text and visual) contexts. To address these limitations, this paper introduces a multimodal modelfor detecting fake news, which relies on similarity reasoning and adversarial networks. The model employsBidirectional Encoder Representation from Transformers (BERT) and Text Convolutional Neural Network (Text-CNN) for extracting textual features while utilizing the pre-trained Visual Geometry Group 19-layer (VGG-19) toextract visual features. Subsequently, the model establishes similarity representations between the textual featuresextracted by Text-CNN and visual features through similarity learning and reasoning. Finally, these features arefused to enhance the accuracy of fake news detection, and adversarial networks have been employed to investigatethe relationship between fake news and events. This paper validates the proposed model using publicly availablemultimodal datasets from Weibo and Twitter. Experimental results demonstrate that our proposed approachachieves superior performance on Twitter, with an accuracy of 86%, surpassing traditional unimodalmodalmodelsand existing multimodal models. In contrast, the overall better performance of our model on the Weibo datasetsurpasses the benchmark models across multiple metrics. The application of similarity reasoning and adversarialnetworks in multimodal fake news detection significantly enhances detection effectiveness in this paper. However,current research is limited to the fusion of only text and image modalities. Future research directions should aimto further integrate features fromadditionalmodalities to comprehensively represent themultifaceted informationof fake news.展开更多
Users’interests are often diverse and multi-grained,with their underlying intents even more so.Effectively captur-ing users’interests and uncovering the relationships between diverse interests are key to news recomm...Users’interests are often diverse and multi-grained,with their underlying intents even more so.Effectively captur-ing users’interests and uncovering the relationships between diverse interests are key to news recommendation.Meanwhile,diversity is an important metric for evaluating news recommendation algorithms,as users tend to reject excessive homogeneous information in their recommendation lists.However,recommendation models themselves lack diversity awareness,making it challenging to achieve a good balance between the accuracy and diversity of news recommendations.In this paper,we propose a news recommendation algorithm that achieves good performance in both accuracy and diversity.Unlike most existing works that solely optimize accuracy or employ more features to meet diversity,the proposed algorithm leverages the diversity-aware capability of the model.First,we introduce an augmented user model to fully capture user intent and the behavioral guidance they might undergo as a result.Specifically,we focus on the relationship between the original clicked news and the augmented clicked news.Moreover,we propose an effective adversarial training method for diversity(AT4D),which is a pluggable component that can enhance both the accuracy and diversity of news recommendation results.Extensive experiments on real-world datasets confirm the efficacy of the proposed algorithm in improving both the accuracy and diversity of news recommendations.展开更多
News media profiling is helpful in preventing the spread of fake news at the source and maintaining a good media and news ecosystem.Most previous works only extract features and evaluate media from one dimension indep...News media profiling is helpful in preventing the spread of fake news at the source and maintaining a good media and news ecosystem.Most previous works only extract features and evaluate media from one dimension independently,ignoring the interconnections between different aspects.This paper proposes a novel news media bias and factuality profiling framework assisted by correlated features.This framework models the relationship and interaction between media bias and factuality,utilizing this relationship to assist in the prediction of profiling results.Our approach extracts features independently while aligning and fusing them through recursive convolu-tion and attention mechanisms,thus harnessing multi-scale interactive information across different dimensions and levels.This method improves the effectiveness of news media evaluation.Experimental results indicate that our proposed framework significantly outperforms existing methods,achieving the best performance in Accuracy and F1 score,improving by at least 1%compared to other methods.This paper further analyzes and discusses based on the experimental results.展开更多
With the rapid spread of Internet information and the spread of fake news,the detection of fake news becomes more and more important.Traditional detection methods often rely on a single emotional or semantic feature t...With the rapid spread of Internet information and the spread of fake news,the detection of fake news becomes more and more important.Traditional detection methods often rely on a single emotional or semantic feature to identify fake news,but these methods have limitations when dealing with news in specific domains.In order to solve the problem of weak feature correlation between data from different domains,a model for detecting fake news by integrating domain-specific emotional and semantic features is proposed.This method makes full use of the attention mechanism,grasps the correlation between different features,and effectively improves the effect of feature fusion.The algorithm first extracts the semantic features of news text through the Bi-LSTM(Bidirectional Long Short-Term Memory)layer to capture the contextual relevance of news text.Senta-BiLSTM is then used to extract emotional features and predict the probability of positive and negative emotions in the text.It then uses domain features as an enhancement feature and attention mechanism to fully capture more fine-grained emotional features associated with that domain.Finally,the fusion features are taken as the input of the fake news detection classifier,combined with the multi-task representation of information,and the MLP and Softmax functions are used for classification.The experimental results show that on the Chinese dataset Weibo21,the F1 value of this model is 0.958,4.9% higher than that of the sub-optimal model;on the English dataset FakeNewsNet,the F1 value of the detection result of this model is 0.845,1.8% higher than that of the sub-optimal model,which is advanced and feasible.展开更多
Purpose: Communicating scientific results to the public is essential to inspire future researchers and ensure that discoveries are exploited. News stories about research are a key communication pathway for this and ha...Purpose: Communicating scientific results to the public is essential to inspire future researchers and ensure that discoveries are exploited. News stories about research are a key communication pathway for this and have been manually monitored to assess the extent of press coverage of scholarship.Design/methodology/Approach: To make larger scale studies practical, this paper introduces an automatic method to extract citations from newspaper stories to large sets of academic journals. Curated ProQuest queries were used to search for citations to 9,639 Science and3,412 Social Science Web of Science(WoS) journals from eight UK daily newspapers during2006–2015. False matches were automatically filtered out by a new program, with 94% of the remaining stories meaningfully citing research.Findings: Most Science(95%) and Social Science(94%) journals were never cited by these newspapers. Half of the cited Science journals covered medical or health-related topics,whereas 43% of the Social Sciences journals were related to psychiatry or psychology. From the citing news stories, 60% described research extensively and 53% used multiple sources,but few commented on research quality.Research Limitations: The method has only been tested in English and from the ProQuest Newspapers database.Practical implications: Others can use the new method to systematically harvest press coverage of research.Originality/value: An automatic method was introduced and tested to extract citations from newspaper stories to large sets of academic journals.展开更多
近日,广州医科大学第一附属医院院长兼胸外科主任、AME旗下Journal of Thoracic Disease(JTD)杂志执行主编何建行教授接受了Lancet Oncology杂志的特邀专访(L a n c e t O n c o l 2 0 1 4.s 1 4 7 0-2045(14)71024-1),针对10月份发表在...近日,广州医科大学第一附属医院院长兼胸外科主任、AME旗下Journal of Thoracic Disease(JTD)杂志执行主编何建行教授接受了Lancet Oncology杂志的特邀专访(L a n c e t O n c o l 2 0 1 4.s 1 4 7 0-2045(14)71024-1),针对10月份发表在BM J杂志的关于开胸手术对比胸腔镜手术治疗肺癌的匹配分析(Paul S,et al.BMJ 2014)进行了点评(图1)。被点评的研究,使用了美国最权威的SEER数据库,纳入了6 008例患者,经过预后因素的匹配后。展开更多
Accurately recommending candidate news to users is a basic challenge of personalized news recommendation systems.Traditional methods are usually difficult to learn and acquire complex semantic information in news text...Accurately recommending candidate news to users is a basic challenge of personalized news recommendation systems.Traditional methods are usually difficult to learn and acquire complex semantic information in news texts,resulting in unsatisfactory recommendation results.Besides,these traditional methods are more friendly to active users with rich historical behaviors.However,they can not effectively solve the long tail problem of inactive users.To address these issues,this research presents a novel general framework that combines Large Language Models(LLM)and Knowledge Graphs(KG)into traditional methods.To learn the contextual information of news text,we use LLMs’powerful text understanding ability to generate news representations with rich semantic information,and then,the generated news representations are used to enhance the news encoding in traditional methods.In addition,multi-hops relationship of news entities is mined and the structural information of news is encoded using KG,thus alleviating the challenge of long-tail distribution.Experimental results demonstrate that compared with various traditional models,on evaluation indicators such as AUC,MRR,nDCG@5 and nDCG@10,the framework significantly improves the recommendation performance.The successful integration of LLM and KG in our framework has established a feasible way for achieving more accurate personalized news recommendation.Our code is available at https://github.com/Xuan-ZW/LKPNR.展开更多
The research chose the reports about the cities in Liaoning Province,Dalian,Shenyang,and Anshan as the objects of study.These reports came from the three newspapers China Daily,New York Times,and Globe and Mail.This r...The research chose the reports about the cities in Liaoning Province,Dalian,Shenyang,and Anshan as the objects of study.These reports came from the three newspapers China Daily,New York Times,and Globe and Mail.This research was based on Frame Work Theory,and used language analysis and case study as the research methods to identify the discourse and organization.The first result is that there is serious stereotyping about the image of Liaoning cities in American and Canadian news.And Chinese news did not cover entire comprehensive topics and was too late to emergency events,so they lost the chance to be an original source of news and the possibility to set the agenda,even lost the discourse power.The second result is that the differences between Chinese reports and American and Canadian reports implied culture differences,the similarities between Canadian and American reports refer to the culture intercommunity.Third,American and Canadian news used multi-framework to make the reports more reliable,but the lack of multi-dimensional reports declined the trustworthiness.展开更多
Beijing,Hanoi vow to advance traditional ties.China rolled out the red carpet on August 19 for Vietnam’s top leader To Lam,and the two socialist countries vowed to further enhance their comprehensive strategic cooper...Beijing,Hanoi vow to advance traditional ties.China rolled out the red carpet on August 19 for Vietnam’s top leader To Lam,and the two socialist countries vowed to further enhance their comprehensive strategic cooperative partnership and advance the building of a community with a shared future that carries strategic significance.展开更多
基金supported by the National Natural Science Foundation of China(No.62302540)with author Fangfang Shan.For more information,please visit their website at https://www.nsfc.gov.cn/(accessed on 31/05/2024)+3 种基金Additionally,it is also funded by the Open Foundation of Henan Key Laboratory of Cyberspace Situation Awareness(No.HNTS2022020)where Fangfang Shan is an author.Further details can be found at http://xt.hnkjt.gov.cn/data/pingtai/(accessed on 31/05/2024)supported by the Natural Science Foundation of Henan Province Youth Science Fund Project(No.232300420422)for more information,you can visit https://kjt.henan.gov.cn/2022/09-02/2599082.html(accessed on 31/05/2024).
文摘Social media has become increasingly significant in modern society,but it has also turned into a breeding ground for the propagation of misleading information,potentially causing a detrimental impact on public opinion and daily life.Compared to pure text content,multmodal content significantly increases the visibility and share ability of posts.This has made the search for efficient modality representations and cross-modal information interaction methods a key focus in the field of multimodal fake news detection.To effectively address the critical challenge of accurately detecting fake news on social media,this paper proposes a fake news detection model based on crossmodal message aggregation and a gated fusion network(MAGF).MAGF first uses BERT to extract cumulative textual feature representations and word-level features,applies Faster Region-based ConvolutionalNeuralNetwork(Faster R-CNN)to obtain image objects,and leverages ResNet-50 and Visual Geometry Group-19(VGG-19)to obtain image region features and global features.The image region features and word-level text features are then projected into a low-dimensional space to calculate a text-image affinity matrix for cross-modal message aggregation.The gated fusion network combines text and image region features to obtain adaptively aggregated features.The interaction matrix is derived through an attention mechanism and further integrated with global image features using a co-attention mechanism to producemultimodal representations.Finally,these fused features are fed into a classifier for news categorization.Experiments were conducted on two public datasets,Twitter and Weibo.Results show that the proposed model achieves accuracy rates of 91.8%and 88.7%on the two datasets,respectively,significantly outperforming traditional unimodal and existing multimodal models.
基金This research was funded by the General Project of Philosophy and Social Science of Heilongjiang Province,Grant Number:20SHB080.
文摘In recent years,how to efficiently and accurately identify multi-model fake news has become more challenging.First,multi-model data provides more evidence but not all are equally important.Secondly,social structure information has proven to be effective in fake news detection and how to combine it while reducing the noise information is critical.Unfortunately,existing approaches fail to handle these problems.This paper proposes a multi-model fake news detection framework based on Tex-modal Dominance and fusing Multiple Multi-model Cues(TD-MMC),which utilizes three valuable multi-model clues:text-model importance,text-image complementary,and text-image inconsistency.TD-MMC is dominated by textural content and assisted by image information while using social network information to enhance text representation.To reduce the irrelevant social structure’s information interference,we use a unidirectional cross-modal attention mechanism to selectively learn the social structure’s features.A cross-modal attention mechanism is adopted to obtain text-image cross-modal features while retaining textual features to reduce the loss of important information.In addition,TD-MMC employs a new multi-model loss to improve the model’s generalization ability.Extensive experiments have been conducted on two public real-world English and Chinese datasets,and the results show that our proposed model outperforms the state-of-the-art methods on classification evaluation metrics.
基金supported by the Department of Education,Hubei Province(Grant No.22Q009).
文摘“The Era of Foreign Newspapers”refers to the period from the emergence of the first modern newspaper in Hankow in 1866 to 1900 when Wuhan’s newspaper industry was dominated by foreign newspapers.The well-known foreign newspapers in Wuhan during this period mainly included Hankow Times,The New Edition of Tan Dao,and Han Bao.The subjective purpose of foreigners’early endeavors of running newspapers in Wuhan was mainly to use newspapers to convey business information,spread religion,or influence public opinion in order to safeguard their own interests in China.However,foreign newspapers in this period played a constructive role in the development of Wuhan’s local society:It gave birth to the emergence and development of the first private and official newspapers in Wuhan and shaped the local social,cultural,and political changes in Wuhan in the late Qing Dynasty.Sorting out and explaining the constructive influence of Hankow’s foreign newspaper in this period has certain significance for restoring the social and political landscape of Wuhan at that time and better understanding the context of historical development.
基金the National Natural Science Foundation of China(No.62302540)with author F.F.S.For more information,please visit their website at https://www.nsfc.gov.cn/.Additionally,it is also funded by the Open Foundation of Henan Key Laboratory of Cyberspace Situation Awareness(No.HNTS2022020)+1 种基金where F.F.S is an author.Further details can be found at http://xt.hnkjt.gov.cn/data/pingtai/.The research is also supported by the Natural Science Foundation of Henan Province Youth Science Fund Project(No.232300420422)for more information,you can visit https://kjt.henan.gov.cn/2022/09-02/2599082.html.Lastly,it receives funding from the Natural Science Foundation of Zhongyuan University of Technology(No.K2023QN018),where F.F.S is an author.You can find more information at https://www.zut.edu.cn/.
文摘As social networks become increasingly complex, contemporary fake news often includes textual descriptionsof events accompanied by corresponding images or videos. Fake news in multiple modalities is more likely tocreate a misleading perception among users. While early research primarily focused on text-based features forfake news detection mechanisms, there has been relatively limited exploration of learning shared representationsin multimodal (text and visual) contexts. To address these limitations, this paper introduces a multimodal modelfor detecting fake news, which relies on similarity reasoning and adversarial networks. The model employsBidirectional Encoder Representation from Transformers (BERT) and Text Convolutional Neural Network (Text-CNN) for extracting textual features while utilizing the pre-trained Visual Geometry Group 19-layer (VGG-19) toextract visual features. Subsequently, the model establishes similarity representations between the textual featuresextracted by Text-CNN and visual features through similarity learning and reasoning. Finally, these features arefused to enhance the accuracy of fake news detection, and adversarial networks have been employed to investigatethe relationship between fake news and events. This paper validates the proposed model using publicly availablemultimodal datasets from Weibo and Twitter. Experimental results demonstrate that our proposed approachachieves superior performance on Twitter, with an accuracy of 86%, surpassing traditional unimodalmodalmodelsand existing multimodal models. In contrast, the overall better performance of our model on the Weibo datasetsurpasses the benchmark models across multiple metrics. The application of similarity reasoning and adversarialnetworks in multimodal fake news detection significantly enhances detection effectiveness in this paper. However,current research is limited to the fusion of only text and image modalities. Future research directions should aimto further integrate features fromadditionalmodalities to comprehensively represent themultifaceted informationof fake news.
基金This research was funded by Beijing Municipal Social Science Foundation(23YTB031)the Fundamental Research Funds for the Central Universities(CUC23ZDTJ005).
文摘Users’interests are often diverse and multi-grained,with their underlying intents even more so.Effectively captur-ing users’interests and uncovering the relationships between diverse interests are key to news recommendation.Meanwhile,diversity is an important metric for evaluating news recommendation algorithms,as users tend to reject excessive homogeneous information in their recommendation lists.However,recommendation models themselves lack diversity awareness,making it challenging to achieve a good balance between the accuracy and diversity of news recommendations.In this paper,we propose a news recommendation algorithm that achieves good performance in both accuracy and diversity.Unlike most existing works that solely optimize accuracy or employ more features to meet diversity,the proposed algorithm leverages the diversity-aware capability of the model.First,we introduce an augmented user model to fully capture user intent and the behavioral guidance they might undergo as a result.Specifically,we focus on the relationship between the original clicked news and the augmented clicked news.Moreover,we propose an effective adversarial training method for diversity(AT4D),which is a pluggable component that can enhance both the accuracy and diversity of news recommendation results.Extensive experiments on real-world datasets confirm the efficacy of the proposed algorithm in improving both the accuracy and diversity of news recommendations.
基金funded by“the Fundamental Research Funds for the Central Universities”,No.CUC23ZDTJ005.
文摘News media profiling is helpful in preventing the spread of fake news at the source and maintaining a good media and news ecosystem.Most previous works only extract features and evaluate media from one dimension independently,ignoring the interconnections between different aspects.This paper proposes a novel news media bias and factuality profiling framework assisted by correlated features.This framework models the relationship and interaction between media bias and factuality,utilizing this relationship to assist in the prediction of profiling results.Our approach extracts features independently while aligning and fusing them through recursive convolu-tion and attention mechanisms,thus harnessing multi-scale interactive information across different dimensions and levels.This method improves the effectiveness of news media evaluation.Experimental results indicate that our proposed framework significantly outperforms existing methods,achieving the best performance in Accuracy and F1 score,improving by at least 1%compared to other methods.This paper further analyzes and discusses based on the experimental results.
基金The authors are highly thankful to the National Social Science Foundation of China(20BXW101,18XXW015)Innovation Research Project for the Cultivation of High-Level Scientific and Technological Talents(Top-Notch Talents of theDiscipline)(ZZKY2022303)+3 种基金National Natural Science Foundation of China(Nos.62102451,62202496)Basic Frontier Innovation Project of Engineering University of People’s Armed Police(WJX202316)This work is also supported by National Natural Science Foundation of China(No.62172436)Engineering University of PAP’s Funding for Scientific Research Innovation Team,Engineering University of PAP’s Funding for Basic Scientific Research,and Engineering University of PAP’s Funding for Education and Teaching.Natural Science Foundation of Shaanxi Province(No.2023-JCYB-584).
文摘With the rapid spread of Internet information and the spread of fake news,the detection of fake news becomes more and more important.Traditional detection methods often rely on a single emotional or semantic feature to identify fake news,but these methods have limitations when dealing with news in specific domains.In order to solve the problem of weak feature correlation between data from different domains,a model for detecting fake news by integrating domain-specific emotional and semantic features is proposed.This method makes full use of the attention mechanism,grasps the correlation between different features,and effectively improves the effect of feature fusion.The algorithm first extracts the semantic features of news text through the Bi-LSTM(Bidirectional Long Short-Term Memory)layer to capture the contextual relevance of news text.Senta-BiLSTM is then used to extract emotional features and predict the probability of positive and negative emotions in the text.It then uses domain features as an enhancement feature and attention mechanism to fully capture more fine-grained emotional features associated with that domain.Finally,the fusion features are taken as the input of the fake news detection classifier,combined with the multi-task representation of information,and the MLP and Softmax functions are used for classification.The experimental results show that on the Chinese dataset Weibo21,the F1 value of this model is 0.958,4.9% higher than that of the sub-optimal model;on the English dataset FakeNewsNet,the F1 value of the detection result of this model is 0.845,1.8% higher than that of the sub-optimal model,which is advanced and feasible.
文摘Purpose: Communicating scientific results to the public is essential to inspire future researchers and ensure that discoveries are exploited. News stories about research are a key communication pathway for this and have been manually monitored to assess the extent of press coverage of scholarship.Design/methodology/Approach: To make larger scale studies practical, this paper introduces an automatic method to extract citations from newspaper stories to large sets of academic journals. Curated ProQuest queries were used to search for citations to 9,639 Science and3,412 Social Science Web of Science(WoS) journals from eight UK daily newspapers during2006–2015. False matches were automatically filtered out by a new program, with 94% of the remaining stories meaningfully citing research.Findings: Most Science(95%) and Social Science(94%) journals were never cited by these newspapers. Half of the cited Science journals covered medical or health-related topics,whereas 43% of the Social Sciences journals were related to psychiatry or psychology. From the citing news stories, 60% described research extensively and 53% used multiple sources,but few commented on research quality.Research Limitations: The method has only been tested in English and from the ProQuest Newspapers database.Practical implications: Others can use the new method to systematically harvest press coverage of research.Originality/value: An automatic method was introduced and tested to extract citations from newspaper stories to large sets of academic journals.
文摘近日,广州医科大学第一附属医院院长兼胸外科主任、AME旗下Journal of Thoracic Disease(JTD)杂志执行主编何建行教授接受了Lancet Oncology杂志的特邀专访(L a n c e t O n c o l 2 0 1 4.s 1 4 7 0-2045(14)71024-1),针对10月份发表在BM J杂志的关于开胸手术对比胸腔镜手术治疗肺癌的匹配分析(Paul S,et al.BMJ 2014)进行了点评(图1)。被点评的研究,使用了美国最权威的SEER数据库,纳入了6 008例患者,经过预后因素的匹配后。
基金supported by National Key R&D Program of China(2022QY2000-02).
文摘Accurately recommending candidate news to users is a basic challenge of personalized news recommendation systems.Traditional methods are usually difficult to learn and acquire complex semantic information in news texts,resulting in unsatisfactory recommendation results.Besides,these traditional methods are more friendly to active users with rich historical behaviors.However,they can not effectively solve the long tail problem of inactive users.To address these issues,this research presents a novel general framework that combines Large Language Models(LLM)and Knowledge Graphs(KG)into traditional methods.To learn the contextual information of news text,we use LLMs’powerful text understanding ability to generate news representations with rich semantic information,and then,the generated news representations are used to enhance the news encoding in traditional methods.In addition,multi-hops relationship of news entities is mined and the structural information of news is encoded using KG,thus alleviating the challenge of long-tail distribution.Experimental results demonstrate that compared with various traditional models,on evaluation indicators such as AUC,MRR,nDCG@5 and nDCG@10,the framework significantly improves the recommendation performance.The successful integration of LLM and KG in our framework has established a feasible way for achieving more accurate personalized news recommendation.Our code is available at https://github.com/Xuan-ZW/LKPNR.
文摘The research chose the reports about the cities in Liaoning Province,Dalian,Shenyang,and Anshan as the objects of study.These reports came from the three newspapers China Daily,New York Times,and Globe and Mail.This research was based on Frame Work Theory,and used language analysis and case study as the research methods to identify the discourse and organization.The first result is that there is serious stereotyping about the image of Liaoning cities in American and Canadian news.And Chinese news did not cover entire comprehensive topics and was too late to emergency events,so they lost the chance to be an original source of news and the possibility to set the agenda,even lost the discourse power.The second result is that the differences between Chinese reports and American and Canadian reports implied culture differences,the similarities between Canadian and American reports refer to the culture intercommunity.Third,American and Canadian news used multi-framework to make the reports more reliable,but the lack of multi-dimensional reports declined the trustworthiness.
文摘Beijing,Hanoi vow to advance traditional ties.China rolled out the red carpet on August 19 for Vietnam’s top leader To Lam,and the two socialist countries vowed to further enhance their comprehensive strategic cooperative partnership and advance the building of a community with a shared future that carries strategic significance.
