网络新媒体视阈下客家议题在日传播特征分析——基于日本Google News的考察

以日本Google News中的客家相关日文报道为语料,通过文本挖掘工具KH Coder进行文本数据挖掘,发现日本网络新闻媒体的客家议题日文报道具有分布零散性及以图片报道为主的特点。报道关注的客家区域主要涉及台湾地区、福建、香港地区、广东等地,其中,台湾地区的客家关注度最高。报道聚焦客家的观光旅游、产业振兴、客家书籍出版等相关内容,形成了客家传统建筑类、台湾地区客家文化交流类、客家文学作品推介类、客家观光旅游类等四大主要议题。本文提出要充分运用Google News等网络新媒体强大的技术功能和传播优势加大客家文化的传播力度。

LKPNR: Large Language Models and Knowledge Graph for Personalized News Recommendation Framework

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.

Multimodal Social Media Fake News Detection Based on Similarity Inference and Adversarial Networks

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.

Fake News Detection Based on Text-Modal Dominance and Fusing Multiple Multi-Model Clues

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.

TOP 10 NEWS STORIES ON 2023 LANCANG-MEKONG COOPERATION

Linked by mountains and rivers,China and the five other Lancang-Mekong countries share cultural similarities and are as close as a big family.The year 2023 marked the 10th anniversary of the Belt and Road Initiative(BRI),the 10th anniversary of the vision of building a community with a shared future for mankind,and the 10th anniversary of the principle of amity,sincerity.

NEWS

The Chinese economy has maintained good recovery momentum,beginning the year on a solid note as the country’s macro policies took effect,official data showed on March 18.Given its solid performance in January and February,China has the conditions and support to achieve its full-year growth target of around 5 percent for 2024 through enhanced efforts,the National Bureau of Statistics(NBS)spokesperson Liu Aihua said.

On the“News Exception”in Personal Information Protection

Protection of personal information is a significant issue in the construction of legal systems in various countries in the information age.Introducing a balanced approach for protecting personal information is an important goal of basic human rights protection and data legislation.Personal information protection involves comprehensive considerations among various values,and the balanced structure between personal information rights and other rights systems has become the key to legislation on personal information protection.The“news exception”is a prominent example representing the balanced structure of personal information protection.As a societal instrument,news not only pursues commercial value but also advocates freedom of expression and public value.There exists a natural tension between news and personal information protection.The“news exception”of the balanced structure has become a fundamental requirement and important connotation for constructing a system for protecting personal information.The balanced structure of the“news exception”requires a reasonable definition of the concept and purpose of news,and both the self-discipline within the news industry and the judicial intervention are necessary factors.China has preliminarily completed the top-level legislative design of personal information protection through laws such as the Civil Code of the People’s Republic of China(PRC)and the Personal Information Protection Law of the People’s Republic of China.However,the balanced mechanism of the“news exception”has not yet been fully established in China.A“news exception”based on the ideas of balance and the improvement of the institutional system is the fundamental principle for the development of China’s personal information protection system.

NEWS ROUNDUP

Boosting Grain Output China has initiated a new round of action to significantly increase its grain output,in the latest e"ort to ensure food security.

News Roundup

SUMMARIES OF TOP NEWS STORIES Ramping Up Public Health Literacy China’s National Health Commission(NHC)announced a three-year campaign launched this month to significantly improve public health literacy.The initiative,co-organised with the National Administration of Disease Control and Prevention and the National Administration of Traditional Chinese Medicine,aims to empower citizens with essential health knowledge and practices.

基于NEWS的急诊早期预警系统的构建及临床应用研究

目的 探讨基于NEWS构建急诊早期预警系统的实践与效果。方法 通过文献研究及Delphi法制定基于NEWS的急诊患者分级预警及干预方案,并构建急诊早期预警信息系统进行临床应用。采用类实验研究方法,选取南京市某三级甲等综合性医院急诊室2020年3月至5月符合纳入标准的所有急诊留观及抢救患者1 131例为干预组,选取运行前1年同期即2019年3月至5月符合纳入标准的所有急诊留观及抢救患者1 005例为对照组,两组患者均按照分级护理制度要求及急诊专科护理常规进行护理,干预组在此基础上应用急诊早期预警系统进行干预。结果 干预组生命体征测量频率、抢救成功率显著高于对照组,护理不良事件发生率显著低于对照组,急诊医护人员安全态度的6个不同维度均有改善,差异有统计学意义(P<0.05)。结论 急诊早期预警系统提高了医务人员的安全态度,减少了护理不良事件的发生,提高了急诊患者的生命体征测量频次和抢救成功率,保障了患者预后及安全。

Fake News Detection Based on Multimodal Inputs

In view of the various adverse effects,fake news detection has become an extremely important task.So far,many detection methods have been proposed,but these methods still have some limitations.For example,only two independently encoded unimodal information are concatenated together,but not integrated with multimodal information to complete the complementary information,and to obtain the correlated information in the news content.This simple fusion approach may lead to the omission of some information and bring some interference to the model.To solve the above problems,this paper proposes the FakeNewsDetectionmodel based on BLIP(FNDB).First,the XLNet and VGG-19 based feature extractors are used to extract textual and visual feature representation respectively,and BLIP based multimodal feature extractor to obtain multimodal feature representation in news content.Then,the feature fusion layer will fuse these features with the help of the cross-modal attention module to promote various modal feature representations for information complementation.The fake news detector uses these fused features to identify the input content,and finally complete fake news detection.Based on this design,FNDB can extract as much information as possible from the news content and fuse the information between multiple modalities effectively.The fake news detector in the FNDB can also learn more information to achieve better performance.The verification experiments on Weibo and Gossipcop,two widely used real-world datasets,show that FNDB is 4.4%and 0.6%higher in accuracy than the state-of-theart fake news detection methods,respectively.

Hunter Prey Optimization with Hybrid Deep Learning for Fake News Detection on Arabic Corpus

Nowadays,the usage of socialmedia platforms is rapidly increasing,and rumours or false information are also rising,especially among Arab nations.This false information is harmful to society and individuals.Blocking and detecting the spread of fake news in Arabic becomes critical.Several artificial intelligence(AI)methods,including contemporary transformer techniques,BERT,were used to detect fake news.Thus,fake news in Arabic is identified by utilizing AI approaches.This article develops a new hunterprey optimization with hybrid deep learning-based fake news detection(HPOHDL-FND)model on the Arabic corpus.The HPOHDL-FND technique undergoes extensive data pre-processing steps to transform the input data into a useful format.Besides,the HPOHDL-FND technique utilizes long-term memory with a recurrent neural network(LSTM-RNN)model for fake news detection and classification.Finally,hunter prey optimization(HPO)algorithm is exploited for optimal modification of the hyperparameters related to the LSTM-RNN model.The performance validation of the HPOHDL-FND technique is tested using two Arabic datasets.The outcomes exemplified better performance over the other existing techniques with maximum accuracy of 96.57%and 93.53%on Covid19Fakes and satirical datasets,respectively.

简版NEWS在识别急诊科抢救室老年病人死亡风险中的应用

目的:分析简版国家早期预警评分(NEWS)识别急诊科抢救室老年病人入院30 d内死亡风险的效能。方法:选择2019年1月—2020年12月深圳市某三级甲等医院急诊科抢救室收治的1184例老年病人作为研究对象。收集急诊科抢救室收治的老年病人入急诊科抢救室时的体温、心率、呼吸频率、血压、意识状态、血氧饱和度数据及入急诊科抢救室的主要诊断、入院30 d内的预后结局,通过模型区分度、校准度分析简版NEWS在识别急诊科抢救室老年人入院30 d内死亡的效能。结果:简版NEWS、NEWS及改良早期预警评分(MEWS)预测急诊科抢救室老年人入院30 d内死亡的受试者工作特征曲线下面积(AUC)分别为0.847,0.842及0.793,Brier评分分别为0.0450,0.0455及0.0478。当简版NEWS得分>3.5分时,病人30 d内死亡风险为13.7%;当简版NEWS得分<3.5分,病人30 d内死亡风险为1.5%。结论:简版NEWS的操作更简捷,在急诊科抢救室老年病人入院30 d内死亡风险评估中有良好的区分度及校准度。

Build neural network models to identify and correct news headlines exaggerating obesity-related scientific findings

Purpose:Media exaggerations of health research may confuse readers’understanding,erode public trust in science and medicine,and cause disease mismanagement.This study built artificial intelligence(AI)models to automatically identify and correct news headlines exaggerating obesity-related research findings.Design/methodology/approach:We searched popular digital media outlets to collect 523 headlines exaggerating obesity-related research findings.The reasons for exaggerations include:inferring causality from observational studies,inferring human outcomes from animal research,inferring distant/end outcomes(e.g.,obesity)from immediate/intermediate outcomes(e.g.,calorie intake),and generalizing findings to the population from a subgroup or convenience sample.Each headline was paired with the title and abstract of the peer-reviewed journal publication covered by the news article.We drafted an exaggeration-free counterpart for each original headline and fined-tuned a BERT model to differentiate between them.We further fine-tuned three generative language models-BART,PEGASUS,and T5 to autogenerate exaggeration-free headlines based on a journal publication’s title and abstract.Model performance was evaluated using the ROUGE metrics by comparing model-generated headlines with journal publication titles.Findings:The fine-tuned BERT model achieved 92.5%accuracy in differentiating between exaggeration-free and original headlines.Baseline ROUGE scores averaged 0.311 for ROUGE-1,0.113 for ROUGE-2,0.253 for ROUGE-L,and 0.253 ROUGE-Lsum.PEGASUS,T5,and BART all outperformed the baseline.The best-performing BART model attained 0.447 for ROUGE-1,0.221 for ROUGE-2,0.402 for ROUGE-L,and 0.402 for ROUGE-Lsum.Originality/value:This study demonstrated the feasibility of leveraging AI to automatically identify and correct news headlines exaggerating obesity-related research findings.

Optimal Weighted Extreme Learning Machine for Cybersecurity Fake News Classification

Fake news and its significance carried the significance of affecting diverse aspects of diverse entities,ranging from a city lifestyle to a country global relativity,various methods are available to collect and determine fake news.The recently developed machine learning(ML)models can be employed for the detection and classification of fake news.This study designs a novel Chaotic Ant Swarm with Weighted Extreme Learning Machine(CAS-WELM)for Cybersecurity Fake News Detection and Classification.The goal of the CAS-WELM technique is to discriminate news into fake and real.The CAS-WELM technique initially pre-processes the input data and Glove technique is used for word embed-ding process.Then,N-gram based feature extraction technique is derived to gen-erate feature vectors.Lastly,WELM model is applied for the detection and classification of fake news,in which the weight value of the WELM model can be optimally adjusted by the use of CAS algorithm.The performance validation of the CAS-WELM technique is carried out using the benchmark dataset and the results are inspected under several dimensions.The experimental results reported the enhanced outcomes of the CAS-WELM technique over the recent approaches.

Fake News Detection Using Machine Learning and Deep Learning Methods

The evolution of the internet and its accessibility in the twenty-first century has resulted in a tremendous increase in the use of social media platforms.Some social media sources contribute to the propagation of fake news that has no real validity,but they accumulate over time and begin to appear in the feed of every consumer producing even more ambiguity.To sustain the value of social media,such stories must be distinguished from the true ones.As a result,an automated system is required to save time and money.The classification of fake news and misinformation from social media data corpora is the subject of this research.Several preprocessing and data improvement procedures are used to gather and preprocess two fake news datasets.Deep text features are extracted using word embedding models Word2vec and Global Vectors for Word representation while textual features are extracted using n-gram approaches named Term Frequency-Inverse Document Frequency and Bag of Words from both datasets individually.Bidirectional Encoder Representations from Transformers(BERT)is also employed to derive embedded representations from the input data.Finally,three Machine Learning(ML)and two Deep Learning(DL)algorithms are utilized for fake news classification.BERT also carries out the classification of embedded outcomes generated by it in parallel with the ML and DL models.In terms of overall performance,the DL-based Convolutional Neural Network stands out in the case of the first while BERT performs better in the case of the second dataset.

SNG-TE: Sports News Generation with Text-Editing Model

Currently,the amount of sports news is increasing,given the number of sports available.As a result,manually writing sports news requires high labor costs to achieve the intended efficiency.Therefore,it is necessary to develop the automatic generation of sports news.Most available news gen-eration methods mainly rely on real-time commentary sentences,which have the following limitations:(1)unable to select suitable commentary sentences for news generation,and(2)the generated sports news could not accurately describe game events.Therefore,this study proposes a sports news generation with text-editing model(SNG-TE)is proposed to generate sports news,which includes selector and rewriter modules.Within the study context,a weight adjustment mechanism in the selector module is designed to improve the hit rate of important sentences.Furthermore,the text-editing model is introduced in the rewriter module to ensure that the generated news sentences can cor-rectly describe the game events.The annotation and generation experiments are designed to evaluate the developed model.The study results have shown that in the annotation experiment,the accuracy of the sentence annotated by the selector increased by about 8%compared with other methods.Moreover,in the generation experiment,the sports news generated by the rewriter achieved a 49.66 ROUGE-1 score and 21.47 ROUGE-2,both of which are better than the available models.Additionally,the proposed model saved about 15 times the consumption of time.Hence,the proposed model provides better performance in both accuracy and efficiency,which is very suitable for the automatic generation of sports news.

FedNRM:A Federal Personalized News Recommendation Model Achieving User Privacy Protection

In recent years,the type and quantity of news are growing rapidly,and it is not easy for users to find the news they are interested in the massive amount of news.A news recommendation system can score and predict the candidate news,and finally recommend the news with high scores to users.However,existing user models usually only consider users’long-term interests and ignore users’recent interests,which affects users’usage experience.Therefore,this paper introduces gated recurrent unit(GRU)sequence network to capture users’short-term interests and combines users’short-term interests and long-terminterests to characterize users.While existing models often only use the user’s browsing history and ignore the variability of different users’interest in the same news,we introduce additional user’s ID information and apply the personalized attention mechanism for user representation.Thus,we achieve a more accurate user representation.We also consider the risk of compromising user privacy if the user model training is placed on the server side.To solve this problem,we design the training of the user model locally on the client side by introducing a federated learning framework to keep the user’s browsing history on the client side.We further employ secure multiparty computation to request news representations from the server side,which protects privacy to some extent.Extensive experiments on a real-world news dataset show that our proposed news recommendation model has a better improvement in several performance evaluation metrics.Compared with the current state-of-the-art federated news recommendation models,our model has increased by 0.54%in AUC,1.97%in MRR,2.59%in nDCG@5%,and 1.89%in nDCG@10.At the same time,because we use a federated learning framework,compared with other centralized news recommendation methods,we achieve privacy protection for users.

Fake News Encoder Classifier (FNEC) for Online Published News Related to COVID-19 Vaccines

In the past few years,social media and online news platforms have played an essential role in distributing news content rapidly.Consequently.verification of the authenticity of news has become a major challenge.During the COVID-19 outbreak,misinformation and fake news were major sources of confusion and insecurity among the general public.In the first quarter of the year 2020,around 800 people died due to fake news relevant to COVID-19.The major goal of this research was to discover the best learning model for achieving high accuracy and performance.A novel case study of the Fake News Classification using ELECTRA model,which achieved 85.11%accuracy score,is thus reported in this manuscript.In addition to that,a new novel dataset called COVAX-Reality containing COVID-19 vaccine-related news has been contributed.Using the COVAX-Reality dataset,the performance of FNEC is compared to several traditional learning models i.e.,Support Vector Machine(SVM),Naive Bayes(NB),Passive Aggressive Classifier(PAC),Long Short-Term Memory(LSTM),Bi-directional LSTM(Bi-LSTM)and Bi-directional Encoder Representations from Transformers(BERT).For the evaluation of FNEC,standard metrics(Precision,Recall,Accuracy,and F1-Score)were utilized.

我国学科建设从“高原”到“高峰”发展探究——基于US News全球大学学科排名及相关数据分析

高峰学科建设是我国一流学科建设的重要任务,文章基于2023 US News全球大学学科排名,使用2017—2022年的排名数据,运用对比分析法、历史分析法,考察高原学科和高峰学科的关系。研究发现,“双一流”建设以来,高原学科和高峰学科迅速发展,高原学科广而弱、高峰学科精而强,但仍存在发展不均衡、国际合作较少等问题。因此,在第二轮“双一流”建设中,高原学科建设应聚焦前沿,发展新兴交叉学科;加强国际合作,提升国际影响力;整合各种资源,建立跨学科平台;以此打造高峰学科,带动学校学科建设整体水平的提升。