Exploring the interaction between the gut microbiota and cyclic adenosine monophosphate-protein kinase A signaling pathway:a potential therapeutic approach for neurodegenerative diseases

The interaction between the gut microbiota and cyclic adenosine monophosphate(cAMP)-protein kinase A(PKA)signaling pathway in the host's central nervous system plays a crucial role in neurological diseases and enhances communication along the gut–brain axis.The gut microbiota influences the cAMP-PKA signaling pathway through its metabolites,which activates the vagus nerve and modulates the immune and neuroendocrine systems.Conversely,alterations in the cAMP-PKA signaling pathway can affect the composition of the gut microbiota,creating a dynamic network of microbial-host interactions.This reciprocal regulation affects neurodevelopment,neurotransmitter control,and behavioral traits,thus playing a role in the modulation of neurological diseases.The coordinated activity of the gut microbiota and the cAMP-PKA signaling pathway regulates processes such as amyloid-β protein aggregation,mitochondrial dysfunction,abnormal energy metabolism,microglial activation,oxidative stress,and neurotransmitter release,which collectively influence the onset and progression of neurological diseases.This study explores the complex interplay between the gut microbiota and cAMP-PKA signaling pathway,along with its implications for potential therapeutic interventions in neurological diseases.Recent pharmacological research has shown that restoring the balance between gut flora and cAMP-PKA signaling pathway may improve outcomes in neurodegenerative diseases and emotional disorders.This can be achieved through various methods such as dietary modifications,probiotic supplements,Chinese herbal extracts,combinations of Chinese herbs,and innovative dosage forms.These findings suggest that regulating the gut microbiota and cAMP-PKA signaling pathway may provide valuable evidence for developing novel therapeutic approaches for neurodegenerative diseases.

Enhancing perianal disease management with integrated physical and psychological approaches

This article provides a comprehensive analysis of the study by Hou et al,focusing on the complex interplay between psychological and physical factors in the postoperative recovery(POR)of patients with perianal diseases.The study sheds light on how illness perception,anxiety,and depression significantly influence recovery outcomes.Hou et al developed a predictive model that demonstrated high accuracy in identifying patients at risk of poor recovery.The article explores the critical role of pre-operative psychological assessment,highlighting the need for mental health support and personalized recovery plans in enhancing POR quality.A multidisciplinary approach,integrating mental health professionals with surgeons,anesthesiologists,and other specialists,is emphasized to ensure comprehensive care for patients.The study’s findings serve as a call to integrate psychological care into surgical practice to optimize outcomes for patients with perianal diseases.

Impact of propofol and sevoflurane anesthesia on cognition and emotion in gastric cancer patients undergoing radical resection

BACKGROUND Propofol and sevoflurane are commonly used anesthetic agents for maintenance anesthesia during radical resection of gastric cancer.However,there is a debate concerning their differential effects on cognitive function,anxiety,and depression in patients undergoing this procedure.AIM To compare the effects of propofol and sevoflurane anesthesia on postoperative cognitive function,anxiety,depression,and organ function in patients undergoing radical resection of gastric cancer.METHODS A total of 80 patients were involved in this research.The subjects were divided into two groups:Propofol group and sevoflurane group.The evaluation scale for cognitive function was the Loewenstein occupational therapy cognitive assessment(LOTCA),and anxiety and depression were assessed with the aid of the self-rating anxiety scale(SAS)and self-rating depression scale(SDS).Hemodynamic indicators,oxidative stress levels,and pulmonary function were also measured.RESULTS The LOTCA score at 1 d after surgery was significantly lower in the propofol group than in the sevoflurane group.Additionally,the SAS and SDS scores of the sevoflurane group were significantly lower than those of the propofol group.The sevoflurane group showed greater stability in heart rate as well as the mean arterial pressure compared to the propofol group.Moreover,the sevoflurane group displayed better pulmonary function and less lung injury than the propofol group.CONCLUSION Both propofol and sevoflurane could be utilized as maintenance anesthesia during radical resection of gastric cancer.Propofol anesthesia has a minimal effect on patients'pulmonary function,consequently enhancing their postoperative recovery.Sevoflurane anesthesia causes less impairment on patients'cognitive function and mitigates negative emotions,leading to an improved postoperative mental state.Therefore,the selection of anesthetic agents should be based on the individual patient's specific circumstances.

Adolescent suicide risk factors and the integration of socialemotional skills in school-based prevention programs

Adolescents are considered one of the most vulnerable groups affected by suicide.Rapid changes in adolescents’physical and mental states,as well as in their lives,significantly and undeniably increase the risk of suicide.Psychological,social,family,individual,and environmental factors are important risk factors for suicidal behavior among teenagers and may contribute to suicide risk through various direct,indirect,or combined pathways.Social-emotional learning is considered a powerful intervention measure for addressing the crisis of adolescent suicide.When deliberately cultivated,fostered,and enhanced,selfawareness,self-management,social awareness,interpersonal skills,and responsible decision-making,as the five core competencies of social-emotional learning,can be used to effectively target various risk factors for adolescent suicide and provide necessary mental and interpersonal support.Among numerous suicide intervention methods,school-based interventions based on social-emotional competence have shown great potential in preventing and addressing suicide risk factors in adolescents.The characteristics of school-based interventions based on social-emotional competence,including their appropriateness,necessity,cost-effectiveness,comprehensiveness,and effectiveness,make these interventions an important means of addressing the crisis of adolescent suicide.To further determine the potential of school-based interventions based on social-emotional competence and better address the issue of adolescent suicide,additional financial support should be provided,the combination of socialemotional learning and other suicide prevention programs within schools should be fully leveraged,and cooperation between schools and families,society,and other environments should be maximized.These efforts should be considered future research directions.

Impact of parenting styles on preschoolers’behaviors

In this editorial,I comment on the article“Association of preschool children behavior and emotional problems with the parenting behavior of both parents”which was published in the latest issue of“World Journal of Clinical Cases”that demonstrates the prevalence of behavioral disorders in preschool children.Therefore I am focused on parenting which is the most effective factor shown to affect the development and continuity of these behaviors.The management of child behavior problems is crucial.Children in early ages,especially preschoolers who are in the first 5 years of life,are influenced by dramatic changes in various aspects of development,such as social,emotional,and physical.Also,children experience many changes linked to different developmental tasks,such as discovering themselves,getting new friendships,and adapting to a new environment.In this period,parents have a critical role in supporting child development.If parents do not manage and overcome their child’s misbehavior,it could be transformed into psychosocial problems in adulthood.Parenting is the most powerful predictor in the social development of preschool children.Several studies have shown that to reduce the child’s emotional and behavioral problems,a warm relationship between parents and children is needed.In addition,recent studies have demonstrated significant relationships between family regulation factors and parenting,as well as with child behaviors.

High-risk factors for delirium in severely ill patients and the application of emotional nursing combined with pain nursing

BACKGROUND Delirium is a neuropsychiatric syndrome characterized by acute disturbances of consciousness with rapid onset,rapid progression,obvious fluctuations,and preventable,reversible,and other characteristics.Patients with delirium in the intensive care unit(ICU)are often missed or misdiagnosed and do not receive adequate attention.AIM To analyze the risk factors for delirium in ICU patients and explore the applica-tion of emotional nursing with pain nursing in the management of delirium.METHODS General data of 301 critically ill patients were retrospectively collected,including histories(cardiovascular and cerebrovascular diseases,hypertension,smoking,alcoholism,and diabetes),age,sex,diagnosis,whether surgery was performed,and patient origin(emergency/clinic).Additionally,the duration of sedation,Richmond Agitation Sedation Scale score,combined emotional and pain care,ven-tilator use duration,vasoactive drug use,drainage tube retention,ICU stay du-ration,C-reactive protein,procalcitonin,white blood cell count,body tempe-rature,Acute Physiology and Chronic Health Evaluation II(APACHE II)score,and Sequential Organ Failure Assessment score were recorded within 24 h after ICU admission.Patients were assessed for delirium according to confusion assessment method for the ICU,and univariate and multivariate logistic regre-ssion analyses were performed to identify the risk factors for delirium in the patients.RESULTS Univariate logistic regression analysis was performed on the 24 potential risk factors associated with delirium in ICU patients.The results showed that 16 risk factors were closely related to delirium,including combined emotional and pain care,history of diabetes,and patient origin.Multivariate logistic regression analysis revealed that no combined emotional and pain care,history of diabetes,emergency source,surgery,long stay in the ICU,smoking history,and high APACHE II score were independent risk factors for de-lirium in ICU patients.CONCLUSION Patients with diabetes and/or smoking history,postoperative patients,patients with a high APACHE II score,and those with emergency ICU admission need emotional and pain care,flexible visiting modes,and early intervention to reduce delirium incidence.

Faster Region Convolutional Neural Network(FRCNN)Based Facial Emotion Recognition

Facial emotion recognition(FER)has become a focal point of research due to its widespread applications,ranging from human-computer interaction to affective computing.While traditional FER techniques have relied on handcrafted features and classification models trained on image or video datasets,recent strides in artificial intelligence and deep learning(DL)have ushered in more sophisticated approaches.The research aims to develop a FER system using a Faster Region Convolutional Neural Network(FRCNN)and design a specialized FRCNN architecture tailored for facial emotion recognition,leveraging its ability to capture spatial hierarchies within localized regions of facial features.The proposed work enhances the accuracy and efficiency of facial emotion recognition.The proposed work comprises twomajor key components:Inception V3-based feature extraction and FRCNN-based emotion categorization.Extensive experimentation on Kaggle datasets validates the effectiveness of the proposed strategy,showcasing the FRCNN approach’s resilience and accuracy in identifying and categorizing facial expressions.The model’s overall performance metrics are compelling,with an accuracy of 98.4%,precision of 97.2%,and recall of 96.31%.This work introduces a perceptive deep learning-based FER method,contributing to the evolving landscape of emotion recognition technologies.The high accuracy and resilience demonstrated by the FRCNN approach underscore its potential for real-world applications.This research advances the field of FER and presents a compelling case for the practicality and efficacy of deep learning models in automating the understanding of facial emotions.

Facial Expression Recognition with High Response-Based Local Directional Pattern (HR-LDP) Network

Although lots of research has been done in recognizing facial expressions,there is still a need to increase the accuracy of facial expression recognition,particularly under uncontrolled situations.The use of Local Directional Patterns(LDP),which has good characteristics for emotion detection has yielded encouraging results.An innova-tive end-to-end learnable High Response-based Local Directional Pattern(HR-LDP)network for facial emotion recognition is implemented by employing fixed convolutional filters in the proposed work.By combining learnable convolutional layers with fixed-parameter HR-LDP layers made up of eight Kirsch filters and derivable simulated gate functions,this network considerably minimizes the number of network parameters.The cost of the parameters in our fully linked layers is up to 64 times lesser than those in currently used deep learning-based detection algorithms.On seven well-known databases,including JAFFE,CK+,MMI,SFEW,OULU-CASIA and MUG,the recognition rates for seven-class facial expression recognition are 99.36%,99.2%,97.8%,60.4%,91.1%and 90.1%,respectively.The results demonstrate the advantage of the proposed work over cutting-edge techniques.

Multi-Objective Equilibrium Optimizer for Feature Selection in High-Dimensional English Speech Emotion Recognition

Speech emotion recognition(SER)uses acoustic analysis to find features for emotion recognition and examines variations in voice that are caused by emotions.The number of features acquired with acoustic analysis is extremely high,so we introduce a hybrid filter-wrapper feature selection algorithm based on an improved equilibrium optimizer for constructing an emotion recognition system.The proposed algorithm implements multi-objective emotion recognition with the minimum number of selected features and maximum accuracy.First,we use the information gain and Fisher Score to sort the features extracted from signals.Then,we employ a multi-objective ranking method to evaluate these features and assign different importance to them.Features with high rankings have a large probability of being selected.Finally,we propose a repair strategy to address the problem of duplicate solutions in multi-objective feature selection,which can improve the diversity of solutions and avoid falling into local traps.Using random forest and K-nearest neighbor classifiers,four English speech emotion datasets are employed to test the proposed algorithm(MBEO)as well as other multi-objective emotion identification techniques.The results illustrate that it performs well in inverted generational distance,hypervolume,Pareto solutions,and execution time,and MBEO is appropriate for high-dimensional English SER.

Depressive semantic awareness from vlog facial and vocal streams via spatio-temporal transformer

With the rapid growth of information transmission via the Internet,efforts have been made to reduce network load to promote efficiency.One such application is semantic computing,which can extract and process semantic communication.Social media has enabled users to share their current emotions,opinions,and life events through their mobile devices.Notably,people suffering from mental health problems are more willing to share their feelings on social networks.Therefore,it is necessary to extract semantic information from social media(vlog data)to identify abnormal emotional states to facilitate early identification and intervention.Most studies do not consider spatio-temporal information when fusing multimodal information to identify abnormal emotional states such as depression.To solve this problem,this paper proposes a spatio-temporal squeeze transformer method for the extraction of semantic features of depression.First,a module with spatio-temporal data is embedded into the transformer encoder,which is utilized to obtain a representation of spatio-temporal features.Second,a classifier with a voting mechanism is designed to encourage the model to classify depression and non-depression effec-tively.Experiments are conducted on the D-Vlog dataset.The results show that the method is effective,and the accuracy rate can reach 70.70%.This work provides scaffolding for future work in the detection of affect recognition in semantic communication based on social media vlog data.

Developing Lexicons for Enhanced Sentiment Analysis in Software Engineering:An Innovative Multilingual Approach for Social Media Reviews

Sentiment analysis is becoming increasingly important in today’s digital age, with social media being a significantsource of user-generated content. The development of sentiment lexicons that can support languages other thanEnglish is a challenging task, especially for analyzing sentiment analysis in social media reviews. Most existingsentiment analysis systems focus on English, leaving a significant research gap in other languages due to limitedresources and tools. This research aims to address this gap by building a sentiment lexicon for local languages,which is then used with a machine learning algorithm for efficient sentiment analysis. In the first step, a lexiconis developed that includes five languages: Urdu, Roman Urdu, Pashto, Roman Pashto, and English. The sentimentscores from SentiWordNet are associated with each word in the lexicon to produce an effective sentiment score. Inthe second step, a naive Bayesian algorithm is applied to the developed lexicon for efficient sentiment analysis ofRoman Pashto. Both the sentiment lexicon and sentiment analysis steps were evaluated using information retrievalmetrics, with an accuracy score of 0.89 for the sentiment lexicon and 0.83 for the sentiment analysis. The resultsshowcase the potential for improving software engineering tasks related to user feedback analysis and productdevelopment.

Detecting the distribution of and public attitudes to charismatic alien species based on media coverage

Charismatic species are often reported by the media,providing information for detecting population status and public perception.To identify the number and distribution of free-living Black Swan(Cygnus atratus),a charismatic alien species in Chinese mainland and to detect the public and the media attitudes to the species,we analyzed the reports and emotional tendency from media coverage in 2000-2022 using manual reading,crawler extraction and latent Dirichlet allocation.A total of 6654 Black Swans were reported at 711 sites,including 147 individuals at 30 nature reserves.Successful breeding was reported at one-fourth of the total sites,including five nature reserves.The proportion of positive emotional tendency to Black Swans was overwhelming in the reports and was higher than that to alien species in general,suggesting that the public and the media are unaware of the risk of biological invasion.Effective management of invasive species requires the media clarifies the invasion risk of charismatic alien species.Promoting the unity between the harmfulness of abstract concept of alien species and the charisma of a specific alien species among the public help effective management.

Smoking of Carica papaya in Nigeria:The rationale,the public health effects and policies for intervention

Psychoactive substance use is characterized by the habitual use of substances that have significant effects in altering the activities of neurotransmitters in certain regions of the brain.Consequently,these alterations manifest as cognitive,emotional,perceptual and behavioural changes in affected individuals[1].

Web-based cognitive interventions on subjective cognitive impairment in cancer survivors:A systemic review

Objective:Cancer survivors have experienced subjective cognitive impairment(SCI)when they received cancer diagnoses or treatments.Their psychosocial and emotional statuses were also impacted.With the advancement of web technologies,web-based cognitive interventions have been implemented in the management and the alleviation of the SCI,the psychosocial distress,and the emotional distress in cancer survivors.This review aimed to summarize the intervention contents of web-based cognitive interventions for SCI,and to explore the effects of the interventions on SCI,psychosocial status,and emotional health.Methods:Six databases(CINAHL Plus,Cochrane Library,Embase,APA PsycInfo,PubMed and CNKI)were searched from the establishment of databases up to December 2023.Literature references were also manually searched for related articles.Results:This review contained 21 studies that covered the contents of web-based cognitive interventions,such as computer-assisted cognitive training,online cognitive rehabilitation,cognitive behavior therapy with the Internet,telehealth physical exercise,and web-based mindfulness interventions.The effects of web-based cognitive interventions positively impacted SCI for cancer survivors.Also,these interventions showed varying degrees of effectiveness in alleviating psychosocial and emotional distresses.Conclusion:By summarizingfive types of cognitive intervention contents delivered via web technology,this review demonstrated that web-based cognitive interventions optimized SCI and overall psychosocial and emotional statuses for the cancer survivors.It is recommended that future research focus on the development of customized web-based cognitive interventions for individuals with SCI,along with their psychosocial and emotional statuses.

How to enhance agricultural plastic waste management in China?Insights from public participation

Agricultural plastics play a pivotal role in agricultural production.However,due to expensive costs,agricultural plastic waste management(APWM)encounters a vast funding gap.As one of the crucial stakeholders,the public deserves to make appropriate efforts for APWM.Accordingly,identifying whether the public is willing to pay for APWM and clarifying the decisions’driving pathways to explore initiatives for promoting their payment intentions are essential to address the dilemma confronting APWM.To this end,by applying the extended theory of planned behavior(TPB),the study conducted an empirical analysis based on 1,288 residents from four provinces(autonomous regions)of northern China.Results illustrate that:1)respondents hold generally positive and relatively strong payment willingness towards APWM;2)respondents’attitude(AT),subjective norm(SN),and perceived behavioral control(PBC)are positively correlated with their payment intentions(INT);3)environmental cognition(EC)and environmental emotion(EE)positively moderate the relationships between AT and INT,and between SN and INT,posing significant indirect impacts on INT.The study’s implications extend to informing government policies,suggesting that multi-entity cooperation,specifically public payment for APWM,can enhance agricultural non-point waste management.

Focus on pet diseases

Pets become vital companions in human life.Tey ofer companionship and emotional support,and contribute signifcantly to physical and mental health,as well as social activities.Living with pets can stimulate human bodies to release more“afnity hormones”such as serotonin,dopamine,and oxytocin,which helps alleviate negative emotions.Te human-animal bond can enhance the human body’s ability to eliminate toxins,reduce the risk of illnesses,strengthen the immune system,and alleviate depressive symptoms.Moreover,pet-related social activities can facilitate the making of new friends and the establishment of social connections,serving as a bridge for those who struggle with social interactions.

E2E-MFERC:AMulti-Face Expression Recognition Model for Group Emotion Assessment

In smart classrooms, conducting multi-face expression recognition based on existing hardware devices to assessstudents’ group emotions can provide educators with more comprehensive and intuitive classroom effect analysis,thereby continuouslypromotingthe improvementof teaching quality.However,most existingmulti-face expressionrecognition methods adopt a multi-stage approach, with an overall complex process, poor real-time performance,and insufficient generalization ability. In addition, the existing facial expression datasets are mostly single faceimages, which are of low quality and lack specificity, also restricting the development of this research. This paperaims to propose an end-to-end high-performance multi-face expression recognition algorithm model suitable forsmart classrooms, construct a high-quality multi-face expression dataset to support algorithm research, and applythe model to group emotion assessment to expand its application value. To this end, we propose an end-to-endmulti-face expression recognition algorithm model for smart classrooms (E2E-MFERC). In order to provide highqualityand highly targeted data support for model research, we constructed a multi-face expression dataset inreal classrooms (MFED), containing 2,385 images and a total of 18,712 expression labels, collected from smartclassrooms. In constructing E2E-MFERC, by introducing Re-parameterization visual geometry group (RepVGG)block and symmetric positive definite convolution (SPD-Conv) modules to enhance representational capability;combined with the cross stage partial network fusion module optimized by attention mechanism (C2f_Attention),it strengthens the ability to extract key information;adopts asymptotic feature pyramid network (AFPN) featurefusion tailored to classroomscenes and optimizes the head prediction output size;achieves high-performance endto-end multi-face expression detection. Finally, we apply the model to smart classroom group emotion assessmentand provide design references for classroom effect analysis evaluation metrics. Experiments based on MFED showthat the mAP and F1-score of E2E-MFERC on classroom evaluation data reach 83.6% and 0.77, respectively,improving the mAP of same-scale You Only Look Once version 5 (YOLOv5) and You Only Look Once version8 (YOLOv8) by 6.8% and 2.5%, respectively, and the F1-score by 0.06 and 0.04, respectively. E2E-MFERC modelhas obvious advantages in both detection speed and accuracy, which can meet the practical needs of real-timemulti-face expression analysis in classrooms, and serve the application of teaching effect assessment very well.

A Model for Detecting Fake News by Integrating Domain-Specific Emotional and Semantic Features

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.

Exploring Sequential Feature Selection in Deep Bi-LSTM Models for Speech Emotion Recognition

Machine Learning(ML)algorithms play a pivotal role in Speech Emotion Recognition(SER),although they encounter a formidable obstacle in accurately discerning a speaker’s emotional state.The examination of the emotional states of speakers holds significant importance in a range of real-time applications,including but not limited to virtual reality,human-robot interaction,emergency centers,and human behavior assessment.Accurately identifying emotions in the SER process relies on extracting relevant information from audio inputs.Previous studies on SER have predominantly utilized short-time characteristics such as Mel Frequency Cepstral Coefficients(MFCCs)due to their ability to capture the periodic nature of audio signals effectively.Although these traits may improve their ability to perceive and interpret emotional depictions appropriately,MFCCS has some limitations.So this study aims to tackle the aforementioned issue by systematically picking multiple audio cues,enhancing the classifier model’s efficacy in accurately discerning human emotions.The utilized dataset is taken from the EMO-DB database,preprocessing input speech is done using a 2D Convolution Neural Network(CNN)involves applying convolutional operations to spectrograms as they afford a visual representation of the way the audio signal frequency content changes over time.The next step is the spectrogram data normalization which is crucial for Neural Network(NN)training as it aids in faster convergence.Then the five auditory features MFCCs,Chroma,Mel-Spectrogram,Contrast,and Tonnetz are extracted from the spectrogram sequentially.The attitude of feature selection is to retain only dominant features by excluding the irrelevant ones.In this paper,the Sequential Forward Selection(SFS)and Sequential Backward Selection(SBS)techniques were employed for multiple audio cues features selection.Finally,the feature sets composed from the hybrid feature extraction methods are fed into the deep Bidirectional Long Short Term Memory(Bi-LSTM)network to discern emotions.Since the deep Bi-LSTM can hierarchically learn complex features and increases model capacity by achieving more robust temporal modeling,it is more effective than a shallow Bi-LSTM in capturing the intricate tones of emotional content existent in speech signals.The effectiveness and resilience of the proposed SER model were evaluated by experiments,comparing it to state-of-the-art SER techniques.The results indicated that the model achieved accuracy rates of 90.92%,93%,and 92%over the Ryerson Audio-Visual Database of Emotional Speech and Song(RAVDESS),Berlin Database of Emotional Speech(EMO-DB),and The Interactive Emotional Dyadic Motion Capture(IEMOCAP)datasets,respectively.These findings signify a prominent enhancement in the ability to emotional depictions identification in speech,showcasing the potential of the proposed model in advancing the SER field.

Evolution and spatiotemporal analysis of earthquake public opinion based on social media data

As critical conduits for the dissemination of online public opinion,social media platforms offer a timely and effective means for managing emergencies during major disasters,such as earthquakes.This study focuses on the analysis of online public opinions following the Maduo M7.4 earthquake in Qinghai Province and the Yangbi M6.4 earthquake in Yunnan Province.By collecting,cleaning,and organizing post-earthquake Sina Weibo(short for Weibo)data,we employed the Latent Dirichlet Allocation(LDA)model to extract information pertinent to public opinion on these earthquakes.This analysis included a comparison of the nature and temporal evolution of online public opinions related to both events.An emotion analysis,utilizing an emotion dictionary,categorized the emotional content of post-earthquake Weibo posts,facilitating a comparative study of the characteristics and temporal trends of online public emotions following the earthquakes.The findings were visualized using Geographic Information System(GIS)techniques.The analysis revealed certain commonalities in online public opinion following both earthquakes.Notably,the peak of online engagement occurred within the first 24 hours post-earthquake,with a rapid decline observed between 24 to 48 hours thereafter.The variation in popularity of online public opinion was linked to aftershock occurrences.Adjusted for population factors,online engagement in areas surrounding the earthquake sites and in Sichuan Province was significantly high.Initially dominated by feelings of“fear”and“surprise”,the public sentiment shifted towards a more positive outlook with the onset of rescue operations.However,distinctions in the online public response to each earthquake were also noted.Following the Yangbi earthquake,Yunnan Province reported the highest number of Weibo posts nationwide;in contrast,Qinghai Province ranked third post-Maduo earthquake,attributable to its smaller population size and extensive damage to communication infrastructure.This research offers a methodological approach for the analysis of online public opinion related to earthquakes,providing insights for the enhancement of post-disaster emergency management and public mental health support.