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.

News Text Topic Clustering Optimized Method Based on TF-IDF Algorithm on Spark

Due to the slow processing speed of text topic clustering in stand-alone architecture under the background of big data,this paper takes news text as the research object and proposes LDA text topic clustering algorithm based on Spark big data platform.Since the TF-IDF(term frequency-inverse document frequency)algorithm under Spark is irreversible to word mapping,the mapped words indexes cannot be traced back to the original words.In this paper,an optimized method is proposed that TF-IDF under Spark to ensure the text words can be restored.Firstly,the text feature is extracted by the TF-IDF algorithm combined CountVectorizer proposed in this paper,and then the features are inputted to the LDA(Latent Dirichlet Allocation)topic model for training.Finally,the text topic clustering is obtained.Experimental results show that for large data samples,the processing speed of LDA topic model clustering has been improved based Spark.At the same time,compared with the LDA topic model based on word frequency input,the model proposed in this paper has a reduction of perplexity.

Technology Innovation Management:Topic Evolutions and Research Trends from 1968 to 2022

The technology innovation management(TIM)field attracts an increasing amount of attention.This paper takes a retrospective look at high-quality publication output in the TIM field over the 55 years from 1968 to 2022,revealing topics,their evolutions,and research trends.A total of 31,498 articles and proceeding papers published during this period are analyzed.The paper first extracts the fine-grained topic words using the tool ITGInsight.Then Linlog algorithm is used to cluster topics based on the cooccurrence of the topic words.Time is integrated within the topic cluster results so that topic evolutions and research trends are analyzed.The TIM field has four main topic clusters:technology research,product research,firm research,and future research.In every topic cluster,there are many fine-sorted macro-topics and micro-topics.There is an obvious increase in diversity in the topic clusters of technology research and firm research.Especially,the evolution of technology research has been closely connected with society.In contrast,product research has declined in its topic size.At the same time,future research maintains a certain stability of its scientific publications.The research predicts that all the four topics will retain their popularity,and play an important role in the TIM field.Among them,technology research will continue to expand and enrich the TIM field.The other three topics will deepen their research for a better development of the TIM field.The paper also proposes some advice for industry professionals,policymakers,and researchers.