Study on Microblog Dissemination Law in View ol Forwarding

Forwarding is a major means of information dissemination on the Microblog platform.The article,combining static analysis and dynamic analysis,takes Microblog forwarding as the object of study,and studies the network topology of grass-roots Microblog forwarding users.It also studies the correlation between characteristic quantity and forwarding times of Microblog network topology.Furthermore,it conducts modification on virus transmission model,builds and verifies the Microblog forwarding dynamical model.The study finds out that Microblog postings present qute strong dissemination capacity on the initial stage,and some Microblog postings with many forwarding times and long duration of forwarding process due to the dynamic growth of the forwarding user network and the joining of strong nodes make network infection density decrease in some phases.

Mining User Interest in Microblogs with a User-Topic Model

Microblogs have become an important platform for people to publish,transform information and acquire knowledge.This paper focuses on the problem of discovering user interest in microblogs.In this paper,we propose a topic mining model based on Latent Dirichlet Allocation(LDA) named user-topic model.For each user,the interests are divided into two parts by different ways to generate the microblogs:original interest and retweet interest.We represent a Gibbs sampling implementation for inference the parameters of our model,and discover not only user's original interest,but also retweet interest.Then we combine original interest and retweet interest to compute interest words for users.Experiments on a dataset of Sina microblogs demonstrate that our model is able to discover user interest effectively and outperforms existing topic models in this task.And we find that original interest and retweet interest are similar and the topics of interest contain user labels.The interest words discovered by our model reflect user labels,but range is much broader.

Analysis of User's Weight in Microblog Network Based on User Influence and Active Degree

Based on user＇s in-degree distribution, traditional ranking algorithms of user＇s weight usually neglect the considerations of the differences among user＇s followers and the features of user＇s tweets. In order to analyze the factors which impact on user＇s weight, under the analysis of the data collected from SINA Microblog network, this paper discovers that user influence and active degrees are the dominant factors for this issue. The proposed algorithm evaluates user influence by user＇s follower number, the influence of user＇s followers and the reciprocity between users. User＇s active degree is modeled by user＇s participation and the quality of user＇s tweets. The models are tested by different data groups to confirm the parameters for the final calculation. Eventually, this paper compares the computational results with the user＇s ranking order given by the SINA official application. The performance of this algorithm presents a stronger stability on the fluctuant range of the value of user＇s weight.

Evaluation of Microblog Users’ Influence Based on PageRank and Users Behavior Analysis

This paper explores the uses’ influences on microblog. At first, according to the social network theory, we present an analysis of information transmitting network structure based on the relationship of following and followed phenomenon of microblog users. Informed by the microblog user behavior analysis, the paper also addresses a model for calculating weights of users’ influence. It proposes a U-R model, using which we can evaluate users’ influence based on PageRank algorithms and analyzes user behaviors. In the U-R model, the effect of user behaviors is explored and PageRank is applied to evaluate the importance and the influence of every user in a microblog network by repeatedly iterating their own U-R value. The users’ influences in a microblog network can be ranked by the U-R value. Finally, the validity of U-R model is proved with a real-life numerical example.

Microblog Summarization via Enriching Contextual Features Based on Sentence-Level Semantic Analysis

A novel microblog summarization approach via enriching contextual features on sentencelevel semantic analysis is proposed in this paper. At first,a Chinese sentential semantic model（ CSM） is employed to analyze the semantic structure of each microblog sentence. Then,a combination of sentence-level semantic analysis and latent dirichlet allocation is utilized to acquire extra features and related words to enrich the collection of microblog messages. The simlilarites between the two sentences are calculated based on the enriched features. Finally,the semantic weight and relation weight are calculated to select the most informative sentences,which form the final summary for microblog messages. Experimental results demonstrate the advantages of our proposed approach.The results indicate that introducing sentence-level semantic analysis for context enrichment can better represent sentential semantic. The proposed criteria,namely,semantic weight and relation weight enhance summary result. Furthermore,CSM is a useful framework for sentence-level semantic analysis.

Educational Reform in Management Courses of Agricultural & Forestry Higher Vocational Schools from the Perspective of Microblog

At present there are many socialized microblog platforms.With powerful mobility,real-time information,fragment of information dissemination,and innovation of interaction,the microblog has become a socialized interaction mode in recent years.Since microblog is very popular with students of agricultural and forestry higher vocational schools,with the rising and development of network education,the microblog as a new information platform will be used by more and more teachers in education.From the perspective of microblog,this paper studied educational reform in management courses of agricultural and forestry higher vocational schools,in the hope of providing certain reference and help for current education practice of agricultural and forestry management courses.

A comparative study of information diffusion in weblogs and microblogs based on social network analysis

Purpose: This paper intends to explore a quantitative method for investigating the characteristics of information diffusion through social media like weblogs and microblogs.By using the social network analysis methods,we attempt to analyze the different characteristics of information diffusion in weblogs and microblogs as well as the possible reasons of these differences.Design/methodology/approach: Using the social network analysis methods,this paper carries out an empirical study by taking the Chinese weblogs and microblogs in the field of Library and Information Science(LIS) as the research sample and employing measures such as network density,core/peripheral structure and centrality.Findings: Firstly,both bloggers and microbloggers maintain weak ties,and both of their social networks display a small-world effect. Secondly,compared with weblog users,microblog users are more interconnected,more equal and more capable of developing relationships with people outside their own social networks. Thirdly,the microblogging social network is more conducive to information diffusion than the blogging network,because of their differences in functions and the information flow mechanism. Finally,the communication mode emerged with microblogging,with the characteristics of micro-content,multi-channel information dissemination,dense and decentralized social network and content aggregation,will be one of the trends in the development of the information exchange platform in the future.Research limitations: The sample size needs to be increased so that samples are more representative. Errors may exist during the data collection. Moreover,the individual-level characteristics of the samples as well as the types of information exchanged need to be further studied.Practical implications: This preliminary study explores the characteristics of information diffusion in the network environment and verifies the feasibility of conducting a quantitative analysis of information diffusion through social media. In addition,it provides insight into the characteristics of information diffusion in weblogs and microblogs and the possible reasons of these differences.Originality/value: We have analyzed the characteristics of information diffusion in weblogs and microblogs by using the social network analysis methods. This research will be useful for a quantitative analysis of the underlying mechanisms of information flow through social media in the network environment.

Predicting Stock Using Microblog Moods

Some research work has showed that public mood and stock market price have some relations in some degree. Although it is difficult to clear the relation, the research about the relation between stock market price and public mood is interested by some scientists. This paper tries to find the relationship between Chinese stock market and Chinese local Microblog. First, C-POMS(Chinese Profile of Mood States) was proposed to analyze sentiment of Microblog feeds. Then Granger causality test confirmed the relation between C-POMS analysis and price series. SVM and Probabilistic Neural Network were used to make prediction, and experiments show that SVM is better to predict stock market movements than Probabilistic Neural Network. Experiments also indicate that adding certain dimension of C-POMS as the input data will improve the prediction accuracy to 66.667%. Two dimensions to input data leads to the highest accuracy of 71.429%, which is about 20% higher than using only history stock data as the input data. This paper also compared the proposed method with the ROSTEA scores, and concluded that only the proposed method brings more accurate predicts.

What is Discussed about COVID-19:A Multi-Modal Framework for Analyzing Microblogs from Sina Weibo without Human Labeling

Starting from late 2019,the new coronavirus disease(COVID-19)has become a global crisis.With the development of online social media,people prefer to express their opinions and discuss the latest news online.We have witnessed the positive influence of online social media,which helped citizens and governments track the development of this pandemic in time.It is necessary to apply artificial intelligence(AI)techniques to online social media and automatically discover and track public opinions posted online.In this paper,we take Sina Weibo,the most widely used online social media in China,for analysis and experiments.We collect multi-modal microblogs about COVID-19 from 2020/1/1 to 2020/3/31 with a web crawler,including texts and images posted by users.In order to effectively discover what is being discussed about COVID-19 without human labeling,we propose a unified multi-modal framework,including an unsupervised short-text topic model to discover and track bursty topics,and a self-supervised model to learn image features so that we can retrieve related images about COVID-19.Experimental results have shown the effectiveness and superiority of the proposed models,and also have shown the considerable application prospects for analyzing and tracking public opinions about COVID-19.

Microblog User Recommendation Based on Particle Swarm Optimization

Considering that there exists a strong similarity between behaviors of users and intelligence of swarm of agents,in this paper we propose a novel user recommendation strategy based on particle swarm optimization(PSO)for Microblog network. Specifically,a PSO-based algorithm is developed to learn the user influence,where not only the number of followers is incorporated,but also the interactions among users(e.g.,forwarding and commenting on other users' tweets). Three social factors,the influence and the activity of the target user,together with the coherence between users,are fused to improve the performance of proposed recommendation strategy. Experimental results show that,compared to the well-known Page Rank-based algorithm,the proposed strategy performs much better in terms of precision and recall and it can effectively avoid a biased result caused by celebrity effect and zombie fans effect.

A time-aware query-focused summarization of an evolving microblogging stream via sentence extraction

With the number of social media users ramping up,microblogs are generated and shared at record levels.The high momentum and large volumes of short texts bring redundancies and noises,in which the users and analysts often find it problematic to elicit useful information of interest.In this paper,we study a query-focused summarization as a solution to address this issue and propose a novel summarization framework to generate personalized online summaries and historical summaries of arbitrary time durations.Our framework can deal with dynamic,perpetual,and large-scale microblogging streams.Specifically,we propose an online microblogging stream clustering algorithm to cluster microblogs and maintain distilled statistics called Microblog Cluster Vectors(MCV).Then we develop a ranking method to extract the most representative sentences relative to the query from the MCVs and generate a query-focused summary of arbitrary time durations.Our experiments on large-scale real microblogs demonstrate the efficiency and effectiveness of our approach.

Learning trendiness from twitter to web： a comparative analysis of microblog and web trending topics

The development of microblog services has a considerable etfect on the patterns oI wed access and Internet resources discovery. Understanding the interrelation between information diffusion in online social media and user web interests can help the web ecosystem stakeholders in developing new services and designing efficient systems with optimized resources. This paper explores whether or not one can infer the trends of topics in the web by observing the Twitter microcosm. Using data- sets collected from Twitter and two representative web services （Google and Alexa）, this work con- ducts a comparative analysis between trending patterns of topics in Twitter and in the web by consid- ering both the temporal and spatial perspectives, and finds that individual topics in Twitter and in the web share similar trending patterns both from the temporal and spatial aspects. Nevertheless, the tren- diness in Twitter can precede for a few hours and is highly unstable compared to the one in web. The application of these findings is also discussed on ad keywords planning in Search Engine Marketing.

User-Level Sentiment Evolution Analysis in Microblog

People's attitudes towards public events or products may change overtime,rather than staying on the same state.Understanding how sentiments change overtime is an interesting and important problem with many applications.Given a certain public event or product,a user's sentiments expressed in microblog stream can be regarded as a vector.In this paper,we define a novel problem of sentiment evolution analysis,and develop a simple yet effective method to detect sentiment evolution in user-level for public events.We firstly propose a multidimensional sentiment model with hierarchical structure to model user's complicate sentiments.Based on this model,we use FP-growth tree algorithm to mine frequent sentiment patterns and perform sentiment evolution analysis by Kullback-Leibler divergence.Moreover,we develop an improve Affinity Propagation algorithm to detect why people change their sentiments.Experimental evaluations on real data sets show that sentiment evolution could be implemented effectively using our method proposed in this article.

An Analysis of the Value and Strategies of Microblog Marketing

Microblog marketing is that the enterprise uses the platform of Sina microblog to carry out its own marketing activities.Every fan and even every netizen are potential business participants.With microblog marketing,the enterprise intends to create a good image for the company and products and achieve marketing goals by means of communicating with customers by updating daily content or publishing topics that may be of interest to customers.

Research on the Use of Network Language in Chinese Government Microblogs

The emergence of government microblog not only widens the channels for the people to participate in politics,but also is of great significance for the government to understand public opinion and promote the process of political democratization.In recent years,the frequency of network language in Chinese government microblog has gradually increased.Taking the“Beijing release”official microblog as an example,this paper discusses the characteristics of network language,and puts forward some suggestions on the use of network language in government microblog:in terms of syllables,network language below three syllables should be selected to meet the requirements of short length of government microblog;In terms of part of speech,network terms such as nouns,verbs and adjectives can express emotional attitude and value judgment;In terms of emotion and style,emotional attitude forms such as intimacy,love and respect are selected.In addition,the norms of network language in government microblog can not be generalized.We should not only pay attention to the expression effect,but also set foot in multiple disciplines and fields.

Celebrity and ordinary users: A comparative study of microblog user behaviors on Sina Weibo

Purpose： This study aims to investigate and compare celebrity and ordinary users＇ behaviors on Sina Weibo. Design/methodology/approach： Data was collected from 12,555 ordinary users and 2,467 celebrity users on Sina Weibo. Correlation and regression analysis was performed on users＇ number of followings, number of followers and number of posts. Findings： The results revealed significant difference between famous and ordinary users＇ behaviors on Sina Weibo. We found correlation among ordinary users＇ number of followings, number of followers and number of posts, but for celebrity users, only their number of followings and number of posts are related with each other. For both ordinary and celebrity users, their number of followings significantly affects how many posts they publish. Research limitations： We only carried out our investigation on Sina Weibo and the findings need to be further verified on other microblogging platforms. Practical implications： This research is useful for microblogging service providers to understand different types of users and promote the continuous use of their services. Originality/value： This research delivers valuable insights into understanding of the characteristics of different types of microbloggers and the ways to increase user viscosity.

A time sequence analysis on the informal information flow mechanism of microblogging

Microblog is a new Internet featured product, which has seen a rapid development in recent years. Researchers from different countries are making various technical analyses on microblogging applications. In this study, through using the natural language processing(NLP) and data mining, we analyzed the information content transmitted via a microblog, users' social networks and their interactions, and carried out an empirical analysis on the dissemination process of one particular piece of information via Sina Weibo.Based on the result of these analyses, we attempt to develop a better understanding about the rule and mechanism of the informal information flow in microblogging.

A Fine-Grained Emotion Analysis Method for Chinese Microblog

The Chinese microblog text is short,full of noise data and emoticons,and the words are often irregularly.For these characteristics,we proposed a fine-grained emotion analysis method.Combined with TF-IDF and variance statistics,we realized a method of calculating multi-class feature selection.We judge the text whether it is positive or negative first,then choose the fine-grained emotional tendency.And we get good result with the test using COAE data set.Compared with other method for feature selection and other emotional library,we did better.

Normalization of Homophonic Words in Chinese Microblogs

Homophonic words are very popular in Chinese microblog, posing a new challenge for Chinese microblog text analysis. However, to date, there has been very little research conducted on Chinese homophonic words normalization. In this paper, we take Chinese homophonic word normalization as a process of language decoding and propose an n-gram based approach. To this end, we first employ homophonic–original word or character mapping tables to generate normalization candidates for a given sentence with homophonic words, and thus exploit n-gram language models to decode the best normalization from the candidate set. Our experimental results show that using the homophonic-original character mapping table and n-grams trained from the microblog corpus help improve performance in homophonic word recognition and restoration.

Time-Based Microblog Search System

This demo shows a time-based microblog research system which developed based on the time profile to estimate the query model,the document model and rank function for microblog search.The system exploits the time profile to boost the performance of microblog search.A brief description of the time-based query model,time-based document model and time-based similarity score is introduced.The index strategy for temporal microblog search is described.Using TREC 2011 and TREC 2012 microblog retrieval collection,the examples of microblog search results are demonstrated.