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.

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.

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.

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.

A Lightweight Sentiment Analysis Method

The emergence of big data leads to an increasing demand for data processing methods.As the most influential media for Chinese domestic movie ratings,Douban contains a huge amount of data and one can understand users'perspectives towards these movies by analyzing these data.In this article,we study movie's critics from the Douban website,perform sentiment analysis on the data obtained by crawling,and visualize the results with a word cloud.We propose a lightweight sentiment analysis method which is free from heavy training and visualize the results in a more conceivable way.

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.

From New Media to Cloud Service,Can Independent College Libraries Face It Calmly?—Based on the Investigation and Analysis of Relevant Applications of Independent College Libraries in Jiangsu Province

The emergence and application of new media are both opportunities and challenges for the development of libraries.In light of statistical analysis of development and use of new media platform tools such as the library portal websites of independent colleges in Jiangsu Province,WeChat,MicroBlog,and mobile libraries,etc.,the author in this paper is aimed at studying independent college libraries’self-development level and innovation of service models under the new information network environment as well as how to win favorable time and space so as to easily face the advent of cloud service era.

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.

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.

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.

Micro Blog微@酒圈

李德美V：这种消息不是第一次出现了。英国mayfaire早些年不就是这样？所以前几天电台采访关于葡萄酒投资，忘了提到这点了，还有“人为的风险”。

Decoding the Microblog

The rise of Sina Weibo has China talking AS Li Yangyang,a 19-year-old university student in Beijing,comes home after a hard day of hitting the books,she immediately logs on the Internet with a sole purpose in mind, updating her microblog on Sina Weibo (literally meaning microblog). "Not a day goes by where I do not update my page.I am always eager

Event Detection and Identification of Influential Spreaders in Social Media Data Streams

Microblogging, a popular social media service platform, has become a new information channel for users to receive and exchange the most up-to-date information on current events. Consequently, it is a crucial platform for detecting newly emerging events and for identifying influential spreaders who have the potential to actively disseminate knowledge about events through microblogs. However, traditional event detection models require human intervention to detect the number of topics to be explored, which significantly reduces the efficiency and accuracy of event detection. In addition, most existing methods focus only on event detection and are unable to identify either influential spreaders or key event-related posts, thus making it challenging to track momentous events in a timely manner. To address these problems, we propose a Hypertext-Induced Topic Search(HITS) based Topic-Decision method(TD-HITS), and a Latent Dirichlet Allocation(LDA) based Three-Step model(TS-LDA). TDHITS can automatically detect the number of topics as well as identify associated key posts in a large number of posts. TS-LDA can identify influential spreaders of hot event topics based on both post and user information.The experimental results, using a Twitter dataset, demonstrate the effectiveness of our proposed methods for both detecting events and identifying influential spreaders.

Mining microblog user interests based on TextRank with TF-IDF factor

It is of great value and significance to model the interests of microblog user in terms of business and sociology.This paper presents a framework for mining and analyzing personal interests from microblog text with a new algorithm which integrates term frequency-inverse document frequency(TF-IDF) with TextRank.Firstly, we build a three-tier category system of user interest based on Wikipedia.In order to obtain the keywords of interest, we preprocess the posts, comments and reposts in different categories to select the keywords which appear both in the category system and microblogs.We then assign weight to each category and calculate the weight of keyword to get TF-IDF factors.Finally we score the ranking of each keyword by the TextRank algorithm with TF-IDF factors.Experiments on real Sina microblog data demonstrate that the precision of our approach significantly outperforms other existing methods.

Big Data Modeling and Analysis of Microblog Ecosystem

Recent progress of Web 2.0 applications has witnessed the rapid development of microblog in China, which has already been one of the most important ways for online communications, especially on sharing information. This paper tries to make an in-depth investigation on the big data modeling and analysis of microblog ecosystem in China by using a real dataset containing over17 million records of SinaWeibo users. First, we present the detailed geography, gender, authentication, education and age analysis of microblog users in this dataset. Then we conduct the numerical features distribution analysis, propose the user influence formula and calculate the influences for different kinds of microblog users. Finally, user content intention analysis is performed to reveal users most concerns in their daily life.