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 to...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.展开更多
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...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.展开更多
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 wi...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.展开更多
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...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.展开更多
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 normaliza...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.展开更多
Message forwarding (e.g.,retweeting on Twitter.com) is one of the most popular functions in many existing microblogs,and a large number of users participate in the propagation of information,for any given messages.Whi...Message forwarding (e.g.,retweeting on Twitter.com) is one of the most popular functions in many existing microblogs,and a large number of users participate in the propagation of information,for any given messages.While this large number can generate notable diversity and not all users have the same ability to diffuse the messages,this also makes it challenging to find the true users with higher spreadability,those generally rated as interesting and authoritative to diffuse the messages.In this paper,a novel method called SpreadRank is proposed to measure the spreadability of users in microblogs,considering both the time interval of retweets and the location of users in information cascades.Experiments were conducted on a real dataset from Twitter containing about 0.26 million users and 10 million tweets,and the results showed that our method is consistently better than the PageRank method with the network of retweets and the method of retweetNum which measures the spreadability according to the number of retweets.Moreover,we find that a user with more tweets or followers does not always have stronger spreadability in microblogs.展开更多
Hashtag recommendation for microblogs is a very hot research topic that is useful to many applications involving microblogs. However, since short text in microblogs and low utilization rate of hashtags will lead to th...Hashtag recommendation for microblogs is a very hot research topic that is useful to many applications involving microblogs. However, since short text in microblogs and low utilization rate of hashtags will lead to the data sparsity problem, it is difficult for typical hashtag recommendation methods to achieve accurate recommendation. In light of this, we propose HRMF, a hashtag recommendation method based on multi-features of microblogs in this article. First, our HRMF expands short text into long text, and then it simultaneously models multi-features （i.e., user, hashtag, text） of microblogs by designing a new topic model. To further alleviate the data sparsity problem, HRMF exploits hashtags of both similar users and similar microblogs as the candidate hashtags. In particular, to find similar users, HRMF combines the designed topic model with typical user-based collaborative filtering method. Finally, we realize hashtag recommendation by calculating the recommended score of each hashtag based on the generated topical representations of multi-features. Experimental results on a real-world dataset crawled from Sina Weibo demonstrate the effectiveness of our HRMF for hashtag recommendation.展开更多
Link prediction in microblogs by using unsupervised methods has been studied extensively in recent years, which aims to find an appropriate similarity measure between users in the network. However, the measures used b...Link prediction in microblogs by using unsupervised methods has been studied extensively in recent years, which aims to find an appropriate similarity measure between users in the network. However, the measures used by existing work lack a simple way to incorporate the structure of the network and the interactions between users. This leads to the gap between the predictive result and the ground truth value. For example, the F 1-measure created by the best method is around 0.2. In this work, we firstly discover the gap and prove its existence. To narrow this gap, we define the retweeting similarity to measure the interactions between users in Twitter, and propose a structural-interaction based matrix factorization model for following-link prediction. Experiments based on the real-world Twitter data show that our model outperforms state-of-the-art methods.展开更多
The rapid development of social networks has resulted in a proliferation of user-generated content(UGC),which can benefit many applications.In this paper,we study the problem of identifying a user's locations from...The rapid development of social networks has resulted in a proliferation of user-generated content(UGC),which can benefit many applications.In this paper,we study the problem of identifying a user's locations from microblogs,to facilitate effective location-based advertisement and recommendation.Since the location information in a microblog is incomplete,we cannot get an accurate location from a local microblog.As such,we propose a global location identification method,Glitter.Glitter combines multiple microblogs of a user and utilizes them to identify the user's locations.Glitter not only improves the quality of identifying a user's location but also supplements the location of a microblog so as to obtain an accurate location of a microblog.To facilitate location identification,Glitter organizes points of interest(POIs)into a tree structure where leaf nodes are POIs and non-leaf nodes are segments of POIs,e.g.,countries,cities,and streets.Using the tree structure,Glitter first extracts candidate locations from each microblog of a user which correspond to some tree nodes.Then Glitter aggregates these candidate locations and identifies top-κlocations of the user.Using the identified top-κuser locations,Glitter refines the candidate locations and computes top-κlocations of each microblog.To achieve high recall,we enable fuzzy matching between locations and microblogs.We propose an incremental algorithm to support dynamic updates of microblogs.We also study how to identify users'trajectories based on the extracted locations.We propose an effective algorithm to extract high-quality trajectories.Experimental results on real-world datasets show that our method achieves high quality and good performance,and scales well.展开更多
Hashtags are important metadata in microblogs and are used to mark topics or index messages. However,statistics show that hashtags are absent from most microblogs. This poses great challenges for the retrieval and ana...Hashtags are important metadata in microblogs and are used to mark topics or index messages. However,statistics show that hashtags are absent from most microblogs. This poses great challenges for the retrieval and analysis of these tagless microblogs. In this paper, we summarize the similarity between microblogs and shortmessage-style news, and then propose an algorithm, named 5WTAG, for detecting microblog topics based on a model of five Ws（When, Where, Who, What, ho W）. As five-W attributes are the core components in event description, it is guaranteed theoretically that 5WTAG can properly extract semantic topics from microblogs. We introduce the detailed procedure of the algorithm in this paper including spam microblog identification, microblog segmentation, and candidate hashtag construction. In addition, we propose a novel recommendation computing method for ranking candidate hashtags, which combines syntax and semantic analysis and observes the distribution of artificial topic hashtags. Finally, we conduct comprehensive experiments to verify the semantic correctness and completeness of the candidate hashtags, as well as the accuracy of the recommendation method using real data from Sina Weibo.展开更多
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. Th...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.展开更多
The objective of this study is to understand the current mental status of college students in China's Mainland. In this study, 60 thousand college students＇ microblog content from January 2014 to June 2014 was co...The objective of this study is to understand the current mental status of college students in China's Mainland. In this study, 60 thousand college students＇ microblog content from January 2014 to June 2014 was collected. An emotional energy level, which was developed by a psychologist David R Hawkins, was taken as a basis for ontology database to divide the student＇s emotion into three parts--positive, negative and neutral status. An ontology-based semantic analysis method was used to analyze the microblog data. The result shows that 46.38% of Sina microblog data reflects positive psychological status, and the ratios of neutral and negative psychological status are 19.77% and 33.85%, respectively. It means that almost one third microblog reflects some negative mentality. The semantic analysis of the big data suggests that most students have healthy mental status, and the negative status of the students should not be ignored.展开更多
Microblog is a social platform with huge user community and mass data. We propose a semantic recommendation mechanism based on sentiment analysis for microblog. Firstly, the keywords and sensibility words in this mech...Microblog is a social platform with huge user community and mass data. We propose a semantic recommendation mechanism based on sentiment analysis for microblog. Firstly, the keywords and sensibility words in this mechanism are extracted by natural language processing including segmentation, lexical analysis and strategy selection. Then, we query the background knowledge base based on linked open data （LOD） with the basic information of users. The experiment result shows that the accuracy of recommendation is within the range of 70% -89% with sentiment analysis and semantic query. Compared with traditional recommendation method, this method can satisfy users＇ requirement greatly.展开更多
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 a...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.展开更多
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...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.展开更多
With the development of online social networks,a special group of online users named organized posters(or Internet water army,Internet paid posters in some literatures) have fl ooded the social network communities. Th...With the development of online social networks,a special group of online users named organized posters(or Internet water army,Internet paid posters in some literatures) have fl ooded the social network communities. They are organized in groups to post with specific purposes and sometimes even confuse or mislead normal users.In this paper,we study the individual and group characteristics of organized posters. A classifier is constructed based on the individual and group characteristics to detect them. Extensive experimental results on three real datasets demonstrate that our method based on individual and group characteristics using SVM model(IGCSVM) is effective in detecting organized posters and better than existing methods. We take a first look at finding the promoters based on the detected organized posters of our IGCSVM method. Our experiments show that it is effective in detecting promoters.展开更多
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&#...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.展开更多
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 ...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.展开更多
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 of...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.展开更多
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 interacti...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.展开更多
基金This work was supported by the National High Technology Research and Development Program of China（No. 2010AA012505, 2011AA010702, 2012AA01A401 and 2012AA01A402）, Chinese National Science Foundation （No. 60933005, 91124002,61303265）, National Technology Support Foundation （No. 2012BAH38B04） and National 242 Foundation （No. 2011A010）
文摘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.
基金supported by Sun Yat-sen University Cultivation Fund for Young Teachers(Grant No.:20000-3161102)the National Social Science Fundation of China(Grant No.:08CTQ015)
文摘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.
基金This paper is supported by the Fundamental Research Funds for the Central Universities[No.JUSRP12021].
文摘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.
文摘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.
基金This study was supported by National Natural Science Foundation of China under Grant No.61170148 and No.60973081, the Returned Scholar Foundation of Heilongjiang Province, and Harbin Innovative Foundation for Returnees under Grant No.2009RFLXG007, respectively.
文摘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.
基金supported by the National Natural Science Foundation of China(Nos.60933005 and 91124002)the National High-Tech R&D Program (863) of China(Nos.012505,2011AA010702,2012AA01A401,and 2012AA01A402)+1 种基金the 242 Information Security Program(No.2011A010)the National Science and Technology Support Program(Nos.2012BAH38B04 and 2012BAH38B06),China
文摘Message forwarding (e.g.,retweeting on Twitter.com) is one of the most popular functions in many existing microblogs,and a large number of users participate in the propagation of information,for any given messages.While this large number can generate notable diversity and not all users have the same ability to diffuse the messages,this also makes it challenging to find the true users with higher spreadability,those generally rated as interesting and authoritative to diffuse the messages.In this paper,a novel method called SpreadRank is proposed to measure the spreadability of users in microblogs,considering both the time interval of retweets and the location of users in information cascades.Experiments were conducted on a real dataset from Twitter containing about 0.26 million users and 10 million tweets,and the results showed that our method is consistently better than the PageRank method with the network of retweets and the method of retweetNum which measures the spreadability according to the number of retweets.Moreover,we find that a user with more tweets or followers does not always have stronger spreadability in microblogs.
基金This work was supported by the National Natural Science Foundation of China under Grant Nos. 61320106006, 61532006, 61772083, and 61502042, and the Fundamental Research Funds for the Central Universities of China under Grant No. 2017RC39.
文摘Hashtag recommendation for microblogs is a very hot research topic that is useful to many applications involving microblogs. However, since short text in microblogs and low utilization rate of hashtags will lead to the data sparsity problem, it is difficult for typical hashtag recommendation methods to achieve accurate recommendation. In light of this, we propose HRMF, a hashtag recommendation method based on multi-features of microblogs in this article. First, our HRMF expands short text into long text, and then it simultaneously models multi-features （i.e., user, hashtag, text） of microblogs by designing a new topic model. To further alleviate the data sparsity problem, HRMF exploits hashtags of both similar users and similar microblogs as the candidate hashtags. In particular, to find similar users, HRMF combines the designed topic model with typical user-based collaborative filtering method. Finally, we realize hashtag recommendation by calculating the recommended score of each hashtag based on the generated topical representations of multi-features. Experimental results on a real-world dataset crawled from Sina Weibo demonstrate the effectiveness of our HRMF for hashtag recommendation.
基金This work is supported by the National Basic Research 973 Program of China under Grant Nos. 2013CB329602 and 2014CB340405, the National Natural Science Foundation of China under Grant Nos. 61173008, 61232010, 60933005, 61402442, 61402022, and 61303244, Beijing Nova Program under Grant No. Z121101002512063, and the Natural Science Foundation of Beijing under Grant No. 4154086.
文摘Link prediction in microblogs by using unsupervised methods has been studied extensively in recent years, which aims to find an appropriate similarity measure between users in the network. However, the measures used by existing work lack a simple way to incorporate the structure of the network and the interactions between users. This leads to the gap between the predictive result and the ground truth value. For example, the F 1-measure created by the best method is around 0.2. In this work, we firstly discover the gap and prove its existence. To narrow this gap, we define the retweeting similarity to measure the interactions between users in Twitter, and propose a structural-interaction based matrix factorization model for following-link prediction. Experiments based on the real-world Twitter data show that our model outperforms state-of-the-art methods.
基金the National Natural Science Foundation of China under Grant Nos.61802414,61632016,61521002 and 61661166012the National Basic Research 973 Program of China under Grant No.2015CB358700+1 种基金the Social Science Foundation of Beijing under Grant No.18XCC011the Humanities and Social Sciences Base Foundation of Ministry of Education of China under Grant No.16JJD860008,Huawei,and TAL(Tomorrow Advancing Life)education.
文摘The rapid development of social networks has resulted in a proliferation of user-generated content(UGC),which can benefit many applications.In this paper,we study the problem of identifying a user's locations from microblogs,to facilitate effective location-based advertisement and recommendation.Since the location information in a microblog is incomplete,we cannot get an accurate location from a local microblog.As such,we propose a global location identification method,Glitter.Glitter combines multiple microblogs of a user and utilizes them to identify the user's locations.Glitter not only improves the quality of identifying a user's location but also supplements the location of a microblog so as to obtain an accurate location of a microblog.To facilitate location identification,Glitter organizes points of interest(POIs)into a tree structure where leaf nodes are POIs and non-leaf nodes are segments of POIs,e.g.,countries,cities,and streets.Using the tree structure,Glitter first extracts candidate locations from each microblog of a user which correspond to some tree nodes.Then Glitter aggregates these candidate locations and identifies top-κlocations of the user.Using the identified top-κuser locations,Glitter refines the candidate locations and computes top-κlocations of each microblog.To achieve high recall,we enable fuzzy matching between locations and microblogs.We propose an incremental algorithm to support dynamic updates of microblogs.We also study how to identify users'trajectories based on the extracted locations.We propose an effective algorithm to extract high-quality trajectories.Experimental results on real-world datasets show that our method achieves high quality and good performance,and scales well.
基金supported by the National Natural Science Foundation of China (No. 61173027)the Northeastern University Fundamental Research Funds for the Central Universities (Nos. N150404012 and N140404006)
文摘Hashtags are important metadata in microblogs and are used to mark topics or index messages. However,statistics show that hashtags are absent from most microblogs. This poses great challenges for the retrieval and analysis of these tagless microblogs. In this paper, we summarize the similarity between microblogs and shortmessage-style news, and then propose an algorithm, named 5WTAG, for detecting microblog topics based on a model of five Ws（When, Where, Who, What, ho W）. As five-W attributes are the core components in event description, it is guaranteed theoretically that 5WTAG can properly extract semantic topics from microblogs. We introduce the detailed procedure of the algorithm in this paper including spam microblog identification, microblog segmentation, and candidate hashtag construction. In addition, we propose a novel recommendation computing method for ranking candidate hashtags, which combines syntax and semantic analysis and observes the distribution of artificial topic hashtags. Finally, we conduct comprehensive experiments to verify the semantic correctness and completeness of the candidate hashtags, as well as the accuracy of the recommendation method using real data from Sina Weibo.
基金supported by National Natural Science Foundation of China(No.61272362)National Basic Research Program ofChina(973 Program)(No.2013CB329606)High-Tech Development Plan of Xinjiang(No.201212124)
文摘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.
基金Supported by the Natural Science Foundation of Hubei Province(2013CFB292)
文摘The objective of this study is to understand the current mental status of college students in China's Mainland. In this study, 60 thousand college students＇ microblog content from January 2014 to June 2014 was collected. An emotional energy level, which was developed by a psychologist David R Hawkins, was taken as a basis for ontology database to divide the student＇s emotion into three parts--positive, negative and neutral status. An ontology-based semantic analysis method was used to analyze the microblog data. The result shows that 46.38% of Sina microblog data reflects positive psychological status, and the ratios of neutral and negative psychological status are 19.77% and 33.85%, respectively. It means that almost one third microblog reflects some negative mentality. The semantic analysis of the big data suggests that most students have healthy mental status, and the negative status of the students should not be ignored.
基金Supported by the National Natural Science Foundation of China(60803160 and 61272110)the Key Projects of National Social Science Foundation of China(11&ZD189)+4 种基金the Natural Science Foundation of Hubei Province(2013CFB334)the Natural Science Foundation of Educational Agency of Hubei Province(Q20101110)the State Key Lab of Software Engineering Open Foundation of Wuhan University(SKLSE2012-09-07)the Teaching Research Project of Hubei Province(2011s005)the Wuhan Key Technology Support Program(2013010602010216)
文摘Microblog is a social platform with huge user community and mass data. We propose a semantic recommendation mechanism based on sentiment analysis for microblog. Firstly, the keywords and sensibility words in this mechanism are extracted by natural language processing including segmentation, lexical analysis and strategy selection. Then, we query the background knowledge base based on linked open data （LOD） with the basic information of users. The experiment result shows that the accuracy of recommendation is within the range of 70% -89% with sentiment analysis and semantic query. Compared with traditional recommendation method, this method can satisfy users＇ requirement greatly.
基金supported by the National Natural Sciences Foundation of China under Grant No. 61172072the Beijing Natural Science Foundation under Grant No. 4112045the Fundamental Research Funds for the Central Universities under Grant No. 2011YJS215
文摘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.
文摘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.
基金supported by 973 Program of China(Grant No.2013CB329601, 2013CB329602,2013CB329604)NSFC of China(Grant No.60933005,91124002)+1 种基金863 Program of China(Grant No.2012AA01A401, 2012AA01A402)National Key Technology RD Program of China(Grant No.2012BAH38B04, 2012BAH38B06)
文摘With the development of online social networks,a special group of online users named organized posters(or Internet water army,Internet paid posters in some literatures) have fl ooded the social network communities. They are organized in groups to post with specific purposes and sometimes even confuse or mislead normal users.In this paper,we study the individual and group characteristics of organized posters. A classifier is constructed based on the individual and group characteristics to detect them. Extensive experimental results on three real datasets demonstrate that our method based on individual and group characteristics using SVM model(IGCSVM) is effective in detecting organized posters and better than existing methods. We take a first look at finding the promoters based on the detected organized posters of our IGCSVM method. Our experiments show that it is effective in detecting promoters.
文摘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.
基金Supported by 242 National Information Security Projects(2017A149)
文摘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.
基金This work was supported by Chongqing Research Program of Basic Research and Frontier Technology(cstc2017jcyjAX0071)Basic and Advanced Research Projects of CSTC(cstc2019jcyjzdxm0102)+1 种基金Chongqing Science and Technology Innovation Leading Talent Support Program(CSTCCXLJRC201908)Science and Technology Research Program of Chongqing Municipal Education Commission(KJZD-K201900605).
文摘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.
文摘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.
