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The sustainable development of mobile government social media depends citizens&rsquo ; continued use. Based on the Stimulus-Organism-Response framework and social response theory, the present study investigated the impacts of perceived similarity and anthropomorphic cues on citizens&rsquo ; mobile government microblog continuance. A research model of mobile government microblog continuance was developed and empirical tested by using dataset collected from 428 mobile government microblog citizens in China. The results of structural equation modeling demonstrated that perceived similarity (including external similarity and internal similarity), and anthropomorphic cues (including social interaction value, visual appearance, and identity attractiveness), have positive influences on both cognitive and affective involvement, which further determinate mobile government microblog continuance. Considering the path coefficient and significant levels, the impact from affective involvement on mobile government microblog continuance is stronger that from cognitive involvement.

In political contexts, it is known that people act as "motivated reasoners", i.e., information is evaluated first for emotional affect, and this emotional reaction influences later deliberative reasoning steps. As social media becomes a more and more prevalent way of receiving political information, it becomes important to understand more completely the interaction between information, emotion, social community, and information-sharing behavior. In this paper, we describe a high-precision classifier for politically-oriented tweets, and an accurate classifier of a Twitter user's political affiliation. Coupled with existing sentiment-analysis tools for microblogs, these methods enable us to systematically study the interaction of emotion and sharing in a large corpus of politically-oriented microblog messages, collected from just before the 2012 US presidential election. In particular, we seek to understand how information sharing is influenced by the political affiliation of the sender and receiver of a message, and the sentiment associated with the message.

This article examines the strategies used to foster citizens' interaction with government microblogs. While government agencies are urged to adopt social media, little is known about how citizens respond to those efforts. Using data collected from the publicity microblogs of prefecture-level municipalities in China, this article indicates that government microblogs can foster citizen-initiated interaction by acquiring microblog influencers as followers, diversifying the sources of government posts and posting more multimedia content. However, regularly updating a government microblog is not necessarily associated with citizen participation.

Owing to rapid development of the Internet and the rise of the big data era, microblog has become the main means for people to spread and obtain information. If people can accurately predict the development trend of a microblog event, it will be of great significance for the government to carry out public relations activities on network event supervision and guide the development of microblog event reasonably for network crisis. This paper presents effective solutions to deal with trend prediction of microblog events' popularity. Firstly, by selecting the influence factors and quantifying the weight of each factor with an information entropy algorithm, the microblog event popularity is modeled. Secondly, the singular spectrum analysis is carried out to decompose and reconstruct the time series of the popularity of microblog event. Then, the box chart method is used to divide the popularity of microblog event into various trend spaces. In addition, this paper exploits the Bi-LSTM model to deal with trend prediction with a sequence to label model. Finally, the comparative experimental analysis is carried out on two real data sets crawled from Sina Weibo platform. Compared to three comparative methods, the experimental results show that our proposal improves F1-score by up to 39%.

This paper reflects the adaptation and transformation of the Chinese party-state's governing strategy in the digital era. Through a discourse analysis of the current Chinese debate on the role of microblogs in China, it argues that China's political elites have revised their social management strategy. They now tend to base their political decision-making on strategic calculations that reflect online public opinion in order to increase the system's efficiency and to generate a new kind of performance-based legitimacy. This turn to a more responsive mode of governance has been driven by the findings of Internet surveys and reports provided by Chinese research institutes and advisory bodies. A close reading of these documents and reports helps to answer the question of why authoritarian states such as China do not prohibit the spread of new communication technologies, even though these are said to have triggered or at least facilitated the rebellions of the Arab Spring. -- governance in China ; e-government ; e-governance ; deliberation

Web search engines discover indexable documents by recursively 'crawling' from a seed URL. Their rankings take into account link popularity. While this works well, it introduces biases towards older documents. Older documents are more likely to be the target of links, while new documents with few, or no, incoming links are unlikely to rank highly in search results. We describe a novel system for 'new-Web' search based on links retrieved from the Twitter micro-blogging service. The Twitter service allows individuals, organisations and governments to rapidly disseminate very short messages to a wide variety of interested parties. When a Twitter message contains a URL, we use the Twitter message as a description of the URL's target. As Twitter is frequently used for discussion of current events, these messages offer useful, up- to-date annotations and instantaneous popularity readings for a small, but timely, portion of the Web. Our working system is simple and fast and we believe may offer a significant advantage in revealing new information on the Web that would otherwise be hidden from searchers. Beyond the basic system, we anticipate the Twitter messages may add supplementary terms for a URL, or add weight to existing terms, and that the reputation or authority of each message sender may serve to weight both annotations and query-independent popularity.

Web search engines discover indexable documents by recursively 'crawling' from a seed URL. Their rankings take into account link popularity. While this works well, it introduces biases towards older documents. Older documents are more likely to be the target of links, while new documents with few, or no, incoming links are unlikely to rank highly in search results. We describe a novel system for 'new-Web' search based on links retrieved from the Twitter micro-blogging service. The Twitter service allows individuals, organisations and governments to rapidly disseminate very short messages to a wide variety of interested parties. When a Twitter message contains a URL, we use the Twitter message as a description of the URL's target. As Twitter is frequently used for discussion of current events, these messages offer useful, up- to-date annotations and instantaneous popularity readings for a small, but timely, portion of the Web. Our working system is simple and fast and we believe may offer a significant advantage in revealing new information on the Web that would otherwise be hidden from searchers. Beyond the basic system, we anticipate the Twitter messages may add supplementary terms for a URL, or add weight to existing terms, and that the reputation or authority of each message sender may serve to weight both annotations and query-independent popularity.

Microblog service (such as Twitter and Sina Weibo) have become an important platform for Internet content sharing. As the information in Microblog are widely used in public opinion mining, viral marketing and political campaigns, understanding how information diffuses over Microblogs, and explaining the process through which some tweets become popular, are important.The analysis of the information diffusion in Microblogs involves the data collection from Microblog, the modeling on information spreading and using the resulting models. Dealing with the huge amount of data flowing through microblogs is by itself a challenge. Designing an efficient and unbiased sampling algorithm for Microblog is therefore essential. Besides, the retweeting process in Microblog is complex because of the ephemerality of information, the topology of Microblog network and the particular features (such as number of followers) of publisher and retweeters.Two traditional models have been used for information diffusion : Independent Cascades and Linear Threshold models. However no one of them can describe completely the retweeting process in Microblog accurately. The analysis and design of new models to characterize the information diffusion in Microblog is therefore necessary. Moreover, a comprehensive description of the correlation between the information diffusion in Microblog and the searching trends of keywords on search engines is lacking although some work has been found some preliminary relationships.This work presnets a complete analysis of information diffusion in Microblog from. The contributions and innovations of this thesis are as follows:1)There are two popular unbiased Online Social Network (OSN) sampling algorithms,Metropolis-Hastings Random Walk (MHRW) and Unbiased Sampling for Directed Social Graph (USDSG) method. However they are both likely to yield considerable self-sampling probabilities when applied to Microblogs where there is local. To solve this problem, I have modelled the process of OSN sampling as a Markov ...

Microblog service (such as Twitter and Sina Weibo) have become an important platform for Internet content sharing. As the information in Microblog are widely used in public opinion mining, viral marketing and political campaigns, understanding how information diffuses over Microblogs, and explaining the process through which some tweets become popular, are important.The analysis of the information diffusion in Microblogs involves the data collection from Microblog, the modeling on information spreading and using the resulting models. Dealing with the huge amount of data flowing through microblogs is by itself a challenge. Designing an efficient and unbiased sampling algorithm for Microblog is therefore essential. Besides, the retweeting process in Microblog is complex because of the ephemerality of information, the topology of Microblog network and the particular features (such as number of followers) of publisher and retweeters.Two traditional models have been used for information diffusion : Independent Cascades and Linear Threshold models. However no one of them can describe completely the retweeting process in Microblog accurately. The analysis and design of new models to characterize the information diffusion in Microblog is therefore necessary. Moreover, a comprehensive description of the correlation between the information diffusion in Microblog and the searching trends of keywords on search engines is lacking although some work has been found some preliminary relationships.This work presnets a complete analysis of information diffusion in Microblog from. The contributions and innovations of this thesis are as follows:1)There are two popular unbiased Online Social Network (OSN) sampling algorithms,Metropolis-Hastings Random Walk (MHRW) and Unbiased Sampling for Directed Social Graph (USDSG) method. However they are both likely to yield considerable self-sampling probabilities when applied to Microblogs where there is local. To solve this problem, I have modelled the process of OSN sampling as a Markov process and have deduced the sufficient and necessary conditions of unbiased sampling. Based on this unbiased conditions, I proposed an efficient and unbiased sampling algorithms, Unbiased Sampling method with Dummy Edges (USDE), which reduces strongly the self-sampling probabilities of MHRW. The experimental evaluation demonstrate thats the average node degree of samples of MHRW and USDSG is 2 - 4 times as high as the ground truth while USDE can provide the approximation of ground truth when the sampling repetitions are removed. Moreover the average sampling time per node in USDE is only a half of MHRW and USDSG one.2)A second contribution targets the shortages of Independent Cascades (IC) and Linear Threshold (LT) models in characterizing the retweeting process in Microblogs. I achieve this by introducing a Galton Watson with Killing (GWK) model which considers all the three important factors including the ephemerality of information, the topology of network and the features of publisher and retweeters accurately. We have validated the applicability of the of GWK model over two datasets from Sina Weibo and Twitter and showed that GWK model can fit 82% of information receivers and 90% of the maximum numbers of hops in the real retweeting process. Besides, the GWK model is useful for revealing the endogenous and exogenous factors which affect the popularity of tweets.3) Motivated by the correlation between popularity and trendiness of topicsin Microblog and search trends, I have developed an economic analysis of the market involving a third-party ad broker, which is a popular market in current SEM, and finds that the adwords augmenting strategy with the trending and popular topics in Twitter enables the broker to achieve, on average, four folds larger return on investment than with a non-augmented strategy, while still maintaining the same level of risk. ; Les services de microblogging (comme Twitter ou Sina Weibo) sont devenu ces dernières années des plateformes très importantes de partage d'information sur l'Internet. Les microblogs sont fréquemment utilisé pour l'analyse de l'opinion, le marketing viral, et les campagnes politiques. Comprendre les mécanismes sous-jacents de la diffusion d'information sur les microblogs et comment des contenus deviennent populaires est important.L'analyse de la diffusion d'information dans les microblogs nécessite la collecte de donnée des microblogs, la modélisation de la diffusion d'information et l'application des modèles résultants. Traiter les données massives issues des microblogs est un défi en soi. Concevoir des algorithmes efficaces et sans biais afin d'échantillonner les microblogs est ainsi fondamental. Ceci doit prendre en compte la complexité du phénomène de « retweet » qui dépend de la valeur éphémère de l'information, de la topologie du réseau de microblogging et des caractéristiques particulières des éditeurs et retweeteurs.Deux modèles ont été traditionnellement appliqués à la diffusion d'information : les cascades indépendantes et modèle à seuil linéaire. Aucun de ces deux modèles n'est à même de décrire le processus du retweeting de façon correcte. Il devient donc nécessaire de de caractériser la diffusion d'information. De plus, une description complète de la relation entre la diffusion d'information dans les microblogs et de popularité des termes recherchés sur Internet serait utile.Ces travaux de thèse présentent une analyse complète de la diffusion d'information dans les microblogs. Les contributions ce cette thèse sont les suivantes :1) Il y'a deux technique d'échantillonnage sans biais pour les réseaux sociaux : la marche aléatoire de Métropolis-Hastings (MHRW), et la méthode d'échantillonnage sans biais de graphes dirigés (USDSG). Néanmoins ces deux méthodes peuvent aboutit à un taux important d'auto-échantillonnage quand elles sont appliquées à des microblogs. Pour résoudre ce problème, j'ai modélisé l'échantillonnage d'un OSN par un processus de Markov et j'en ai déduit les conditions nécessaires et suffisantes d'un échantillonnage sans biais. Ces conditions m'ont permis de proposer un algorithme d'échantillonnage sans biais et efficace que j'ai nommé : échantillonnage sans biais par liens vide (USDE). Cette nouvelle méthode d'échantillonage réduit fortement l'auto-échantillonnage du MHRW. L 'évaluation empirique montre que la moyenne des dégrées des nœuds échantillonnés est proche de la vérité terrain alors que pour MHRW et USDSG elle est 2 à 4 fois supérieure.2) La seconde contribution de cette thèse vise les lacunes des modèles en cascades indépendantes et de seuils linéaires. J'ai développé un modèle fondé sur les processus de Galton-Watson avec mort (GWK) qui prennent en compte tous les facteurs importants du processus de retweet. Ce nouveau modèle est validé par une application sur des données issues de Twitter et de Weibo.3) La troisième contribution est relative au développement d'un modèle économique du marché des acteurs actifs dans le domaine du marketing sur les mots clés dans les sites de recherches. J'ai développé des méthodes de gestion de portfolios de mots clés et montrés que ces portfolios permettent d'améliorer fortement les rendements sans augmenter le niveau de risque.

Microblog service (such as Twitter and Sina Weibo) have become an important platform for Internet content sharing. As the information in Microblog are widely used in public opinion mining, viral marketing and political campaigns, understanding how information diffuses over Microblogs, and explaining the process through which some tweets become popular, are important.The analysis of the information diffusion in Microblogs involves the data collection from Microblog, the modeling on information spreading and using the resulting models. Dealing with the huge amount of data flowing through microblogs is by itself a challenge. Designing an efficient and unbiased sampling algorithm for Microblog is therefore essential. Besides, the retweeting process in Microblog is complex because of the ephemerality of information, the topology of Microblog network and the particular features (such as number of followers) of publisher and retweeters.Two traditional models have been used for information diffusion : Independent Cascades and Linear Threshold models. However no one of them can describe completely the retweeting process in Microblog accurately. The analysis and design of new models to characterize the information diffusion in Microblog is therefore necessary. Moreover, a comprehensive description of the correlation between the information diffusion in Microblog and the searching trends of keywords on search engines is lacking although some work has been found some preliminary relationships.This work presnets a complete analysis of information diffusion in Microblog from. The contributions and innovations of this thesis are as follows:1)There are two popular unbiased Online Social Network (OSN) sampling algorithms,Metropolis-Hastings Random Walk (MHRW) and Unbiased Sampling for Directed Social Graph (USDSG) method. However they are both likely to yield considerable self-sampling probabilities when applied to Microblogs where there is local. To solve this problem, I have modelled the process of OSN sampling as a Markov process and have deduced the sufficient and necessary conditions of unbiased sampling. Based on this unbiased conditions, I proposed an efficient and unbiased sampling algorithms, Unbiased Sampling method with Dummy Edges (USDE), which reduces strongly the self-sampling probabilities of MHRW. The experimental evaluation demonstrate thats the average node degree of samples of MHRW and USDSG is 2 - 4 times as high as the ground truth while USDE can provide the approximation of ground truth when the sampling repetitions are removed. Moreover the average sampling time per node in USDE is only a half of MHRW and USDSG one.2)A second contribution targets the shortages of Independent Cascades (IC) and Linear Threshold (LT) models in characterizing the retweeting process in Microblogs. I achieve this by introducing a Galton Watson with Killing (GWK) model which considers all the three important factors including the ephemerality of information, the topology of network and the features of publisher and retweeters accurately. We have validated the applicability of the of GWK model over two datasets from Sina Weibo and Twitter and showed that GWK model can fit 82% of information receivers and 90% of the maximum numbers of hops in the real retweeting process. Besides, the GWK model is useful for revealing the endogenous and exogenous factors which affect the popularity of tweets.3) Motivated by the correlation between popularity and trendiness of topicsin Microblog and search trends, I have developed an economic analysis of the market involving a third-party ad broker, which is a popular market in current SEM, and finds that the adwords augmenting strategy with the trending and popular topics in Twitter enables the broker to achieve, on average, four folds larger return on investment than with a non-augmented strategy, while still maintaining the same level of risk. ; Les services de microblogging (comme Twitter ou Sina Weibo) sont devenu ces dernières années des plateformes très importantes de partage d'information sur l'Internet. Les microblogs sont fréquemment utilisé pour l'analyse de l'opinion, le marketing viral, et les campagnes politiques. Comprendre les mécanismes sous-jacents de la diffusion d'information sur les microblogs et comment des contenus deviennent populaires est important.L'analyse de la diffusion d'information dans les microblogs nécessite la collecte de donnée des microblogs, la modélisation de la diffusion d'information et l'application des modèles résultants. Traiter les données massives issues des microblogs est un défi en soi. Concevoir des algorithmes efficaces et sans biais afin d'échantillonner les microblogs est ainsi fondamental. Ceci doit prendre en compte la complexité du phénomène de « retweet » qui dépend de la valeur éphémère de l'information, de la topologie du réseau de microblogging et des caractéristiques particulières des éditeurs et retweeteurs.Deux modèles ont été traditionnellement appliqués à la diffusion d'information : les cascades indépendantes et modèle à seuil linéaire. Aucun de ces deux modèles n'est à même de décrire le processus du retweeting de façon correcte. Il devient donc nécessaire de de caractériser la diffusion d'information. De plus, une description complète de la relation entre la diffusion d'information dans les microblogs et de popularité des termes recherchés sur Internet serait utile.Ces travaux de thèse présentent une analyse complète de la diffusion d'information dans les microblogs. Les contributions ce cette thèse sont les suivantes :1) Il y'a deux technique d'échantillonnage sans biais pour les réseaux sociaux : la marche aléatoire de Métropolis-Hastings (MHRW), et la méthode d'échantillonnage sans biais de graphes dirigés (USDSG). Néanmoins ces deux méthodes peuvent aboutit à un taux important d'auto-échantillonnage quand elles sont appliquées à des microblogs. Pour résoudre ce problème, j'ai modélisé l'échantillonnage d'un OSN par un processus de Markov et j'en ai déduit les conditions nécessaires et suffisantes d'un échantillonnage sans biais. Ces conditions m'ont permis de proposer un algorithme d'échantillonnage sans biais et efficace que j'ai nommé : échantillonnage sans biais par liens vide (USDE). Cette nouvelle méthode d'échantillonage réduit fortement l'auto-échantillonnage du MHRW. L 'évaluation empirique montre que la moyenne des dégrées des nœuds échantillonnés est proche de la vérité terrain alors que pour MHRW et USDSG elle est 2 à 4 fois supérieure.2) La seconde contribution de cette thèse vise les lacunes des modèles en cascades indépendantes et de seuils linéaires. J'ai développé un modèle fondé sur les processus de Galton-Watson avec mort (GWK) qui prennent en compte tous les facteurs importants du processus de retweet. Ce nouveau modèle est validé par une application sur des données issues de Twitter et de Weibo.3) La troisième contribution est relative au développement d'un modèle économique du marché des acteurs actifs dans le domaine du marketing sur les mots clés dans les sites de recherches. J'ai développé des méthodes de gestion de portfolios de mots clés et montrés que ces portfolios permettent d'améliorer fortement les rendements sans augmenter le niveau de risque.

Inspired by a European project, PHEME, that requires the close analysis of Twitter-based conversations in order to look at the spread of rumors via social media, this article has two objectives. The first of these is to take the analysis of microblogs back to first principles and lay out what microblog analysis should look like as a foundational program of work. The other is to describe how this is of fundamental relevance to human-computer interaction's interest in grasping the constitution of people's interactions with technology within the social order. Our critical finding is that, despite some surface similarities, Twitter-based conversations are a wholly distinct social phenomenon requiring an independent analysis that treats them as unique phenomena in their own right, rather than as another species of conversation that can be handled within the framework of existing conversation analysis. This motivates the argument that microblog analysis be established as a foundationally independent program, examining the organizational characteristics of microblogging from the ground up. We articulate how aspects of this approach have already begun to shape our design activities within the PHEME project.

China's weibo community numbered more than 300 million users in 2013. This article assesses the rise and influence of microblogs from political, social, and commercial aspects. It examines ways the Chinese government has controlled microblogs, considers newer competing forms of communication, and assesses trends in Chinese digital discourse.