Topic
Microblogging
About: Microblogging is a research topic. Over the lifetime, 4186 publications have been published within this topic receiving 137030 citations. The topic is also known as: microblog.
Papers published on a yearly basis
Papers
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20 Aug 2010TL;DR: It is shown that two structural characteristics, transitivity and mutuality, are significant predictors of the desire to form new ties, especially in online networks.
Abstract: New ties are often formed between people who already have friends in common Though the social sciences have addressed the effects of existing structural patterns on the formation of new ties, less attention has been given to ties in directed networks Drawing from the microblogging service Twitter, we conducted a web-based experiment in which subjects were asked to rate their interest in forming ties to other people, blind to existing network connections between them We show that two structural characteristics, transitivity and mutuality, are significant predictors of the desire to form new ties Our findings shed light on tie formation, especially in online networks
140 citations
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01 Jul 2010TL;DR: The results showed that content gratifications and new technology gratification are the two key types of gratifications affecting the continuance intention to use Twitter.
Abstract: In this study, we proposed a research model to investigate the factors influencing users’ continuance intention to use Twitter. Building on the uses and gratification framework, we have proposed four types of gratifications for Twitter usage, including content gratification, technology gratification, process gratification, and social gratification. We conducted an online survey and collected 124 responses. The data was analyzed using Partial Least Squares. Our results showed that content gratifications and new technology gratification are the two key types of gratifications affecting the continuance intention to use Twitter. We conclude with a discussion of theoretical and practical implications. We believe that this study will provide important insights for future research on Twitter.
139 citations
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TL;DR: ScatterBlogs2 is suggested, a new approach to let analysts build task-tailored message filters in an interactive and visual manner based on recorded messages of well-understood previous events that can be orchestrated and adapted afterwards for interactive, visual real-time monitoring and analysis of microblog feeds.
Abstract: The number of microblog posts published daily has reached a level that hampers the effective retrieval of relevant messages, and the amount of information conveyed through services such as Twitter is still increasing. Analysts require new methods for monitoring their topic of interest, dealing with the data volume and its dynamic nature. It is of particular importance to provide situational awareness for decision making in time-critical tasks. Current tools for monitoring microblogs typically filter messages based on user-defined keyword queries and metadata restrictions. Used on their own, such methods can have drawbacks with respect to filter accuracy and adaptability to changes in trends and topic structure. We suggest ScatterBlogs2, a new approach to let analysts build task-tailored message filters in an interactive and visual manner based on recorded messages of well-understood previous events. These message filters include supervised classification and query creation backed by the statistical distribution of terms and their co-occurrences. The created filter methods can be orchestrated and adapted afterwards for interactive, visual real-time monitoring and analysis of microblog feeds. We demonstrate the feasibility of our approach for analyzing the Twitter stream in emergency management scenarios.
139 citations
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TL;DR: The study finds that a majority of messages in government microblog accounts were posted for self-promotion rather than service delivery, and a longitudinal comparison between data in two sequential years indicates that government use of microblogs is improved over time.
138 citations
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TL;DR: This paper investigates machine-learning-based rumor identification schemes by applying five new features based on users' behaviors, and combines the new features with the existing well-proved effective user behavior-based features to predict whether a microblog post is a rumor.
Abstract: In recent years, microblog systems such as Twitter and Sina Weibo have averaged multimillion active users. On the other hand, the microblog system has become a new means of rumor-spreading platform. In this paper, we investigate the machine-learning-based rumor identification approaches. We observed that feature design and selection has a stronger impact on the rumor identification accuracy than the selection of machine-learning algorithms. Meanwhile, the rumor publishers' behavior may diverge from normal users', and a rumor post may have different responses from a normal post. However, mass behavior on rumor posts has not been explored adequately. Hence, we investigate rumor identification schemes by applying five new features based on users' behaviors, and combine the new features with the existing well-proved effective user behavior-based features, such as followers' comments and reposting, to predict whether a microblog post is a rumor. Experiment results on real-world data from Sina Weibo demonstrate the efficacy and efficiency of our proposed method and features. From the experiments, we conclude that the rumor detection based on mass behaviors is more effective than the detection based on microblogs' inherent features.
138 citations