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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.


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Journal ArticleDOI
TL;DR: Feeley et al. as mentioned in this paper examined the Twitter utilization practices of the 100 largest nonprofit organizations in the United States and found that there are three key functions of microblogging updates: information, community, and action.
Abstract: The rapid diffusion of “microblogging” services such as Twitter is ushering in a new era of possibilities for organizations to communicate with and engage their core stakeholders and the general public. To enhance understanding of the communicative functions microblogging serves for organizations, this study examines the Twitter utilization practices of the 100 largest nonprofit organizations in the United States. The analysis reveals there are three key functions of microblogging updates—“information,”“community,” and “action.” Though the informational use of microblogging is extensive, nonprofit organizations are better at using Twitter to strategically engage their stakeholders via dialogic and community-building practices than they have been with traditional websites. The adoption of social media appears to have engendered new paradigms of public engagement. © 2012 Wiley Periodicals, Inc. (Authors are listed in alphabetical order. The authors would like to thank Tom Feeley, Richard Waters, Seungahn Nah, I-hsuan Chiu, Yuchao Huang, and Kenton Anderson for helpful comments and suggestions.)

571 citations

Journal ArticleDOI
TL;DR: This analysis of one person’s Twitter network shows that it is the basis for a real community, even though Twitter was not designed to support the development of online communities.
Abstract: The notion of “community” has often been caught between concrete social relationships and imagined sets of people perceived to be similar. The rise of the Internet has refocused our attention on this ongoing tension. The Internet has enabled people who know each other to use social media, from e-mail to Facebook, to interact without meeting physically. Into this mix came Twitter, an asymmetric microblogging service: If you follow me, I do not have to follow you. This means that connections on Twitter depend less on in-person contact, as many users have more followers than they know. Yet there is a possibility that Twitter can form the basis of interlinked personal communities—and even of a sense of community. This analysis of one person’s Twitter network shows that it is the basis for a real community, even though Twitter was not designed to support the development of online communities. Studying Twitter is useful for understanding how people use new communication technologies to form new social connections and maintain existing ones.

567 citations

Journal ArticleDOI
03 Aug 2011-PLOS ONE
TL;DR: The ‘economy of attention’ is limited in the online world by cognitive and biological constraints as predicted by Dunbar's theory and a simple model for users' behavior that includes finite priority queuing and time resources is proposed that reproduces the observed social behavior.
Abstract: Microblogging and mobile devices appear to augment human social capabilities, which raises the question whether they remove cognitive or biological constraints on human communication. In this paper we analyze a dataset of Twitter conversations collected across six months involving 1.7 million individuals and test the theoretical cognitive limit on the number of stable social relationships known as Dunbar's number. We find that the data are in agreement with Dunbar's result; users can entertain a maximum of 100–200 stable relationships. Thus, the ‘economy of attention’ is limited in the online world by cognitive and biological constraints as predicted by Dunbar's theory. We propose a simple model for users' behavior that includes finite priority queuing and time resources that reproduces the observed social behavior.

553 citations

Proceedings ArticleDOI
17 Oct 2015
TL;DR: A novel approach to capture the temporal characteristics of features related to microblog contents, users and propagation patterns based on the time series of rumor's lifecycle, for which time series modeling technique is applied to incorporate various social context information.
Abstract: Automatically identifying rumors from online social media especially microblogging websites is an important research issue. Most of existing work for rumor detection focuses on modeling features related to microblog contents, users and propagation patterns, but ignore the importance of the variation of these social context features during the message propagation over time. In this study, we propose a novel approach to capture the temporal characteristics of these features based on the time series of rumor's lifecycle, for which time series modeling technique is applied to incorporate various social context information. Our experiments using the events in two microblog datasets confirm that the method outperforms state-of-the-art rumor detection approaches by large margins. Moreover, our model demonstrates strong performance on detecting rumors at early stage after their initial broadcast.

514 citations

Proceedings ArticleDOI
13 Apr 2015
TL;DR: A graph-kernel based hybrid SVM classifier which captures the high-order propagation patterns in addition to semantic features such as topics and sentiments and is 88% confident in detecting an average false rumor just 24 hours after the initial broadcast.
Abstract: This paper studies the problem of automatic detection of false rumors on Sina Weibo, the popular Chinese microblogging social network. Traditional feature-based approaches extract features from the false rumor message, its author, as well as the statistics of its responses to form a flat feature vector. This ignores the propagation structure of the messages and has not achieved very good results. We propose a graph-kernel based hybrid SVM classifier which captures the high-order propagation patterns in addition to semantic features such as topics and sentiments. The new model achieves a classification accuracy of 91.3% on randomly selected Weibo dataset, significantly higher than state-of-the-art approaches. Moreover, our approach can be applied at the early stage of rumor propagation and is 88% confident in detecting an average false rumor just 24 hours after the initial broadcast.

507 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023202
2022551
2021153
2020238
2019226
2018282