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.

Exploring urban characteristics using movement history of mass mobile microbloggers

Abstract: The explosive growth of the smartphones market and social network sites such as the Twitter has enabled to share numerous short messages reflecting our daily lives and social events from outside their homes or offices Compared to conventional blogging sites, micro-blogging sites through mobile network enable us to easily write and share their daily logs without any spatial or temporal restrictions Such mass geo-tagged and time-stamped micro-blogs can inform us about social patterns, regardless of their scale, time, or significance We investigate characteristic patterns in urban areas such as populated areas from the movement histories of mass mobile micro-bloggers In particular, some interesting movement patterns can be frequently observed in urban areas using our two measures such as aggregation and dispersion We also present experimental results in determining urban characteristics from actual micro-blog dataset from the Twitter

Geoparsing and Geosemantics for Social Media: Spatiotemporal Grounding of Content Propagating Rumors to Support Trust and Veracity Analysis during Breaking News

Abstract: In recent years, there has been a growing trend to use publicly available social media sources within the field of journalism. Breaking news has tight reporting deadlines, measured in minutes not days, but content must still be checked and rumors verified. As such, journalists are looking at automated content analysis to prefilter large volumes of social media content prior to manual verification. This article describes a real-time social media analytics framework for journalists. We extend our previously published geoparsing approach to improve its scalability and efficiency. We develop and evaluate a novel approach to geosemantic feature extraction, classifying evidence in terms of situatedness, timeliness, confirmation, and validity. Our approach works for new unseen news topics. We report results from four experiments using five Twitter datasets crawled during different English-language news events. One of our datasets is the standard TREC 2012 microblog corpus. Our classification results are promising, with F1 scores varying by class from 0.64 to 0.92 for unseen event types. We lastly report results from two case studies during real-world news stories, showcasing different ways our system can assist journalists filter and cross-check content as they examine the trust and veracity of content and sources.

A Temporal-Topic Model for Friend Recommendations in Chinese Microblogging Systems

Abstract: Due to its brief form and growing popularity, microblogging is becoming people’s favorite choice for seeking information and expressing opinions. Messages received by a user mainly depend on whom the user follows. Thus, recommending users with similar interests may improve the experience quality for information receiving. Since messages posted by microblogging users reflect their interests, and the keywords in the messages indicate their main focus to a large extent, we can discover users’ preferences by analyzing the user-generated contents. Moreover, users’ interests are not static, on the contrary, they change as time goes by. Based on such intuitions, in this paper, we propose a temporal-topic model to analyze users’ possible behaviors and predict their potential friends in microblogging. The model learns users’ latent preferences by extracting keywords on aggregated messages over a period of time via a topic model, and then the impact of time is considered to deal with interest drifts. The experimental results of friend recommendations on Sina Weibo, one of the most popular microblogging sites in China, have demonstrated the effectiveness of our model.

Peers and Sources as Social Capital in the Production of News: Online Social Networks as Communities of Journalists

Abstract: In a very short time span, Twitter has become a major force in modern societies and also in the production of news by journalists. How journalists use Twitter is studied extensively, particularly on a small scale i.e., qualitative research, specific events, mostly descriptive. However, studies on how Twitter has impacted journalism as a whole are relatively scarce. This study focuses on the adoption of Twitter and its emerging community network structure in the Netherlands. Using the social network data of 2,152 journalists as retrieved from Twitter, analysis shows that the social network among journalists is well connected. The journalists who are extremely popular are also able to influence the flow of information through the network more than others cf. gatekeeper role. Still, even though gatekeeping positions in the network are present due to the absence of specific relations, and the network consists of eight tightly knit network communities, the entire network is very well connected. The adoption of Twitter as a microblogging and networking service over time indicated that adoption increased particularly in early 2009. The possible consequences of these tightly knit communities for the production of news are discussed in terms of pack journalism, echo chambers, and information cascades.

Microblog sentiment analysis using social and topic context.

Abstract: Analyzing massive user-generated microblogs is very crucial in many fields, attracting many researchers to study. However, it is very challenging to process such noisy and short microblogs. Most prior works only use texts to identify sentiment polarity and assume that microblogs are independent and identically distributed, which ignore microblogs are networked data. Therefore, their performance is not usually satisfactory. Inspired by two sociological theories (sentimental consistency and emotional contagion), in this paper, we propose a new method combining social context and topic context to analyze microblog sentiment. In particular, different from previous work using direct user relations, we introduce structure similarity context into social contexts and propose a method to measure structure similarity. In addition, we also introduce topic context to model the semantic relations between microblogs. Social context and topic context are combined by the Laplacian matrix of the graph built by these contexts and Laplacian regularization are added into the microblog sentiment analysis model. Experimental results on two real Twitter datasets demonstrate that our proposed model can outperform baseline methods consistently and significantly.