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.

Sentimentalizer: Docker container utility over Cloud

Abstract: In this computer era, the most interesting thing is to determine the human opinion using machines. Humans use opinions for conveying their response on a host of things to others. With the increasing popularity and availability of enriching opinion mediums such as personal blogs, forum discussions, online review sites, and micro blogging sites like Twitter, there are new challenges and opportunities for using this information to understand and analyze the sentiments of others. However, web texts usually seem noisy and represent significant issues at the lexical as well as the syntactic level. In this paper, lightweight Docker container is employed over cloud as a utility for sentiment analysis using the four popular classification approaches. It analyzes the reviewer's comment on a product across multiple websites. The analyzed information can be used as a recommendation for the product to a customer. The evaluation process on NLTK benchmark movie review dataset is performed with accuracy, computational cost and resources utilization. The computational analysis shows that our proposed approach meets the requirements of the real time applications over Cloud.

Micro-blogging practices in Canadian public libraries: A national snapshot

Abstract: This paper reports the first phase of a study examining Canada’s public library sector’s micro-blogging practices. Results of a national survey of public library (n=71) Twitter practices and an analysis of Twitter followers from one library are presented. Five themes are proposed as a future research agenda: community size and population density; managing the public library’s Twitter voice; the potential for controversy; the library as the community’s daily digest; and the network effects of community building. As the first national study, a contribution is made to advancing research on micro-blogging from the public library’s distinct institutional perspective.

Opinion Mining and Sentiment Analysis in Social Networks: A Retweeting Structure-Aware Approach

Abstract: Microblogs have become quick and easy online information sharing platforms with the explosive growth of online social media. Weibo, a Twitter-like microblog service in China, is characterized by timeliness and interactivity. A Weibo message carries the user's views and sentiments, particularly forms a fission-like spreading structure while being retweeted. Such structure accelerates information diffusion, and reflects different topics and opinions as well. However, current researches mainly focus on sentiment classification, which neither efficiently combine tree-like retweeting structure nor analyze opinion evolutions with a holistic view. In light of this, we build an opinion descriptive model, and propose an opinion mining method based on this model. With a microblog-oriented sentiment lexicon being constructed, a lexicon-based sentiment orientation analysis algorithm is designed to classify sentiments. Finally, we design and implement a prototype which can mine opinions with respect to retweeting tree structures and retweeting comments.

Exploiting user-to-user topic inclusion degree for link prediction in social-information networks

Abstract: As one kind of typical network big data, social-information networks (such as Weibo and Twitter) include both the complex network structure among users and the rich microblog/tweets information published by users. Understanding the interplay of rich content and social relationships is potentially valuable to the fundamental network mining task, i.e. the link prediction. Although some of the link prediction methods have been proposed by combining topological and non-topological information simultaneously, the in-depth analysis of the rich content still being in a minority, and the rich content in the social-information networks is still underused in solving link prediction. In this paper, we approach the link prediction problem in social-information network by combining network structure and topic information which is extracted from users’ rich content. We first define a kind of user-to-user topic inclusion degree (TID) based on the dissemination mechanism of the published content in the social-information networks, and then construct a TID-based sparse network. On the basis, we build a fusion probabilistic matrix factorization model which solves the link prediction problem by fusing the information of the original following/followed network and the TID-based network in a unified probabilistic matrix factorization framework. We conduct link prediction experiments on two types of real social-information network datasets, i.e. Twitter and Weibo. The experimental results demonstrate that the proposed method is more effective in solving the link prediction problem in social-information networks.

The Effect of Mobile Platforms on Twitter Content Generation

Abstract: The increased popularity of feature-rich mobile devices in recent years has enabled widespread consumption and production of social media content via mobile devices. Because mobile devices and mobile applications change context within which an individual generates and consumes microblog content, we might expect microblogging behavior to differ depending on whether the user is using a mobile device. To our knowledge, little has been established about what, if any, effects such mobile interfaces have on microblogging. In this paper, we investigate this question within the context of Twitter, among the most popular microblogging platforms. This work makes three specific contributions. First, we quantify the ways in which user profiles are effected by the mobile context: (1) the extent to which users tend to be either fully non-mobile or mobile and (2) the relative activity of the mobile Twitter community. Second, we assess the differences in content between mobile and non-mobile tweets (posts to the Twitter platform). Our results show that mobile platforms produce very different patterns of Twitter usage. As part of our analysis, we propose and apply a classification system for tweets. We consider this to be the third contribution of this work. While other classification systems have been proposed, ours is the first to permit the independent encoding of a tweet’s form, content, and intended audience. In this paper we apply this system to show how tweets differ between mobile and non-mobile contexts. However, because of its flexibility and breadth, the schema may be useful to researchers studying Twitter content in other contexts as well.