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.

Harnessing Semantic Features for Large-Scale Content-Based Hashtag Recommendations on Microblogging Platforms

Abstract: Search engines and social networks are two entirely different data sources that can provide valuable information about Influenza. While search engine hosts can deliver popular queries or terms used for searching the Influenza related information, the social networks contain useful links of information sources that people have found valuable. The authors hypothesize that such data sources can provide vital first-hand information. In this article, they have proposed a methodology for detecting the information sources from social networks, particularly Twitter. The data filtering and source finding tasks are posed as classification tasks. Search engine queries are used for extracting related dataset. Results have shown that propose approach can be beneficial for extracting useful information regarding side effects, medications and to track geographical location of epidemics affected area.

Using twitter as an instructional tool: a case study in higher education

Abstract: The implementation of Web 2.0 technologies and related research studies are in their early stages. Therefore, this study addresses the utilization of the most commonly used microblogging website, Twitter, in a higher education course. Study participants (n = 48) filled out a quantitative survey before, during, and after participating in a course that utilized Twitter as an instructional tool. At the end of the course, the participants responded to eight open-ended questions about the process and idea of using Twitter. The study result showed that the participants increased their uses of Twitter for learning purposes. The participants’ ideas of using Twitter as a teaching or learning tool progressed during the course. They also remarked about the possible negative effects of Twitter in instruction.

TRCM: a methodology for temporal analysis of evolving concepts in Twitter

Abstract: The Twitter network has been labelled the most commonly used microblogging application around today With about 500 million estimated registered users as of June, 2012, Twitter has become a credible medium of sentiment/opinion expression It is also a notable medium for information dissemination; including breaking news on diverse issues since it was launched in 2007 Many organisations, individuals and even government bodies follow activities on the network in order to obtain knowledge on how their audience reacts to tweets that affect them We can use postings on Twitter (known as tweets) to analyse patterns associated with events by detecting the dynamics of the tweets A common way of labelling a tweet is by including a number of hashtags that describe its contents Association Rule Mining can find the likelihood of co-occurrence of hashtags In this paper, we propose the use of temporal Association Rule Mining to detect rule dynamics, and consequently dynamics of tweets We coined our methodology Transaction-based Rule Change Mining (TRCM) A number of patterns are identifiable in these rule dynamics including, new rules, emerging rules, unexpected rules and ‘dead’ rules Also the linkage between the different types of rule dynamics is investigated experimentally in this paper

Microblog Analysis as a Program of Work

Abstract: 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.

ActNeT: Active learning for networked texts in microblogging

Abstract: Supervised learning, e.g., classification, plays an important role in processing and organizing microblogging data. In microblogging, it is easy to mass vast quantities of unlabeled data, but would be costly to obtain labels, which are essential for supervised learning algorithms. In order to reduce the labeling cost, active learning is an effective way to select representative and informative instances to query for labels for improving the learned model. Different from traditional data in which the instances are assumed to be independent and identically distributed (i.i.d.), instances in microblogging are networked with each other. This presents both opportunities and challenges for applying active learning to microblogging data. Inspired by social correlation theories, we investigate whether social relations can help perform effective active learning on networked data. In this paper, we propose a novel Active learning framework for the classification of Networked Texts in microblogging (ActNeT). In particular, we study how to incorporate network information into text content modeling, and design strategies to select the most representative and informative instances from microblogging for labeling by taking advantage of social network structure. Experimental results on Twitter datasets show the benefit of incorporating network information in active learning and that the proposed framework outperforms existing state-of-the-art methods.