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.

Medical Journals in the Age of Ubiquitous Social Media.

Abstract: Medical journals increasingly use social media to engage their audiences in a variety of ways, from simply broadcasting content via blogs, microblogs, and podcasts to more interactive methods such as Twitter chats and online journal clubs. Online discussion may increase readership and help improve peer review, for example, by providing postpublication peer review. Challenges remain, including the loss of nuance and context of shared work. Furthermore, uncertainty remains regarding how to assess the impact of journal social media outreach, abundant but unclear metrics, and the magnitude of benefit (if any), particularly given the substantial work required for substantive interactive engagement. Continued involvement and innovation from medical journals through social media offers potential in engaging journal audiences and improving knowledge translation.

Twittering for earth: a study on the impact of microblogging activism on earth hour 2009 in Australia

Abstract: The role of Twitter - a form of microblogging - as both influencer and reflector of real-world events is fast emerging in today's world of Web 2.0 and social media. In this investigation, we survey how the use of Twitter in Australia is linked to the real-world success of the Earth Hour 2009 campaign. The results of this research will give us an idea of the emergence of microblogging as a new medium of influencing human behavior and providing a source of collective intelligence in planning and decision making, specifically in the Australian context. We found that, from our observations, there is a correlation between the inter-state total energy reduction during this campaign with the amount of interstate online Twitter discussion. We also identified a link between the Twitter discussion frequency and the total real-life population of the locale in which the chatter takes place, which could be used as a yardstick to analyze the reach of online technologies in the real world.

Social Web Data Analytics: Relevance, Redundancy, Diversity

Abstract: In the past decade, the Social Web has evolved into both an essential channel for people to exchange information and a new type of mass media. The immense amount of data produced presents new possibilities and challenges: algorithms and technologies need to be developed to extract and infer useful information from the Social Web. One of the main issues on the (Social) Web is the impurity of the data – not all content produced is meaningful or useful. (1) How can we predict the relevance of messages on the Social Web to users’information needs? (2) How can we reduce the redundancy among a list of messages retrieved in response to a user query? (3) How can we boost the diversity of such a ranked list in order to provide a more comprehensive coverage of the aspects pertinent to an information need? In this thesis, we answer these questions through Social Web data analytics on microblog data. The first part of the thesis introduces the Twitter Analytical Platform (TAP), which is an analytical platform for Twitter data. It aims at providing an easy-to-use platform for data scientists and software developers to efficiently conduct analytical tasks. The tasks can be customized with the Twitter Analysis Language (TAL), which is a language for designing data analytical workflows. In order to conduct the research presented in this thesis, a number of tools and components were implemented in TAP and are broadly applicable to typical Social Web analytics use cases. Taking search on Twitter as one of the main use cases for this research, the second part of the thesis presents our results for answering the aforementioned three questions. We first propose a query expansion framework that utilizes information from external knowledge bases. We integrate our research findings into a relevance estimation framework, which aims at analyzing the importance of different tweet-based features in predicting their relevance to an information need. Our second contribution is based on the insight that microblog search result rankings often contain a considerable amount of redundancy. We propose a near-duplicate detection framework designed to tackle this issue. Since a reduction in redundancy does not necessarily lead to increased diversity in the search result ranking, we also build a corpus specifically to investigate issues of novelty and diversity. Finally, we put the analytical results derived from investigating relevance, redundancy and diversity into practice and introduce Twinder, a search engine for Twitter streams. Twinder demonstrates the applicability of both our analytical platform TAP as well as our analytical findings. Inspired by real-life use cases, the last part of the thesis focuses on the development of Twitcident, an application aimed at fulfilling the information need from (semi-)public sectors during emergency or potentially dangerous circumstances. Based on TAP, we develop an interface of semantic-based faceted search and multiple widgets of visualized analytics for Twitcident. These components allow users to explore Twitter messages more efficiently. The application and the evaluation results show the validity of TAP as well as the effectiveness of exploiting semantics for filtering Twitter messages.

Public Policy Formulation through Non Moderated Crowdsourcing in Social Media

Abstract: The emergence of web 2.0 social media enables the gradual emergence of a second generation of e-participation characterized by more citizens’ control, in which government agencies post content (e.g. short or longer text, images, video) to various social media and then analyze citizens’ interactions with it (e.g. views, likes/dislikes, comments, etc.). In this paper we propose an even more citizens controlled third generation of e-participation exploiting web 2.0 social media as well, but in a different manner. It is based on the search by government agencies for content on a public policy under formulation, which has been created in a large set of web 2.0 sources (e.g. blogs and microblogs, news sharing sites, online forums) by citizens freely, without any initiation, stimulation or moderation through government postings. This content undergoes advanced processing in order to extract from it arguments, opinions, issues and proposals on the particular policy, identify their sentiments (positive or negative), and finally summarize and visualize them. This approach allows the exploitation of the vast amount of user-generated content created in numerous web 2.0 social media for supporting governments to understand better the needs, wishes and beliefs of citizens, and create better and more socially rooted policies.

Exploring academic libraries’ use of Twitter: a content analysis

Abstract: Purpose – This research aims to analyze academic libraries’ Twitter content and present a categorization framework for the study of their tweets. Design/methodology/approach – The research adopted a statistical descriptive analysis in addition to a content analysis of the tweets. Consequently, many categories and subcategories were created to classify the tweets according to different aspects. A total of 17 academic library accounts were examined. Findings – The findings show that academic libraries used Twitter as a multifaceted tool. “News and announcements” received the highest score as the type of information most often posted on Twitter by libraries, followed by “library collections” and “library services”. The subcategories that received the highest scores were “library marketing and news”, “answers and referrals” and “books”. Academic libraries showed a penchant for posting links more often than other content. Other results show different patterns of communication and interaction between libraries ...