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.

Aging 2.0: health information about dementia on Twitter.

Abstract: Online social media is widespread, easily accessible and attracts a global audience with a widening demographic. As a large proportion of adults now seek health information online and through social media applications, communication about health has become increasingly interactive and dynamic. Online health information has the potential to significantly impact public health, especially as the population gets older and the prevalence of dementia increases. However, little is known about how information pertaining to age-associated diseases is disseminated on popular social media platforms. To fill this knowledge gap, we examined empirically: (i) who is using social media to share information about dementia, (ii) what sources of information about dementia are promoted, and (iii) which dementia themes dominate the discussion. We data-mined the microblogging platform Twitter for content containing dementia-related keywords for a period of 24 hours and retrieved over 9,200 tweets. A coding guide was developed and content analysis conducted on a random sample (10%), and on a subsample from top users’ tweets to assess impact. We found that a majority of tweets contained a link to a third party site rather than personal information, and these links redirected mainly to news sites and health information sites. As well, a large number of tweets discussed recent research findings related to the prediction and risk management of Alzheimer’s disease. The results highlight the need for the dementia research community to harness the reach of this medium and its potential as a tool for multidirectional engagement.

"Beacons of hope" in decentralized coordination: learning from on-the-ground medical twitterers during the 2010 Haiti earthquake

Abstract: We examine the public, social media communications of 110 emergency medical response teams and organizations in the immediate aftermath of the January 12, 2010 Haiti earthquake. We found the teams through an inductive analysis of Twitter communications acquired over the three-week emergency period from 89,114 Twitterers. We then analyzed the teams' Twitter streams, as well as all digital media they generated and pointed to in their streams - blog posts, photographs, videos, status updates and field reports - to understand the medical coordination challenges they faced from pre-deployment readiness to on-the-ground action. Here we identify opportunities for improving coordination in a decentralized and distributed environment where staffing, disease trajectories, and other circumstances rapidly change. We extrapolate from these findings to theorize about how "beaconing" behavior is a sign of latent potential for coordination upon which mechanisms of coordination can capitalize.

Towards effective event detection, tracking and summarization on microblog data

Abstract: Microblogging has become one of the most popular social Web applications in recent years. Posting short messages (i.e., a maximum of 140 characters) to the Web at any time and at any place lowers the usage barrier, accelerates the information diffusion process, and makes it possible for instant publication. Among those daily userpublished posts, many are related to recent or real-time events occurring in our daily life. While microblog sites usually display a list of words representing the trend topics during a time period (e.g., 24 hours, a week or even longer) on their homepages, the topical words do not make any sense to let the users have a comprehensive view of the topic, especially for those without any background knowledge. Additionally, users can only open each post in the relevant list to learn the topic details. In this paper, we propose a unified workflow of event detection, tracking and summarization on microblog data. Particularly, we introduce novel features considering the characteristics of microblog data for topical words selection, and thus for event detection. In the tracking phase, a bipartite graph is constructed to capture the relationship between two events occurring at adjacent time. The matched event pair is grouped into an event chain. Furthermore, inspired by diversity theory in Web search, we are the first to summarize event chains by considering the content coverage and evolution over time. The experimental results show the effectiveness of our approach on microblog data.

The Velocity of Censorship: High-Fidelity Detection of Microblog Post Deletions

Abstract: Weibo and other popular Chinese microblogging sites are well known for exercising internal censorship, to comply with Chinese government requirements. This research seeks to quantify the mechanisms of this censorship: how fast and how comprehensively posts are deleted.Our analysis considered 2.38 million posts gathered over roughly two months in 2012, with our attention focused on repeatedly visiting "sensitive" users. This gives us a view of censorship events within minutes of their occurrence, albeit at a cost of our data no longer representing a random sample of the general Weibo population. We also have a larger 470 million post sampling from Weibo's public timeline, taken over a longer time period, that is more representative of a random sample. We found that deletions happen most heavily in the first hour after a post has been submitted. Focusing on original posts, not reposts/retweets, we observed that nearly 30% of the total deletion events occur within 5- 30 minutes. Nearly 90% of the deletions happen within the first 24 hours. Leveraging our data, we also considered a variety of hypotheses about the mechanisms used by Weibo for censorship, such as the extent to which Weibo's censors use retrospective keyword-based censorship, and how repost/retweet popularity interacts with censorship. We also used natural language processing techniques to analyze which topics were more likely to be censored.

Twitter Sentiment Analysis Using Machine Learning Techniques

Abstract: Twitter is a microblogging site in which users can post updates (tweets) to friends (followers). It has become an immense dataset of the so-called sentiments. In this paper, we introduce an approach to selection of a new feature set based on Information Gain, Bigram, Object-oriented extraction methods in sentiment analysis on social networking side. In addition, we also proposes a sentiment analysis model based on Naive Bayes and Support Vector Machine. Its purpose is to analyze sentiment more effectively. This model proved to be highly effective and accurate on the analysis of feelings.