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.

A microblogging-based approach to terrorism informatics: Exploration and chronicling civilian sentiment and response to terrorism events via Twitter

Abstract: The study of terrorism informatics utilizing the Twitter microblogging service has not been given apt attention in the past few years. Twitter has been identified as both a potential facilitator and also a powerful deterrent to terrorism. Based on observations of Twitter's role in civilian response during the recent 2009 Jakarta and Mumbai terrorist attacks, we propose a structured framework to harvest civilian sentiment and response on Twitter during terrorism scenarios. Coupled with intelligent data mining, visualization, and filtering methods, this data can be collated into a knowledge base that would be of great utility to decision-makers and the authorities for rapid response and monitoring during such scenarios. Using synthetic experimental data, we demonstrated that the proposed framework has yielded meaningful graphical visualizations of information, to reveal potential response to terrorist threats. The novelty of this study is that microblogging has never been studied in the domain of terrorism informatics. This paper also contributes to the understanding of the capability of conjoint structured data and unstructured content mining in extracting deep knowledge from noisy twitter messages, through our proposed structured framework.

Cognos: crowdsourcing search for topic experts in microblogs

Abstract: Finding topic experts on microblogging sites with millions of users, such as Twitter, is a hard and challenging problem. In this paper, we propose and investigate a new methodology for discovering topic experts in the popular Twitter social network. Our methodology relies on the wisdom of the Twitter crowds -- it leverages Twitter Lists, which are often carefully curated by individual users to include experts on topics that interest them and whose meta-data (List names and descriptions) provides valuable semantic cues to the experts' domain of expertise. We mined List information to build Cognos, a system for finding topic experts in Twitter. Detailed experimental evaluation based on a real-world deployment shows that: (a) Cognos infers a user's expertise more accurately and comprehensively than state-of-the-art systems that rely on the user's bio or tweet content, (b) Cognos scales well due to built-in mechanisms to efficiently update its experts' database with new users, and (c) Despite relying only on a single feature, namely crowdsourced Lists, Cognos yields results comparable to, if not better than, those given by the official Twitter experts search engine for a wide range of queries in user tests. Our study highlights Lists as a potentially valuable source of information for future content or expert search systems in Twitter.

Sentiment Analysis Of Twitter Data

Abstract: Twitter is a popular micro blogging service where users create status messages or small text-based Web posts called tweets. Twitter currently receives in excess of340 million tweets a day, in which people share their comments regarding a wide range of topics. A large number of tweets include opinions about numerous subjects. Analyzing these tweets to extract opinions or sentiments help us determine the popularity of the subjects. This paper talks about a sentiment analyzer engine that can be used to analyze tweets. Tweets retrieved real time are classified as to belonging to one of positive, negative or neutral category using pre classified tweets as training data. The paper discusses about Naive Bayes algorithm for implementing the sentiment analyzer engine. The Sentiment analyzer engine developed can give an approximate estimation of the success or popularity of a subject. The algorithm's efficiency is mainly dependent on the quality of the training data, for the training data chosen for this project we obtained an accuracy of close to 42% with precision and recall standing out at 45.65% and 67.74% respectively.

Using topic models for Twitter hashtag recommendation

Abstract: Since the introduction of microblogging services, there has been a continuous growth of short-text social networking on the Internet. With the generation of large amounts of microposts, there is a need for effective categorization and search of the data. Twitter, one of the largest microblogging sites, allows users to make use of hashtags to categorize their posts. However, the majority of tweets do not contain tags, which hinders the quality of the search results. In this paper, we propose a novel method for unsupervised and content-based hashtag recommendation for tweets. Our approach relies on Latent Dirichlet Allocation (LDA) to model the underlying topic assignment of language classified tweets. The advantage of our approach is the use of a topic distribution to recommend general hashtags.

Engagement through microblogging: educator professional development via Twitter

Abstract: Traditional, top-down professional development (PD) can render teachers mere implementers of the ideas of others, but there is some hope that the participatory nature of social media such as Twitter might support more grassroots PD. To better understand Twitter’s role in education, we conducted a survey of K–16 educators regarding their use of the microblogging service for professional purposes. Respondents described multifaceted and intense use, with PD activities more common than use with students and families. This paper delves into qualitative data from 494 respondents who described their perspectives on Twitter PD. Educators praised the platform as efficient, accessible and interactive. Twitter was credited with providing opportunities to access novel ideas and stay abreast of education advances and trends, particularly regarding educational technology. Numerous respondents compared Twitter favorably with other PD available to them. Members of our sample also appreciated how Twitter connected them to...