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.

Digital leadership, Twitter and Pope Francis

Abstract: The Internet reproduces and strengthens our model of social dialog. Just as in the physical world, the online public conversation and, above all, the ideological debate, requires leaders who can be a point of reference to either foster values or contradict them. The concept of leadership has drawn the attention of several studies concerning communication management. Leaders are neither all equal nor do they exercise leadership by means of the same tools. This article studies both the concept of digital leadership as a guide for online conversation and the use that microblogs, such as Twitter, can provide for this purpose. Among several public figures using Twitter, we have focused our study on the @Pontifex account to have an insight into the type of leadership exercised by the Holy Father and the impact of his teaching. The analysis shows that the Pope uses Twitter for catechetical purposes and that he is aware that his message can reach a large audience. Moreover, although interaction between th...

Deep Learning-Based Rumor Detection on Microblogging Platforms: A Systematic Review

Abstract: With the rapid increase in the popularity of social networks, the propagation of rumors is also increasing. Rumors can spread among thousands of users immediately without verification and can cause serious damages. Recently, several research studies have been investigated to control online rumors automatically by mining rich text available on the open network with deep learning techniques. In this paper, we conducted a systematic literature review for rumor detection using deep neural network approaches. A total of 108 studies were retrieved using manual research from five databases (IEEE Explore, Springer Link, Science Direct, ACM Digital Library, and Google Scholar). The considered studies are then examined in our systematic review to answer the seven research questions that we have formulated to deeply understand the overall trends in the use of deep learning methods for rumor detection. Apart from this, our systematic review also presents the challenges and issues that are faced by the researchers in this area and suggests promising future research directions. Our review will be beneficial for researchers in this domain as it will facilitate researchers' comparison with the existing works due to the availability of a complete description of the used performance matrices, dataset characteristics, and the deep learning model used per each work. Our review will also assist researchers in finding the available annotated datasets that can be used as benchmarks for comparing their new proposed approaches with the existing state-of-the-art works.

Personalized News Recommendation Using Twitter

Abstract: Online news reading has become a popular way to read news articles from a huge collection of news sources around the globe. News recommender systems help users manage this flood by suggesting articles based on user interests rather than presenting articles in order of their occurrence. We present our research on developing personalized news recommendation system with the help of a popular micro-blogging service, "Twitter." News articles are ranked based on the popularity of the article identified with the help of the tweets from Twitter's public timeline. In addition, users construct profiles based on their interests and news articles are also ranked based on their match to the user profile. By combining these two approaches, we present a hybrid news recommendation model that recommends interesting news stories to the user based on their popularity as well as their relevance to the user profile.

Implementing Twitter in a Health Sciences Library

Abstract: The NYU Health Sciences Libraries created an account on Twitter, a microblogging service, as a new outreach tool marketed to students, faculty, and staff. The team used Twitter to promote resources, events, and news. Twitter is a part of a pipeline of information that also includes the library's Web site and Facebook. Although it is difficult to measure the effectiveness of a social networking tool, the overhead of using Twitter is so low that it merits consideration.

Twarql: tapping into the wisdom of the crowd

Abstract: Twarql is an infrastructure translating microblog posts from Twitter as Linked Open Data in real-time. The approach employed in Twarql can be summarized as follows: (1) extract content (e.g. entity mentions, hashtags and URLs) from microposts streamed from Twitter; (2) encode content in RDF using shared and well-known vocabularies (FOAF, SIOC, MOAT, etc.); (3) enable structured querying of microposts with SPARQL; (4) enable subscription to a stream of microposts that match a given query; and (5) enable scalable real-time delivery of streaming annotated data using sparqlPuSH. In this paper we use a brand tracking scenario to demonstrate how Twarql enables flexibility in handling the information overload of those interested in collectively analyzing microblog data for sensemaking. The dataset produced is shared as Linked Data. Twarql is available as open source and can be easily deployed or extended for monitoring Twitter data in various contexts such as brand tracking, disaster relief management, stock exchange monitoring, etc.