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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.


Papers
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Journal ArticleDOI
TL;DR: This review describes how Twitter is being used in non-psychiatric medical fields and highlights four current and/or potential uses of Twitter in psychiatry.
Abstract: Social media tools such as blogs, microblogs, social networking sites, podcasts, and video-sharing sites are now ubiquitous. These tools enable instantaneous interactions with a global community of individuals, including medical professionals, learners, and patients. An understanding of social media tools and how they can be used by psychiatrists is increasingly important. This review defines some relevant social media terms and addresses challenges specific to the use of social media in psychiatry. Focused primarily on Twitter, one of the most commonly used social media tools, the review describes how Twitter is being used in non-psychiatric medical fields and highlights four current and/or potential uses of Twitter in psychiatry: (1) patient care and advocacy, (2) lifelong learning, (3) research data collection and collaboration, and (4) scholarly recognition and impact.

31 citations

Journal ArticleDOI
TL;DR: In this article, a bibliometric analysis was conducted in order to identify significant authors, journals, and institutions who engaged in the research oriented towards Twitter utilization in tourism, and text-mining analysis has been conducted to extract and identify the topics of the papers investigating the utilization of Twitter for tourism research.
Abstract: Background: Twitter is the most popular microblog platform. Individuals, companies, organizations, and even governments use Twitter on a daily bases and get vast benefits from it. Twitter also has been valuable for the tourism sector, especially in developing business strategies, planning and studying tourist decision-making processes. Objectives: Goal of the paper is to identify the trends, patterns and the research gaps of the research focusing on the Twitter usage in tourism. Methods/Approach: A bibliometric analysis was conducted in order to identify significant authors, journals, and institutions who engaged in the research-oriented towards Twitter utilization in tourism. In addition, text-mining analysis has been conducted in order to extract and identify the topics of the papers investigating the utilization of Twitter for tourism research. Results: Research of Twitter utilization in tourism has increased substantially in the last decade, with most of the research conducted in the United States and Japan. Extracted topics are focused on distinctive themes, such as network analysis, word of mouth, and destination management. Conclusions: New topics have emerged, such as the utilization of Twitter in crisis communication and terrorist attacks, as well as the integration of Twitter and other social media such as Flickr. This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

31 citations

Proceedings Article
15 Mar 2013
TL;DR: This paper introduces the combination of the noun phrases’ frequency and their PMI measure as constraint on aspect extraction and shows that the meronymy relationship between politicians and their topics holds and improves accuracy of aspect extraction.
Abstract: Due to the vast amount of user-generated content in the emerging Web 2.0, there is a growing need for computational processing of sentiment analysis in documents. Most of the current research in this field is devoted to product reviews from websites. Microblogs and social networks pose even a greater challenge to sentiment classification. However, especially marketing and political campaigns leverage from opinions expressed on Twitter or other social communication platforms. The objects of interest in this paper are the presidential candidates of the Republican Party in the USA and their campaign topics. In this paper we introduce the combination of the noun phrases’ frequency and their PMI measure as constraint on aspect extraction. This compensates for sparse phrases receiving a higher score than those composed of high-frequency words. Evaluation shows that the meronymy relationship between politicians and their topics holds and improves accuracy of aspect extraction.

31 citations

Journal ArticleDOI
13 Sep 2017
TL;DR: A pipeline, implemented as a web service, allows a decision maker to monitor Twitter’s sentiment regarding Google Trends, enabling users to choose geographic areas for their monitors, and aims to bridge the gap among Google search query data and sentiments that emerge on Twitter about these trends.
Abstract: An ever-growing body of knowledge demonstrates the correlation among real-world phenomena and search query data issued on Google, as showed in the literature survey introduced in the following. The purpose of this paper is to introduce a pipeline, implemented as a web service, which, starting with recent Google Trends, allows a decision maker to monitor Twitter’s sentiment regarding these trends, enabling users to choose geographic areas for their monitors. In addition to the positive/negative sentiments about Google Trends, the pipeline offers the ability to view, on the same dashboard, the emotions that Google Trends triggers in the Twitter population. Such a set of tools, allows, as a whole, monitoring real-time on Twitter the feelings about Google Trends that would otherwise only fall into search statistics, even if useful. As a whole, the pipeline has no claim of prediction over the trends it tracks. Instead, it aims to provide a user with guidance about Google Trends, which, as the scientific literature demonstrates, is related to many real-world phenomena (e.g. epidemiology, economy, political science).,The proposed experimental framework allows the integration of Google search query data and Twitter social data. As new trends emerge in Google searches, the pipeline interrogates Twitter to track, also geographically, the feelings and emotions of Twitter users about new trends. The core of the pipeline is represented by a sentiment analysis framework that make use of a Bayesian machine learning device exploiting deep natural language processing modules to assign emotions and sentiment orientations to a collection of tweets geolocalized on the microblogging platform. The pipeline is accessible as a web service for any user authorized with credentials.,The employment of the pipeline for three different monitoring task (i.e. consumer electronics, healthcare, and politics) shows the plausibility of the proposed approach in order to measure social media sentiments and emotions concerning the trends emerged on Google searches.,The proposed approach aims to bridge the gap among Google search query data and sentiments that emerge on Twitter about these trends.

31 citations

Book ChapterDOI
11 Mar 2010
TL;DR: This work analyzes a user’s friends and followers to gain information on him and evaluates them using different metrics to determine the amount of trust his peers give him.
Abstract: As online social networks acquire a larger user base, they also become more interesting targets for spammers. Spam can take very different forms on social web sites and can not always be detected by analyzing textual content. However, the platform’s social nature also offers new ways of approaching the spam problem. In this work we analyze a user’s friends and followers to gain information on him. Next, we evaluate them using different metrics to determine the amount of trust his peers give him. We use the Twitter microblogging platform for this case study.

31 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023202
2022551
2021153
2020238
2019226
2018282