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.

Sentiment analysis and classification of Indian farmers’ protest using twitter data

Abstract: Protests are an integral part of democracy and an important source for citizens to convey their demands and/or dissatisfaction to the government. As citizens become more aware of their rights, there has been an increasing number of protests all over the world for various reasons. With the advancement of technology, there has also been an exponential rise in the use of social media to exchange information and ideas. In this research, we gathered data from the microblogging website Twitter concerning farmers’ protest to understand the sentiments that the public shared on an international level. We used models to categorize and analyze the sentiments based on a collection of around 20,000 tweets on the protest. We conducted our analysis using Bag of Words and TF-IDF and discovered that Bag of Words performed better than TF-IDF. In addition, we also used Naive Bayes, Decision Trees, Random Forests, and Support Vector Machines and also discovered that Random Forest had the highest classification accuracy.

A Little Bird Told Me, So I Didn't Believe It: Twitter, Credibility, and Issue Perceptions

Abstract: This article investigates how media use of the microblogging tool Twitter affects perceptions of the issue covered and the credibility of the information. In contrast to prior studies showing that ordinary blogs are often judged credible, especially by their users, data from 2 experiments show that Twitter is considered less credible than various forms of stories posted on a newspaper Web site, and fails to convey importance as well as a newspaper or blog.

Increased use of Twitter at a medical conference: a report and a review of the educational opportunities.

Abstract: Background: Most consider Twitter as a tool purely for social networking. However, it has been used extensively as a tool for online discussion at nonmedical and medical conferences, and the academic benefits of this tool have been reported. Most anesthetists still have yet to adopt this new educational tool. There is only one previously published report of the use of Twitter by anesthetists at an anesthetic conference. This paper extends that work. Objective: We report the uptake and growth in the use of Twitter, a microblogging tool, at an anesthetic conference and review the potential use of Twitter as an educational tool for anesthetists. Methods: A unique Twitter hashtag (#WSM12) was created and promoted by the organizers of the Winter Scientific Meeting held by The Association of Anaesthetists of Great Britain and Ireland (AAGBI) in London in January 2012. Twitter activity was compared with Twitter activity previously reported for the AAGBI Annual Conference (September 2011 in Edinburgh). All tweets posted were categorized according to the person making the tweet and the purpose for which they were being used. The categories were determined from a literature review. Results: A total of 227 tweets were posted under the #WSM12 hashtag representing a 530% increase over the previously reported anesthetic conference. Sixteen people joined the Twitter stream by using this hashtag (300% increase). Excellent agreement (κ = 0.924) was seen in the classification of tweets across the 11 categories. Delegates primarily tweeted to create and disseminate notes and learning points (55%), describe which session was attended, undertake discussions, encourage speakers, and for social reasons. In addition, the conference organizers, trade exhibitors, speakers, and anesthetists who did not attend the conference all contributed to the Twitter stream. The combined total number of followers of those who actively tweeted represented a potential audience of 3603 people. Conclusions: This report demonstrates an increase in uptake and growth in the use of Twitter at an anesthetic conference and the review illustrates the opportunities and benefits for medical education in the future. [J Med Internet Res 2012;14(6):e176]

A Hybrid Approach for Detecting Automated Spammers in Twitter

Abstract: Twitter is one of the most popular microblogging services, which is generally used to share news and updates through short messages restricted to 280 characters. However, its open nature and large user base are frequently exploited by automated spammers, content polluters, and other ill-intended users to commit various cybercrimes, such as cyberbullying, trolling, rumor dissemination, and stalking. Accordingly, a number of approaches have been proposed by researchers to address these problems. However, most of these approaches are based on user characterization and completely disregarding mutual interactions. In this paper, we present a hybrid approach for detecting automated spammers by amalgamating community-based features with other feature categories, namely metadata- , content- , and interaction-based features. The novelty of the proposed approach lies in the characterization of users based on their interactions with their followers given that a user can evade features that are related to his/her own activities, but evading those based on the followers is difficult. Nineteen different features, including six newly defined features and two redefined features, are identified for learning three classifiers, namely, random forest , decision tree , and Bayesian network , on a real dataset that comprises benign users and spammers. The discrimination power of different feature categories is also analyzed, and interaction- and community-based features are determined to be the most effective for spam detection, whereas metadata-based features are proven to be the least effective.

Social media as a sensor of air quality and public response in China.

Abstract: Background: Recent studies have demonstrated the utility of social media data sources for a wide range of public health goals, including disease surveillance, mental health trends, and health perceptions and sentiment. Most such research has focused on English-language social media for the task of disease surveillance. Objective: We investigated the value of Chinese social media for monitoring air quality trends and related public perceptions and response. The goal was to determine if this data is suitable for learning actionable information about pollution levels and public response. Methods: We mined a collection of 93 million messages from Sina Weibo, China’s largest microblogging service. We experimented with different filters to identify messages relevant to air quality, based on keyword matching and topic modeling. We evaluated the reliability of the data filters by comparing message volume per city to air particle pollution rates obtained from the Chinese government for 74 cities. Additionally, we performed a qualitative study of the content of pollution-related messages by coding a sample of 170 messages for relevance to air quality, and whether the message included details such as a reactive behavior or a health concern. Results: The volume of pollution-related messages is highly correlated with particle pollution levels, with Pearson correlation values up to .718 (n=74, P <.001). Our qualitative results found that 67.1% (114/170) of messages were relevant to air quality and of those, 78.9% (90/114) were a firsthand report. Of firsthand reports, 28% (32/90) indicated a reactive behavior and 19% (17/90) expressed a health concern. Additionally, 3 messages of 170 requested that action be taken to improve quality. Conclusions: We have found quantitatively that message volume in Sina Weibo is indicative of true particle pollution levels, and we have found qualitatively that messages contain rich details including perceptions, behaviors, and self-reported health effects. Social media data can augment existing air pollution surveillance data, especially perception and health-related data that traditionally requires expensive surveys or interviews. [J Med Internet Res 2015;17(3):e22]