Predicting Rising Follower Counts on Twitter Using Profile Information

When evaluating the cause of one's popularity on Twitter, one thing is considered to be the main driver: Many tweets. There is debate about the kind of tweet one should publish, but little beyond tweets. Of particular interest is the information provided by each Twitter user's profile page. One of the features are the given names on those profiles. Studies on psychology and economics identified correlations of the first name to, e.g., one's school marks or chances of getting a job interview in the US. Therefore, we are interested in the influence of those profile information on the follower count. We addressed this question by analyzing the profiles of about 6 Million Twitter users. All profiles are separated into three groups: Users that have a first name, English words, or neither of both in their name field. The assumption is that names and words influence the discoverability of a user and subsequently his/her follower count. We propose a classifier that labels users who will increase their follower count within a month by applying different models based on the user's group. The classifiers are evaluated with the area under the receiver operator curve score and achieves a score above 0.800.Comment: 10 pages, 3 figures, 8 tables, WebSci '17, June 25--28, 2017, Troy, NY, US

Twitter as a formal and informal language learning tool: from potential to evidence

Twitter can be used as a language learning tool and this potential has been identified by a number of scholars. This chapter presents an overview of the identified potential of Twitter as a language learning tool and presents an overview of different studies carried out to provide evidence of language learning using Twitter in different contexts. It concludes that, although there is evidence of language acquisition in formal contexts, more research is needed to inform how Twitter is used in informal settings

Probabilistic Inference of Twitter Users' Age based on What They Follow

Twitter provides an open and rich source of data for studying human behaviour at scale and is widely used in social and network sciences. However, a major criticism of Twitter data is that demographic information is largely absent. Enhancing Twitter data with user ages would advance our ability to study social network structures, information flows and the spread of contagions. Approaches toward age detection of Twitter users typically focus on specific properties of tweets, e.g., linguistic features, which are language dependent. In this paper, we devise a language-independent methodology for determining the age of Twitter users from data that is native to the Twitter ecosystem. The key idea is to use a Bayesian framework to generalise ground-truth age information from a few Twitter users to the entire network based on what/whom they follow. Our approach scales to inferring the age of 700 million Twitter accounts with high accuracy.Comment: 9 pages, 9 figure

Engaging Higher Education Students Through Tweeting

Social media such as Twitter helps engage learners and promotes interaction with course content. Twitter allows learners and educators to interact via “tweets” via Smartphones, laptops, or any device with Internet access. This presentation will demonstrate how to use Twitter as a platform for discussion, polling, question and answer sessions, and other collaborative interactions in a higher education classroom. It will step educators through the process of setting up a Twitter account and the process of planning a lesson with Twitter applications

TwitterMancer: predicting interactions on Twitter accurately

This paper investigates the interplay between different types of user interactions on Twitter, with respect to predicting missing or unseen interactions. For example, given a set of retweet interactions between Twitter users, how accurately can we predict reply interactions? Is it more difficult to predict retweet or quote interactions between a pair of accounts? Also, how important is time locality, and which features of interaction patterns are most important to enable accurate prediction of specific Twitter interactions? Our empirical study of Twitter interactions contributes initial answers to these questions.We have crawled an extensive data set of Greek-speaking Twitter accounts and their follow, quote, retweet, reply interactions over a period of a month. We find we can accurately predict many interactions of Twitter users. Interestingly, the most predictive features vary with the user profiles, and are not the same across all users. For example, for a pair of users that interact with a large number of other Twitter users, we find that certain “higher-dimensional” triads, i.e., triads that involve multiple types of interactions, are very informative, whereas for less active Twitter users, certain in-degrees and out-degrees play a major role. Finally, we provide various other insights on Twitter user behavior. Our code and data are available at https://github.com/twittermancer/.Accepted manuscrip

TwitterMancer: Predicting Interactions on Twitter Accurately

This paper investigates the interplay between different types of user interactions on Twitter, with respect to predicting missing or unseen interactions. For example, given a set of retweet interactions between Twitter users, how accurately can we predict reply interactions? Is it more difficult to predict retweet or quote interactions between a pair of accounts? Also, how important is time locality, and which features of interaction patterns are most important to enable accurate prediction of specific Twitter interactions? Our empirical study of Twitter interactions contributes initial answers to these questions. We have crawled an extensive dataset of Greek-speaking Twitter accounts and their follow, quote, retweet, reply interactions over a period of a month. We find we can accurately predict many interactions of Twitter users. Interestingly, the most predictive features vary with the user profiles, and are not the same across all users. For example, for a pair of users that interact with a large number of other Twitter users, we find that certain "higher-dimensional" triads, i.e., triads that involve multiple types of interactions, are very informative, whereas for less active Twitter users, certain in-degrees and out-degrees play a major role. Finally, we provide various other insights on Twitter user behavior. Our code and data are available at https://github.com/twittermancer/. Keywords: Graph mining, machine learning, social media, social network

Upgrading a Social Media Strategy to Increase Twitter Engagement During the Spring Annual Meeting of the American Society of Regional Anesthesia and Pain Medicine.

Microblogs known as tweets are a rapid, effective method of information dissemination in health care. Although several medical specialties have described their Twitter conference experiences, Twitter-related data in the fields of anesthesiology and pain medicine are sparse. We therefore analyzed the Twitter content of 2 consecutive spring meetings of the American Society of Regional Anesthesia and Pain Medicine using publicly available online transcripts. We also examined the potential contribution of a targeted social media campaign on Twitter engagement during the conferences. The original Twitter meeting content was largely scientific in nature and created by meeting attendees, the majority of whom were nontrainee physicians. Physician trainees, however, represent an important and increasing minority of Twitter contributors. Physicians not in attendance predominantly contributed via retweeting original content, particularly picture-containing tweets, and thus increased reach to nonattendees. A social media campaign prior to meetings may help increase the reach of conference-related Twitter discussion

Studying Social Networks at Scale: Macroscopic Anatomy of the Twitter Social Graph

Twitter is one of the largest social networks using exclusively directed links among accounts. This makes the Twitter social graph much closer to the social graph supporting real life communications than, for instance, Facebook. Therefore, understanding the structure of the Twitter social graph is interesting not only for computer scientists, but also for researchers in other fields, such as sociologists. However, little is known about how the information propagation in Twitter is constrained by its inner structure. In this paper, we present an in-depth study of the macroscopic structure of the Twitter social graph unveiling the highways on which tweets propagate, the specific user activity associated with each component of this macroscopic structure, and the evolution of this macroscopic structure with time for the past 6 years. For this study, we crawled Twitter to retrieve all accounts and all social relationships (follow links) among accounts; the crawl completed in July 2012 with 505 million accounts interconnected by 23 billion links. Then, we present a methodology to unveil the macroscopic structure of the Twitter social graph. This macroscopic structure consists of 8 components defined by their connectivity characteristics. Each component group users with a specific usage of Twitter. For instance, we identified components gathering together spammers, or celebrities. Finally, we present a method to approximate the macroscopic structure of the Twitter social graph in the past, validate this method using old datasets, and discuss the evolution of the macroscopic structure of the Twitter social graph during the past 6 years.Comment: ACM Sigmetrics 2014 (2014

Clustering of twitter technology tweets and the impact of stopwords on clusters

Year of 2010 could be termed as the year in which Twitter became completely mainstream. Twitter, which started as a means of communicating with friends, became much more than its beginning. Now Twitter is used by companies to promote their new products, used by movie industry to promote movies. A lot of advertising and branding is now tied to Twitter and most importantly any breaking news that happens, the first place one goes and tries to find is to search it on Twitter. Be it the Mumbai attacks that happened in 2008, or the minor earthquakes that happened in Bay Area in 2010 or the twitter revolution cause of the Iran elections, most of the tech and not so tech savvy viewers were following twitter rather than any main stream news channels. In fact most of the breaking news now comes on Twitter because of the huge number of user base rather than the traditional mainstream media. The focus of this paper is clustering with the TF-IDF weighted mechanism of daily technology news tweets of prominent bloggers and news sites using Apache Mahout and to evaluate the effects of introducing and removing stop words on the quality of clustering. This project restricts itself to only tweets in the English language