Twitter’s big hitters

We describe the results of a new computational experiment on Twitter data. By listening to Tweets on a selected topic, we generate a dynamic social interaction network. We then apply a recently proposed dynamic network analysis algorithm that ranks Tweeters according to their ability to broadcast information. In particular, we study the evolution of importance rankings over time. Our presentation will also describe the outcome of an experiment where results from automated ranking algorithms are compared with the views of social media experts

Patterns of implicit and non-follower retweet propagation: investigating the role of applications and hashtags

Existing literature on retweets seems to focus mainly on retweets created using explicit, formal retweeting mechanisms, such as Twitter's own native retweet function, and the prefixing of the terms 'RT' or 'via' in front of copied tweets. However, retweets can also be made using implicit, informal mechanisms. These include tweet replies and other mechanisms, which use neither the native nor RT/via mechanisms, but their content and timelines suggest the likelihood of being a retweet. Moreover, retweets can also occur with or without a defined follower/following network path between a tweet originator and a retweeter. This paper presents an initial taxonomy of propagation based on seven different ways a tweet may spread: native, native non-follower, RT/Via, RT/Via non-follower, replies, non-follower replies and other implicit 'retweets'. An experiment has examined this new model, by investigating where tweets containing URLs from the domains of online petitions, charity fundraisers, news portals, and YouTube videos can be classified into the seven different categories. When including other implicit 'retweets', more than 50% of all the retweets found across all four domains were classified as implicit retweets, while more than 79% of all retweets were made by non-followers. More work needs to be done on the composition of other implicit 'retweets'. Initial investigations found hashtags in 99-100% of these tweets, suggesting that retweeting using conventional mechanisms may not be the main method that URLs get propagated across microblogs

How open are journalists on Twitter? Trends towards the end-user journalism

The many activities of journalists on Twitter should be analyzed. Are they doing a different kind of journalism? With a content analysis of 1125 tweets, this study reveals trends of some Spanish journalists using Twitter. A traditional role like gatekeeping can be highly amplified in terms of transparency and accountability with actions as retweeting or linking. The landscape offered by this platform is framed with the "ambient journalism", which will help to understand the proposal of this study: the end-user journalism. The findings will show the level of opening with the audience in aspects about replies, requests and linking

Tweeting the Mind and Instagramming the Heart: Exploring Differentiated Content Sharing on Social Media

Understanding the usage of multiple OSNs (Online Social Networks) has been of significant research interest as it helps in identifying the unique and distinguishing trait in each social media platform that contributes to its continued existence. The comparison between the OSNs is insightful when it is done based on the representative majority of the users holding active accounts on all the platforms. In this research, we collected a set of user profiles holding accounts on both Twitter and Instagram, these platforms being of prominence among a majority of users. An extensive textual and visual analysis on the media content posted by these users revealed that both these platforms are indeed perceived differently at a fundamental level with Instagram engaging more of the users' heart and Twitter capturing more of their mind. These differences got reflected in almost every microscopic analysis done upon the linguistic, topical and visual aspects.Comment: 4 pages, 8 figure

Excitable human dynamics driven by extrinsic events in massive communities

Using empirical data from a social media site (Twitter) and on trading volumes of financial securities, we analyze the correlated human activity in massive social organizations. The activity, typically excited by real-world events and measured by the occurrence rate of international brand names and trading volumes, is characterized by intermittent fluctuations with bursts of high activity separated by quiescent periods. These fluctuations are broadly distributed with an inverse cubic tail and have long-range temporal correlations with a $1/f$ power spectrum. We describe the activity by a stochastic point process and derive the distribution of activity levels from the corresponding stochastic differential equation. The distribution and the corresponding power spectrum are fully consistent with the empirical observations.Comment: 9 pages, 3 figure

A Semi-automatic Method for Efficient Detection of Stories on Social Media

Twitter has become one of the main sources of news for many people. As real-world events and emergencies unfold, Twitter is abuzz with hundreds of thousands of stories about the events. Some of these stories are harmless, while others could potentially be life-saving or sources of malicious rumors. Thus, it is critically important to be able to efficiently track stories that spread on Twitter during these events. In this paper, we present a novel semi-automatic tool that enables users to efficiently identify and track stories about real-world events on Twitter. We ran a user study with 25 participants, demonstrating that compared to more conventional methods, our tool can increase the speed and the accuracy with which users can track stories about real-world events.Comment: ICWSM'16, May 17-20, Cologne, Germany. In Proceedings of the 10th International AAAI Conference on Weblogs and Social Media (ICWSM 2016). Cologne, German