Memelab: motivation and viral impact

What drives online viral phenomena? The investigators built a live meme-generator website for tracking online social transmission. Participants created memes to share with their social networks, and online sharing behavior was longitudinally monitored. Individual differences in expertise and specific meme features were predictive of social transmission behaviors

Agent Based Modeling of Emotional Selection in Urban Legends

Previous research has found that Urban Legends are selectively received and transmitted based upon emotional selection. We first replicate this work using Agent Based Modeling (n=80), then scale our simulation to n=16,000. We demonstrate viral retransmission of Urban Legends among Agents performing emotional selection under network conditions

A Synthetic World Population for Agent-Based Social Simulation

pplapi.com (pronounced “people API”) is a web-based data service that provides access to a synthetic world population, n = 7,171,922,938. Researchers can submit queries to pplapi.com using its Application Programming Interface (API) to obtain samples consisting of synthetic agents drawn from this population. Because researchers do not need to host the synthetic population themselves, pplapi.com reduces start-up costs associated with using synthetic agents in research. pplapi.com provides a canonical namespace for Agent Based Social Simulations, permitting comparisons between models implemented with disparate modeling frameworks and programming languages

The Ends Justify the Memes

This talk presents an update on my research into memes.  It begins with an introduction to memes that is suitable for any audience.  It concludes with a detailed description of human research and simulation results that converge with one another.  I also present a short online study on email forwarding chains

Meme creation and sharing processes: individuals shaping the masses

<div>The propagation of online memes is initially influenced by meme creators and secondarily by meme consumers, whose individual sharing decisions accumulate to determine total meme propagation. We characterize this as a sender/receiver sequence in which the first sender is also the creator. This sequence consists of two distinct processes, the creation process and the sharing process. We investigated these processes separately to determine their individual influence on sharing outcomes. Our study observed participants creating memes in the lab. We then tracked the sharing of those memes, derived a model of sharing behavior, and implemented our sharing model in a contagion simulation.</div><div><br></div><div>Although we assume meme consumers typically have little or no information about a meme's creator when making a decision about whether to share a meme (and vice versa), we nevertheless ask whether consumer re-sharing behavior can be predicted based on features of the creator. Using human participants, web log monitoring, and statistical model fitting, the resulting Creator Model of Re-sharing Behavior predicts 11.5\% of the variance in the behavior of consumers. Even when we know nothing about re-sharers of a meme, we can predict something about their behavior by observing the creation process.</div><div><br></div><div>To investigate the individual re-sharing decisions that, together, constitute a meme's total consumer response, we built a statistical model from human observation. Receivers make their decision to share as a function of the meme's content and their reaction to it, which we model as a consumer's decision to share. The resulting Consumer Model of Sharing Decisions describes 37.5\% of the variance in this decision making process.</div><div><br></div><div>Finally, we implemented our consumer model of sharing as the infection function in an SIS contagion simulation. When using model parameters similar to our human participants research, we received encouraging results that suggest future refinements will be worthwhile.</div

Memelab

In this 3-minute lightning talk, I present a human behavioral study designed to measure the meme creation process and assess the online viral impact of those memes.  Then, I demonstrate the use of a simulation for replicating the same results using synthetic agents with a behavioral model based on human results

Memelab: can pictures of adorable kittens explain political revolutions?

Last year, Tweets across Egypt called a political system into question, while more recently PSY’s Gangnam Style music video received over 300 million views in just two months. These viral phenomena are the product of information flowing through an interconnected population, so could the same viral mechanisms be an explanation for both events? My work uses internet memes to study the way we create and share things online. During the spring of 2012, over 100 UTSC students used Memelab, an online viral research laboratory, to create memes and share them with friends. This presentation includes a history of memes, the results of the first Memelab experiment, and the future of social network simulation

Disagreement in Online Discussion

This talk discusses my research into online newspaper discussion forums.  I present a meme detection method, community detection algorithm, and a multilevel model designed to isolate the influence of meme authors from the effects of their content

Do Earthlings go online for different reasons

Do all humans use the Internet for the same reasons? Motivations for Internet engagement were assessed in 5 countries: Canada, Greece, Spain, United States, and China. The resulting 4 factors are stable between countries, but mean levels vary. Each country’s 4-factor “signature” is rendered like a flower using a novel radial visualization method