Predictability of extreme events in social media

It is part of our daily social-media experience that seemingly ordinary items (videos, news, publications, etc.) unexpectedly gain an enormous amount of attention. Here we investigate how unexpected these events are. We propose a method that, given some information on the items, quantifies the predictability of events, i.e., the potential of identifying in advance the most successful items defined as the upper bound for the quality of any prediction based on the same information. Applying this method to different data, ranging from views in YouTube videos to posts in Usenet discussion groups, we invariantly find that the predictability increases for the most extreme events. This indicates that, despite the inherently stochastic collective dynamics of users, efficient prediction is possible for the most extreme events.Comment: 13 pages, 3 figure

The Metabolism and Growth of Web Forums

We view web forums as virtual living organisms feeding on user's attention and investigate how these organisms grow at the expense of collective attention. We find that the "body mass" ($PV$) and "energy consumption" ($UV$) of the studied forums exhibits the allometric growth property, i.e., $PV_t \sim UV_t ^ \theta$. This implies that within a forum, the network transporting attention flow between threads has a structure invariant of time, despite of the continuously changing of the nodes (threads) and edges (clickstreams). The observed time-invariant topology allows us to explain the dynamics of networks by the behavior of threads. In particular, we describe the clickstream dissipation on threads using the function $D_i \sim T_i ^ \gamma$, in which $T_i$ is the clickstreams to node $i$ and $D_i$ is the clickstream dissipated from $i$. It turns out that $\gamma$, an indicator for dissipation efficiency, is negatively correlated with $\theta$ and $1/\gamma$ sets the lower boundary for $\theta$. Our findings have practical consequences. For example, $\theta$ can be used as a measure of the "stickiness" of forums, because it quantifies the stable ability of forums to convert $UV$ into $PV$, i.e., to remain users "lock-in" the forum. Meanwhile, the correlation between $\gamma$ and $\theta$ provides a convenient method to evaluate the `stickiness" of forums. Finally, we discuss an optimized "body mass" of forums at around $10^5$ that minimizes $\gamma$ and maximizes $\theta$.Comment: 6 figure

Drivers of Knowledge Contribution in Open Fora: Findings from Wikipedians

Today’s social computing platforms include many open content fora where users voluntarily create and edit content online. This has opened up a new mechanism for knowledge acquisition raising related research questions, including identification of the reasons why people contribute to open fora. While altruism is mentioned most frequently, it has been suggested that there may be additional drivers in play. To explore this possibility, we examine contribution behavior in the Wikipedia context using qualitative data from two focus groups of Wikipedians. Content analysis of the data reveals a number of different drivers of contributor behavior which we then map into the Motivation-Ability-Opportunity (MAO) theoretical framework developed in the organizational behavior literature on work performance. The mapping can provide a theoretical basis for quantitative examination of contributor behavior and lead to more effective methods of managing collective knowledge in other open forum settings, such as corporate wikis

Participation and contribution in crowdsourced surveys

This paper identifies trends within and relationships between the amount of participation and the quality of contributions in three crowdsourced surveys. Participants were asked to perform a collective problem solving task that lacked any explicit incentive: they were instructed not only to respond to survey questions but also to pose new questions that they thought might-if responded to by others-predict an outcome variable of interest to them. While the three surveys had very different outcome variables, target audiences, methods of advertisement, and lengths of deployment, we found very similar patterns of collective behavior. In particular, we found that: the rate at which participants submitted new survey questions followed a heavy-tailed distribution; the distribution in the types of questions posed was similar; and many users posed non-obvious yet predictive questions. By analyzing responses to questions that contained a built-in range of valid response we found that less than 0.2% of responses lay outside of those ranges, indicating that most participants tend to respond honestly to surveys of this form, even without explicit incentives for honesty. While we did not find a significant relationship between the quantity of participation and the quality of contribution for both response submissions and question submissions, we did find several other more nuanced participant behavior patterns, which did correlate with contribution in one of the three surveys. We conclude that there exists an optimal time for users to pose questions early on in their participation, but only after they have submitted a few responses to other questions. This suggests that future crowdsourced surveys may attract more predictive questions by prompting users to pose new questions at specific times during their participation and limiting question submission at non-optimal times

Patterns in Knowledge Production

abstract: This dissertation will look at large scale collaboration through the lens of online communities to answer questions about what makes a collaboration persist. Results address how collaborations attract contributions, behaviors that could give rise to patterns seen in the data, and the properties of collaborations that drive those behaviors. It is understood that collaborations, online and otherwise, must retain users to remain productive. However, before users can be retained they must be recruited. In the first project, a few necessary properties of the ``attraction'' function are identified by constraining the dynamics of an ODE (Ordinary Differential Equation) model. Additionally, more than 100 communities of the Stack Exchange networks are parameterized and their distributions reported. Collaborations do not exist in a vacuum, they compete with and share users with other collaborations. To address this, the second project focuses on an agent-based model (ABM) of a community of online collaborations using a mechanistic approach. The ABM is compared to data obtained from the Stack Exchange network and produces similar distributional patterns. The third project is a thorough sensitivity analysis of the model created in the second project. A variance based sensitivity analysis is performed to evaluate the relative importance of 21 parameters of the model. Results indicate that population parameters impact many outcome metrics, though even those parameters that tend towards a low impact can be crucial for some outcomes.Dissertation/ThesisDoctoral Dissertation Applied Mathematics for the Life and Social Sciences 201

Open-process academic publishing *

Publishing and knowledge production in academia can be significantly improved if aspects of cooperative models developed in software and networking communities are adopted. The Open Access movement focuses on the openness of the final result. The most important attributes of the development of the Internet, the Web and their communication-cooperation tools is the openness of the entire process of production. The novelty that can take many forms is in the organizational structures, decision-making and cooperation. This paper argues that journals adopting a form of Open Process could benefit by increased quality of submissions and publications, faster and more responsive pace of research and by attracting more risk taking and innovative authors. Through open-process publishing and peer reviewing clearer structure and visibility of tasks could be achieved. Equally important could be the possible internal benefits for journal management: the recognition of the most important workers and decision making in their hands, easier and improved project management, attracting new volunteers and reducing the impact of counter-productive participants. If these changes were implemented well, such open-process journals would gain readership and reputation. A simple transition model is suggested: how to start with an email list and proper cultural safeguards. Gcommons.org, a more advanced solution, is a highly configurable Free Software platform that assists open processes. Free software and 'Open Process: why Open Access, or Open Source is not enough' Publishing and peer review processes in academia are outdated and closed models. Key flaws are lack of transparency in the pre-publication process, a lack of dialogue in both pre and post-publication phases, and a linear use of digital media that only scratches the surface of possibilities for greater reflexivity and dialogue in order to have more powerful, effective and responsive knowledge production (Cope and Kalantzis, 2009).