Marco A. Janssen

What makes Question & Answer (Q&A) communities productive? In this paper, we look into how the diversity of behavioral types of agents impacts the performance of Q&A communities using different performance metrics. We do this by developing an agent-based model informed by insights from previous studies on Q&A communities. By analyzing the different strategies for how questions are selected to answer, we find that there are mixtures of strategies leading to the best outcomes for different performance conditions. Particularly, Q&A communities that encourage participants to focus on answering the new questions reach the best performance in answering the questions, creating the long term value, and improving the competence of solving difficulty. In conclusion, we find that the current strategies of question selection on Stack Overflow are in line with the high performance of producing public benefit from the collective attention available.

We find that the flow of attention on the Web forms a directed, tree-like structure implying the time-sensitive browsing behavior of users. Using the data of a news sharing website, we construct clickstream networks in which nodes are news stories and edges represent the consecutive clicks between two stories. To identify the flow direction of clickstreams, we define the “flow distance” of nodes (L_i), which measures the average number of steps a random walker takes to reach the ith node. It is observed that L_i is related with the clicks (C_i) to news stories and the age (T_i) of stories. Putting these three variables together help us understand the rise and decay of news stories from a network perspective. We also find that the studied clickstream networks preserve a stable structure over time, leading to the scaling between users and clicks. The universal scaling behavior is confirmed by the 1,000 Web forums. We suggest that the tree-like, stable structure of clickstream networks reveals the time-sensitive preference of users in online browsing. To test our assumption, we discuss three models on individual browsing behavior and compare the simulation results with empirical data.

Online communities are becoming increasingly important as platforms for large-scale human cooperation. These communities allow users seeking and sharing professional skills to solve problems collaboratively. To investigate how users cooperate to complete a large number of knowledge-producing tasks, we analyze Stack Exchange, one of the largest question and answer systems in the world. We construct attention networks to model the growth of 110 communities in the Stack Exchange system and quantify individual answering strategies using the linking dynamics on attention networks. We identify two answering strategies. Strategy A aims at performing maintenance by doing simple tasks, whereas strategy B aims at investing time in doing challenging tasks. Both strategies are important: empirical evidence shows that strategy A decreases the median waiting time for answers and strategy B increases the acceptance rate of answers. In investigating the strategic persistence of users, we find that users tends to stick on the same strategy over time in a community, but switch from one strategy to the other across communities. This finding reveals the different sets of knowledge and skills between users. A balance between the population of users taking A and B strategies that approximates 2:1, is found to be optimal to the sustainable growth of communities.

With the Internet has come the phenomenon of people volunteering to work on digital public goods such as open source software and online encyclopedia articles. Presumably, the success of individual public goods has an effect on attracting volunteers. However, the definition of success is ill-defined. This paper explores the impact of different success metrics on a simple public goods model. The findings show that the different success metrics considered do have an impact on the behavior of the model, with the largest differences being between consumeroriented and producer-oriented metrics. This indicates that many proposed success metrics may be mapped into one of these two categories and within a category, all success metrics measure the same phenomenon.We argue that the characteristics of produceroriented metrics more closely match real world phenomena, indicating that public goods are driven by producer, and not consumer, interests.

Keywords: Digital public goods, success metrics, FLOSS, open source software, Wikipedia

The last few years have seen a rapid increase in the number of Free/Libre Open Source Software (FLOSS) projects. Some of these projects, such as Linux and the Apache web server, have become phenomenally successful. However, for every successful FLOSS project there are dozens of FLOSS projects which never succeed. These projects fail to attract developers and/or consumers and, as a result, never get off the ground. The aim of this research is to better understand why some FLOSS projects flourish while others wither and die. This article presents a simple agent-based model that is calibrated on key patterns of data from SourceForge, the largest online site hosting open source projects. The calibrated model provides insight into the conditions necessary for FLOSS success and might be used for scenario analysis of future developments of FLOSS.

Large scale collaboration is a fundamental characteristic of human society, and has recently manifested in the development and proliferation of online communities. These virtual social spaces provide an opportunity to explore large scale collaborations as natural experiments in which determinants of success can be tested. In order to do this, we first review previous work on meddling online communities to build an understanding of how these communities function. Having thus identified the operating mechanisms inherent in online communities, we propose a population ecology model of online communities that seeks to explain a number of statistical patterns from a selection of such communities.