Shocking the Crowd: The Effect of Censorship Shocks on Chinese Wikipedia

Collaborative crowdsourcing has become a popular approach to organizing work across the globe. Being global also means being vulnerable to shocks -- unforeseen events that disrupt crowds -- that originate from any country. In this study, we examine changes in collaborative behavior of editors of Chinese Wikipedia that arise due to the 2005 government censor- ship in mainland China. Using the exogenous variation in the fraction of editors blocked across different articles due to the censorship, we examine the impact of reduction in group size, which we denote as the shock level, on three collaborative behavior measures: volume of activity, centralization, and conflict. We find that activity and conflict drop on articles that face a shock, whereas centralization increases. The impact of a shock on activity increases with shock level, whereas the impact on centralization and conflict is higher for moderate shock levels than for very small or very high shock levels. These findings provide support for threat rigidity theory -- originally introduced in the organizational theory literature -- in the context of large-scale collaborative crowds

Participation of New Editors After Times of Shock on Wikipedia

User participation is vital to the success of collaborative crowdsourcing platforms such as Wikipedia. Previously user participation has been studied during “normal times”. However, less is known about participation following shocks that draw attention to an article. Such events can be recruiting opportunities due to increased attention; but can also pose a threat to the quality and control of the article and drive away newcomers. We study the collaborative dynamics of Wikipedia articles after times corresponding to shocks generated by drastic increases in attention as indicated by data from Google trends.We find that participation following such events is indeed different from participation during normal times–both newcomers and incumbents participate at higher rates during shocks. We also identify collaboration dynamics that mediate the effects of shocks on continued participation after the shock. The impact of shocks on participation is mediated by the amount of negative feedback given to newcomers in the form of reverted edits and the amount of coordination editors engage in through edits of the article’s talk page.National Science Foundation Grant No. IIS-1617820Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/148429/1/Zhang et al. 2019.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/148429/4/3253-Article Text-6302-1-10-20190531.pdfDescription of Zhang et al. 2019.pdf : Preprint versionDescription of 3253-Article Text-6302-1-10-20190531.pdf : Final Versio

Crowd Development: The Interplay between Crowd Evaluation and Collaborative Dynamics in Wikipedia

Collaborative crowdsourcing is an increasingly common way of accomplishing work in our economy. Yet, we know very little about how the behavior of these crowds changes over time and how these dynamics impact their performance. In this paper, we take a group development approach that considers how the behavior of crowds change over time in anticipation and as a result of their evaluation and recognition. Towards this goal, this paper studies the collaborative behavior of groups comprised of editors of articles that have been recognized for their outstanding quality and given the Good Articles (GA) status and those that eventually become Featured Articles (FA) on Wikipedia. The results show that the collaborative behavior of GA groups radically changes just prior to their nomination. In particular, the GA groups experience increases in the level of activity, centralization of workload, and level of GA experience and decreases in conflict (i.e., reverts) among editors. After being promoted to GA, they converge back to their typical behavior and composition. This indicates that crowd behavior prior to their evaluation period is dramatically different than behavior before or after. In addition, the collaborative behaviors of crowds during their promotion to GA are predictive of whether they are eventually promoted to FA. Our findings shed new light on the importance of time in understanding the relationship between crowd performance and collaborative measures such as centralization, conflict and experience.National Science Foundation under Grant No. IIS-1617820Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/138122/1/Zhang et al. 2017.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/138122/4/a119-zhang.pdfDescription of a119-zhang.pdf : Published Versio

Regression of average score in quiz on behavioral attributes.

<p>Regression of average score in quiz on behavioral attributes.</p

Distribution of willingness-to-pay for non-instrumental information (curiosity).

<p>Distribution of willingness-to-pay for non-instrumental information (curiosity).</p

Quiz score distributions among non-participants (left panel) and participants of economic games (right panel).

<p>Quiz score distributions among non-participants (left panel) and participants of economic games (right panel).</p

Real or bogus: Predicting susceptibility to phishing with economic experiments

<div><p>We present a lab-in-the-field experiment to demonstrate how individual behavior in the lab predicts their ability to identify phishing attempts. Using the business and finance staff members from a large public university in the U.S., we find that participants who are intolerant of risk, more curious, and less trusting commit significantly more errors when evaluating interfaces. We also replicate prior results on demographic correlates of phishing vulnerability, including age, gender, and education level. Our results suggest that behavioral characteristics such as intolerance of risk, curiosity, and trust can be used to predict individual ability to identify phishing interfaces.</p></div

Root mean square prediction error.

<p>Root mean square prediction error.</p

Average score in the quiz.

<p>Average score in the quiz.</p

Risk preference calibration and classification using consistent subjects.

<p>Risk preference calibration and classification using consistent subjects.</p