Wikipedia, the largest encyclopedia ever created, is a global initiative driven by volunteer contributions. When the COVID-19 pandemic broke out and mobility restrictions ensued across the globe, it was unclear whether contributions to Wikipedia would decrease in the face of the pandemic, or whether volunteers would withstand the added stress and increase their contributions to accommodate the growing readership uncovered in recent studies. We analyze $$\mathbf {223}$$ 223 million edits contributed from 2018 to 2020 across twelve Wikipedia language editions and find that Wikipedia’s global volunteer community responded resiliently to the pandemic, substantially increasing both productivity and the number of newcomers who joined the community. For example, contributions to the English Wikipedia increased by over $$\mathbf {20\%}$$ 20 % compared to the expectation derived from pre-pandemic data. Our work sheds light on the response of a global volunteer population to the COVID-19 crisis, providing valuable insights into the behavior of critical online communities under stress.

Currently, the relation between edit behavior, link structure, and article quality is not well-understood in our community, notwithstanding that this relationship may facilitate editing processes and content quality on Wikipedia. To shed light on this complex relation, we classify article edits and perform an in-depth analysis of editing sequences for 4941 articles. Additionally, we build a network of internal Wikipedia hyperlinks between articles. Using this data, we compute parsimonious metrics to quantify editing and linking behavior. Our analysis unveils that conflicted articles differ substantially from others in almost all metrics, while we also detect slight trends for high-quality articles. With our network analysis we find evidence indicating that controversial and edit war articles frequently span structural holes in the Wikipedia network. Finally, in a prediction experiment we demonstrate the usefulness of edit behavior patterns and network properties in predicting conflict and article quality. With our work, we assist online collaboration communities, especially Wikipedia, in long-term improvement of content quality by offering valuable insights about the interplay of article quality, controversies and edit wars, editing behavior, and network properties via sequence-based edit and network-based article metrics.

Legislative proceedings present a rich source of multidimensional information that is crucial to citizens and journalists in a democratic system. At present, no fully automated solution exists that is capable of capturing all the necessary information during such proceedings. Even if professional-quality automated transcriptions existed, other tasks such as speaker or rhetorical position identifications are not fully automatable. This work focuses on improving and evaluating the transcription software used by the Digital Democracy initiative, named Transcription Tool. Human transcribers work to up-level state legislative proceedings using this tool. Five phases of tool improvements are introduced and for each phase, the resulting change in efficiency is measured. We investigate over 12,000 individual transcription sessions (2,300 hours of video), where each session is the record of one bill discussion. A set of about 3,200 sessions belonging to a single cohort of 20 transcribers is further evaluated. Through introduction of new tool features, human-assisted transcription efficiency can be improved by 19.4% over five phases. Furthermore, investigation into transcriber usage patterns reveals that transcription time is composed of passive time, speaker identification, text correction, tool startup, as well as splitting and merging utterances. We analyze and rank these as a contribution.

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