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Many North American cities are building bicycling infrastructure. Lively discussions on social media, where people passionately support or reject bicycling infrastructure projects, provide a unique data set on attitudes towards bicycling infrastructure. Our goal is to analyse social media posts in Edmonton and Victoria, Canada as new bike infrastructure was implemented to understand the thematic and social elements of the conversation and how these changed over time. We collected Twitter messages ( n = 13,121: 7640 in Edmonton; 5481 in Victoria) and compared three timeframes: before lanes opened (January 2015 to lane opening); the first riding season (opening to April 2017); and the second riding season (May 2017 to November 2018). For each timeframe, we evaluated word-combination frequencies (to understand the use of language) and social network structures (to understand which accounts were influential and how they interacted). We observed a change in the three time periods. Before the bicycling infrastructure was built, Twitter activity was focused on advocacy, which was especially strong in Victoria. The first riding season had the most social media activity, the most diverse perspectives and the most controversy. The second riding season held more support. Based on the Twitter activity, we found that Edmonton had more support from local businesses and traditional media, launching a connected network of infrastructure with less social media opposition. Our results suggest that attitudes associated with change in bicycling infrastructure may have a cycle, with initial negative responses to change, followed by an uptick in positive attitudes.

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This paper explores infrastructure supporting humanities–computer science research in large–scale image data by asking: Why is collaboration a requirement for work within digital humanities projects? What is required for fruitful interdisciplinary collaboration? What are the technical and intellectual approaches to constructing such an infrastructure? What are the challenges associated with digital humanities collaborative work? We reveal that digital humanities collaboration requires the creation and deployment of tools for sharing that function to improve collaboration involving large–scale data repository analysis among multiple sites, academic disciplines, and participants through data sharing, software sharing, and knowledge sharing practices.

This article introduces a model for detecting low-quality information we refer to as the Index of Measured-diversity, Partisan-certainty, Ephemerality, and Domain (IMPED). The model purports that low-quality information is characterized by ephemerality, as opposed to quality content that is designed for permanence. The IMPED model leverages linguistic and temporal patterns in the content of social media messages and linked webpages to estimate a parametric survival model and the likelihood the content will be removed from the internet. We review the limitations of current approaches for the detection of problematic content, including misinformation and false news, which are largely based on fact checking and machine learning, and detail the requirements for a successful implementation of the IMPED model. The article concludes with a review of examples taken from the 2018 election cycle and the performance of the model in identifying low-quality information as a proxy for problematic content.