Isaac L. Johnson

The Effect of Population and "Structural" Biases on Social Media-based Algorithms: A Case Study in Geolocation Inference Across the Urban-Rural Spectrum

Much research has shown that social media platforms have substantial population biases. However, very little is known about how these population biases affect the many algorithms that rely on social media data. Focusing on the case study of geolocation inference algorithms and their performance across the urban-rural spectrum, we establish that these algorithms exhibit significantly worse performance for underrepresented populations (i.e. rural users). We further establish that this finding is robust across both text- and network-based algorithm designs. However, we also show that some of this bias can be attributed to the design of algorithms themselves rather than population biases in the underlying data sources. For instance, in some cases, algorithms perform badly for rural users even when we substantially overcorrect for population biases by training exclusively on rural data. We discuss the implications of our findings for the design and study of social media-based algorithms.

Beautiful…but at What Cost?: An Examination of Externalities in Geographic Vehicle Routing

Millions of people use platforms such as Google Maps to search for routes to their desired destinations. Recently, researchers and mapping platforms have shown growing interest in optimizing routes for criteria other than travel time, e.g. simplicity, safety, and beauty. However, despite the ubiquity of algorithmic routing and its potential to define how millions of people move around the world, very little is known about the externalities that arise when adopting these new optimization criteria, e.g. potential redistribution of traffic to certain neighborhoods and increased route complexity (with its associated risks). In this paper, we undertake the first controlled examination of these externalities, doing so across multiple mapping platforms, alternative optimizations, and cities. We find, for example, that scenic routing (i.e. “beauty”-optimized routing) would remove vehicles from highways, greatly increase traffic around parks, and, in certain cases, do the same for high-income areas. Our results also highlight that the interaction between routing criteria and urban structure is complex and effects vary from city to city, an important consideration for the growing literature on alternative routing strategies. Finally, to address the lack of open implementations of alternative routing algorithms and controlled routing evaluation frameworks, we are releasing our alternative routing and evaluation platform with this paper.

Examining Wikipedia With a Broader Lens: Quantifying the Value of Wikipedia's Relationships with Other Large-Scale Online Communities

The extensive Wikipedia literature has largely considered Wikipedia in isolation, outside of the context of its broader Internet ecosystem. Very recent research has demonstrated the significance of this limitation, identifying critical relationships between Google and Wikipedia that are highly relevant to many areas of Wikipedia-based research and practice. This paper extends this recent research beyond search engines to examine Wikipedias relationships with large-scale online communities, Stack Overflow and Reddit in particular. We find evidence of consequential, albeit unidirectional relationships. Wikipedia provides substantial value to both communities, with Wikipedia content increasing visitation, engagement, and revenue, but we find little evidence that these websites contribute to Wikipedia in return. Overall, these findings highlight important connections between Wikipedia and its broader ecosystem that should be considered by researchers studying Wikipedia. Critically, our results also emphasize the key role that volunteer-created Wikipedia content plays in improving other websites, even contributing to revenue generation.

Developing a GIS-Based Online Survey Instrument to Elicit Perceived Neighborhood Geographies to Address the Uncertain Geographic Context Problem

Although neighborhood factors have been consistently associated with health, technological difficulties in eliciting self-defined neighborhoods from large cohorts have compromised the interpretability of this research. Here, we offer a mixed-methods approach to elicit and validate self-defined neighborhoods. Participants used a customized Google.Maps interface to “draw” their neighborhood and answered questions about perceived map accuracy, neighborhood definition, and neighborhood activities. We compared geographic concordance of drawn and narrative neighborhood definitions, quantified differential accuracy by demographic characteristics, and examined factors influencing neighborhood definitions. We found similar geographic concordance between narrative and mapped boundaries in two cities, with no differences by neighborhood size. Self-reported neighborhoods had greater concordance with larger administrative areas (e.g., police precincts) than for smaller units (e.g., census tracts). To delineate their neighborhood boundaries, participants reported using administrative definitions, walking distance, their familiarity with people and structures, where they spend time, and physical landmarks. In New York City, participants also reported considering sociodemographic characteristics and transportation. Our method demonstrates the feasibility of collecting perceived (egocentric) neighborhoods through online mapping surveys, adaptable to many study settings.

Oh The Places You'll Share: An Affordances-Based Model of Social Media Posting Behaviors

With rising use of multiple social network sites (SNSs), people now have an increasing number of options for audience, media, and other SNS features at their disposal. In this paper, our goal is to build machine learning models that can predict people»s multi-SNS posting decisions, thus enabling technology that can personalize and augment current SNS use. We explore affordances--the perceived utilities of a SNS»s features-for creating these models. We build an affordance-based model using data collected from a survey about people»s multi-SNS posting behavior (n=674). Our model predicts posting decisions that are ~35% more accurate compared to a random baseline, and ~10% more accurate than predictions based on SNS popularity.