Publications

To cope with the immense amount of content on the web, search engines often use complex algorithms to personalize search results for individual users. However, personalization of search results has led to worries about the Filter Bubble Effect, where the personalization algorithm decides that some useful information is irrelevant to the user, and thus prevents them from locating it.In this paper, we propose a novel methodology to explore the impact of location-based personalization on GoogleSearch results. Assessing the relationship between location and personalization is crucial, since users’ geolocation can be used as a proxy for other demographic traits, like race, income, educational attainment, and political affiliation. In other words, does location-based personalization trap users in geolocal Filter Bubbles?Using our methodology, we collected 30 days of search results from Google Search in response to 240 different queries.By comparing search results gathered from 59 GPS coordinates around the US at three different granularities (county, state, and national), we are able to observe that differences in search results due to personalization grow as physical distance increases. However these differences are highly dependent on what a user searches for: queries for local establishments receive 4-5 different results per page, while more general terms exhibit essentially no personalization.

Do formal deliberative events influence larger patterns of political discussion and public opinion? Critics argue that only a tiny number of people can participate in any given gathering and that deliberation may not remedy and may in fact exacerbate inequalities. We assess these criticisms with an experimental design merging a formal deliberative session with data on participants' social networks. We conducted a field experiment in which randomly selected constituents attended an online deliberative session with their U.S. Senator. We find that attending the deliberative session dramatically increased interpersonal political discussion on topics relating to the event. Importantly, after an extensive series of moderation checks, we find that no participant/nodal characteristics, or dyadic/network characteristics, conditioned these effects; this provides reassurance that observed, positive spillovers are not limited to certain portions of the citizenry. The results of our study suggest that even relatively small-scale deliberative encounters can have a broader effect in the mass public, and that these events are equal-opportunity multipliers.

Humanity is in the early stages of the rise of social algorithms: programs that size us up, evaluate what we want, and provide a customized experience. This quiet but epic paradigm shift is fraught with social and policy implications. The evolution of Google exemplifies this shift. It began as a simple deterministic ranking system based on the linkage structure among Web sites—the model of algorithmic Fordism, where any color was fine as long as it was black (1). The current Google is a very different product, personalizing results (2) on the basis of information about past searches and other contextual information, like location. On page 1130 of this issue, Bakshy et al. (3) explore whether such personalized curation on Facebook prevents users from accessing posts presenting conflicting political views.

The increasing abundance of digital textual archives provides an opportunity for understanding human social systems. Yet the literature has not adequately considered the disparate social processes by which texts are produced. Drawing on communication theory, we identify three common processes by which documents might be detectably similar in their textual features' authors sharing subject matter, sharing goals, and sharing sources. We hypothesize that these processes produce distinct, detectable relationships between authors in different kinds of textual overlap. We develop a novel ngram extraction technique to capture such signatures based on ngrams of different lengths. We test the hypothesis on a corpus where the author attributes are observable: the public statements of the members of the U.S. Congress. This article presents the first empirical finding that shows different social relationships are detectable through the structure of overlapping textual features. Our study has important implications for designing text modeling techniques to make sense of social phenomena from aggregate digital traces.

Can data from mobile phones be used to observe economic shocks and their consequences at multiple scales? Here we present novel methods to detect mass layoffs, identify individuals affected by them, and predict changes in aggregate unemployment rates using call detail record (CDR) data from mobile phones. Using the closure of a large manufacturing plant as a case study, we first describe structural break and Bayesian classification models to detect a mass layoff and the individuals affected by it by observing changes in calling behavior. For these affected individuals, we find measure significant declines in social behavior and mobility following job loss. We then apply these findings to the macro level and show that the same changes in these calling behaviors, aggregated at the regional level, can improve forecasts of unemployment rates.

Using data from a novel laboratory experiment on complex problem solving in which we varied the structure of 16-person networks, we investigate how an organization's network structure shapes the performance of problem-solving tasks. Problem solving, we argue, involves both exploration for information and exploration for solutions. Our results show that network clustering has opposite effects for these two important and complementary forms of exploration. Dense clustering encourages members of a network to generate more diverse information but discourages them from generating diverse theories; that is, clustering promotes exploration in information space but decreases exploration in solution space. Previous research, generally focusing on only one of those two spaces at a time, has produced an inconsistent understanding of the value of network clustering. By adopting an experimental platform on which information was measured separately from solutions, we bring disparate results under a single theoretical roof and clarify the effects of network clustering on problem-solving behavior and performance. The finding both provides a sharper tool for structuring organizations for knowledge work and reveals challenges inherent in manipulating network structure to enhance performance, as the communication structure that helps one determinant of successful problem solving may harm the other.

This article advocates a lesson plan for introductory comparative politics and elections courses. The authors argue that Wikipedia (yes, Wikipedia) provides a unique platform for improving learning outcomes and a useful social good from traditional student papers on elections. The proposed lesson plan can achieve this in at least three ways: (1) by providing social incentives for learning and a method for students to contribute to social science knowledge from their earliest courses, the incorporation of Wikipedia editing can improve student learning and retention; (2) incorporating an online information component can help both future students and researchers by improving the quality and quantity of easily accessible and well-referenced information about historical and upcoming elections; and (3) the use of the Wiki format is becoming increasingly common in both business and government. Teaching the basics of editing is an increasingly useful skill for students to learn for future employment.