Christopher J. Fariss

A 61-Million-Person Experiment in Social Influence and Political Mobilization

Human behaviour is thought to spread through face-to-face social networks, but it is difficult to identify social influence effects in observational studies, and it is unknown whether online social networks operate in the same way–. Here we report results from a randomized controlled trial of political mobilization messages delivered to 61 million Facebook users during the 2010 US congressional elections. The results show that the messages directly influenced political self-expression, information seeking and real-world voting behaviour of millions of people. Furthermore, the messages not only influenced the users who received them but also the users’ friends, and friends of friends. The effect of social transmission on real-world voting was greater than the direct effect of the messages themselves, and nearly all the transmission occurred between ‘close friends’ who were more likely to have a face-to-face relationship. These results suggest that strong ties are instrumental for spreading both online and real-world behaviour in human social networks.

Respect for Human Rights Has Improved Over Time: Modeling the Changing Standard of Accountability

According to indicators of political repression currently used by scholars, human rights practices have not improved over the past 35 years, despite the spread of human rights norms, better monitoring, and the increasing prevalence of electoral democracy. I argue that this empirical pattern is not an indication of stagnating human rights practices. Instead, it reflects a systematic change in the way monitors, like Amnesty International and the U.S. State Department, encounter and interpret information about abuses. The standard of accountability used to assess state behaviors becomes more stringent as monitors look harder for abuse, look in more places for abuse, and classify more acts as abuse. In this article, I present a new, theoretically informed measurement model, which generates unbiased estimates of repression using existing data. I then show that respect for human rights has improved over time and that the relationship between human rights respect and ratification of the UN Convention Against Torture is positive, which contradicts findings from existing research.

Inferring Tie Strength from Online Directed Behavior

Some social connections are stronger than others. People have not only friends, but also best friends. Social scientists have long recognized this characteristic of social connections and researchers frequently use the term tie strength to refer to this concept. We used online interaction data (specifically, Facebook interactions) to successfully identify real-world strong ties. Ground truth was established by asking users themselves to name their closest friends in real life. We found the frequency of online interaction was diagnostic of strong ties, and interaction frequency was much more useful diagnostically than were attributes of the user or the user’s friends. More private communications (messages) were not necessarily more informative than public communications (comments, wall posts, and other interactions).

Dynamic Patterns of Human Rights Practices

A science of human rights requires valid comparisons of repression levels across time and space. Though extensive data collection efforts have made such comparisons possible in principle, statistical measures based on simple additive scales have made them rare in practice. This article uses a dynamic measurement model that contrasts with current approaches by (1) accounting for the fact that human rights indicators can be more or less informative about the latent level of repression, (2) allowing realistic descriptions of measurement uncertainty in the form of credible intervals, and (3) providing a theoretical motivation for modeling temporal dependence in human rights levels. We present several techniques, which demonstrate that the dynamic ordinal IRT model outperforms the static version of the model.

Measuring Mutual Dependence between State Repressive Actions

This study explores the relationships between state violations of different human rights. Though most quantitative studies in international relations treat different types of repressive behaviors as either independent or arising from the same underlying process, significant insights are gained by conceptualizing different human rights violations as separate but dependent processes. We present a theoretical framework for conceptualizing the mechanisms relating human rights practices and produce a novel measurement strategy based on network analysis for exploring these relationships. We illustrate high levels of complementarity between most human rights practices. Substitution effects, in contrast, are occasionally substantial but relatively rare. Finally, using empirically informed Monte Carlo analyses, we present predictions regarding likely sequences of rights violations resulting in extreme violations of different physical integrity rights.

The Path to Asylum in the US and the Determinants for Who Gets In and Why

The migration of political asylum seekers into the United States has long been a salient political topic; however, social scientists have yet to examine this process in its entirety and in the context of political changes since September 11, 2001. Previous research shows that humanitarian and strategic interests are important for decisions made by asylum officers but that research overlooks the decisions made by immigration judges. Here we examine decisions made by both asylum officers and immigration judges using data from a global set of countries, from 1999 to 2004. We find that the waning importance of human rights is more pronounced for asylum officers than for immigration judges after the attack on the World Trade Center. We also find that language heritage, specifically for asylum seekers from English-, Spanish-, and Arabic-speaking countries, substantially affects acceptance rates made by both decision-makers between the two time periods of our study.

Human Rights Texts: Converting Human Rights Primary Source Documents into Data

We introduce and make publicly available a large corpus of digitized primary source human rights documents which are published annually by monitoring agencies that include Amnesty International, Human Rights Watch, the Lawyers Committee for Human Rights, and the United States Department of State. In addition to the digitized text, we also make available and describe document-term matrices, which are datasets that systematically organize the word counts from each unique document by each unique term within the corpus of human rights documents. To contextualize the importance of this corpus, we describe the development of coding procedures in the human rights community and several existing categorical indicators that have been created by human coding of the human rights documents contained in the corpus. We then discuss how the new human rights corpus and the existing human rights datasets can be used with a variety of statistical analyses and machine learning algorithms to help scholars understand how human rights practices and reporting have evolved over time. We close with a discussion of our plans for dataset maintenance, updating, and availability.

Yahtzee: An Anonymized Group Level Matching Procedure

Researchers often face the problem of needing to protect the privacy of subjects while also needing to integrate data that contains personal information from diverse data sources. The advent of computational social science and the enormous amount of data about people that is being collected makes protecting the privacy of research subjects ever more important. However, strict privacy procedures can hinder the process of joining diverse sources of data that contain information about specific individual behaviors. In this paper we present a procedure to keep information about specific individuals from being “leaked” or shared in either direction between two sources of data without need of a trusted third party. To achieve this goal, we randomly assign individuals to anonymous groups before combining the anonymized information between the two sources of data. We refer to this method as the Yahtzee procedure, and show that it performs as predicted by theoretical analysis when we apply it to data from Facebook and public voter records.

Uncovering Patterns Among Latent Variables: Human Rights and De Facto Judicial Independence

In this paper, we reexamine the relationship between judicial independence and state respect for human rights by taking advantage of new latent measures of both constructs. In our analysis, we demonstrate a simple method for incorporating the uncertainty of these latent variables. Our results provide strong support for theoretical and empirical claims that independent courts constrain human rights abuses. Although we show that independent courts influence state behavior, the strength of the estimated relationship depends upon whether and to what degree empirical models account for uncertainty in the measurement of the latent variables.

Social Endorsement Cues and Political Participation

Which individuals are most responsive to get-out-the-vote (GOTV) messages that emphasize the social aspects of voting? Recent literature has shown that GOTV messages that emphasize the social environment in which an individual is embedded are particularly effective at increasing voting rates. Until now, we have not had good estimates for the types of people for whom social GOTV messages are most effective. We report a new set of disaggregated results of a randomized controlled trial of political mobilization messages delivered to 61 million Facebook users during the 2010 U.S. Congressional elections. The results suggest that social endorsement cues are differentially effective for different types of political behaviors—political expression, information seeking, and voting—and for different kinds of people, based on both demographic and social characteristics, raising new questions about the mechanisms explaining social pressure effects.