Adam D I Kramer’s research while affiliated with Meta and other places

Social influence is key in technology adoption, but its role in security-feature adoption is unique and remains unclear. Here, we analyzed how three Facebook security features' Login Approvals, Login Notifications, and Trusted Contacts-diffused through the social networks of 1.5 million people. Our results suggest that social influence affects one's likelihood to adopt a security feature, but its effect varies based on the observability of the feature, the current feature adoption rate among a potential adopter's friends, and the number of distinct social circles from which those feature-adopting friends originate. Curiously, there may be a threshold higher than which having more security feature adopting friends predicts for higher adoption likelihood, but below which having more feature-adopting friends predicts for lower adoption likelihood. Furthermore, the magnitude of this threshold is modulated by the attributes of a feature-features that are more noticeable (Login Approvals, Trusted Contacts) have lower thresholds.

One of the largest outstanding problems in computer security is the need for higher awareness and use of available security tools. One promising but largely unexplored approach is to use social proof: by showing people that their friends use security features, they may be more inclined to explore those features, too. To explore the efficacy of this approach, we showed 50,000 people who use Facebook one of 8 security announcements - 7 variations of social proof and 1 non-social control - to increase the exploration and adoption of three security features: Login Notifications, Login Approvals, and Trusted Contacts. Our results indicated that simply showing people the number of their friends that used security features was most effective, and drove 37% more viewers to explore the promoted security features compared to the non-social announcement (thus, raising awareness). In turn, as social announcements drove more people to explore security features, more people who saw social announcements adopted those features, too. However, among those who explored the promoted features, there was no difference in the adoption rate of those who viewed a social versus a non-social announcement. In a follow up survey, we confirmed that the social announcements raised viewer's awareness of available security features.

Significance We show, via a massive ( N = 689,003) experiment on Facebook, that emotional states can be transferred to others via emotional contagion, leading people to experience the same emotions without their awareness. We provide experimental evidence that emotional contagion occurs without direct interaction between people (exposure to a friend expressing an emotion is sufficient), and in the complete absence of nonverbal cues.

Happiness and other emotions spread between people in direct contact, but it is unclear whether massive online social networks also contribute to this spread. Here, we elaborate a novel method for measuring the contagion of emotional expression. With data from millions of Facebook users, we show that rainfall directly influences the emotional content of their status messages, and it also affects the status messages of friends in other cities who are not experiencing rainfall. For every one person affected directly, rainfall alters the emotional expression of about one to two other people, suggesting that online social networks may magnify the intensity of global emotional synchrony.

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.

R program that can be modified to implement the procedure with any dataset. R program that estimates the number of values needed per record to generate a specific classification error. (ZIP)

The Internet has fundamentally changed the way we conceptualize public opinion, even beyond the way it has altered the traditional measurement of public attitudes and behaviors using random sampling of the broader population. The data analyzed in this paper offer one of the first comprehensive evaluations of mass political discourse generated via messages posted publicly on Facebook (called “status updates”), and allow us to quantify political discussion on Facebook without relying on self-reported behavior, or data from a limited topical context or sample. We find that while less than 1% of the users who post status updates on Facebook on a given day post a political status update, discussion is remarkably responsive to real-world political events. We suggest that instead of thinking of social media data simply as a measurement tool, we re-conceptualize it as both a way to monitor public discourse and also as a driver of public opinion.

We report results from an exploratory analysis examining "last-minute" self-censorship, or content that is filtered after being written, on Facebook. We collected data from 3.9 million users over 17 days and associate self-censorship behavior with features describing users, their social graph, and the interactions between them. Our results indicate that 71% of users exhibited some level of last-minute self-censorship in the time period, and provide specific evidence supporting the theory that a user's "perceived audience" lies at the heart of the issue: posts are censored more frequently than comments, with status updates and posts directed at groups censored most frequently of all sharing use cases investigated. Furthermore, we find that: people with more boundaries to regulate censor more; males censor more posts than females and censor even more posts with mostly male friends than do females, but censor no more comments than females; people who exercise more control over their audience censor more content; and, users with more politically and age diverse friends censor less, in general. Copyright © 2013, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.

How does the level of electoral competition affect an individual’s political engagement? We utilize a unique collection of 113 million status messages on Facebook to answer this persistent question in the study of political behavior by comparing users’ political discussion during the 2008 presidential election in politically competitive versus uncompetitive states. "Battleground" state users are significantly more likely to discuss politics in the campaign season than are "blackout" state users, and political status message posting has a large effect on a person’s self-reported voter turnout. Day-to-day emotional and cognitive engagement with politics mediates approximately 31% of the relationship between exposure to political competition and turnout. This paper is one of the first efforts to use the massive quantity of data generated through online social media sites to explain the microfoundations of how people think, feel, and act on a daily basis in response to their political environment.