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This chapter examines privacy as a multilevel concept. While current conceptualizations of privacy tend to focus on the individual level, technological advancements are making group privacy increasingly important to understand. This chapter offers a typology of both groups and group privacy to establish a framework for conceptualizing how privacy operates beyond the individual level. The chapter describes several contemporary practices that influence the privacy of multiple actors and considers the dynamics of multi-stakeholder privacy decision-making. Potential tensions that exist between the rights and preferences of individual group members or between individuals and the group as a whole are also examined. Finally, recommendations for tools and other mechanisms to support collaborative privacy management and group privacy protection are provided.

Online social networks are major hubs of communications that are used to share information by societies worldwide. However, the ability to freely communicate on these platforms is increasingly restricted in countries across the globe and existing technological solutions do not fully address the needs of affected communities. In this paper we explore the design process of SecurePost, a novel tool that allows verified group anonymity to those communicating publicly on online social networks. We present survey-based research and ethnographic interviews of communities vulnerable to censorship conducted in Zambia, Turkey, and Mongolia between 2013 to 2016. We use analysis of this data to ground our work. We explore needs and requirements of users such as modes of censorship, resistance to network disruption, and appropriate platform consideration. We outline our technological solution and expand on how on-the-ground research of user communities guides technological requirements.

Machine learning and data mining threaten personal privacy, and many tools exist to help users protect their privacy (e.g., available privacy settings on Facebook, anonymization and encryption of personal data, etc.). But such technologies also pose threats to "group privacy," which is a concept scholars know relatively little about. Moreover, there are few tools to address the problem of protecting group privacy. This paper discusses an emerging class of software applications and services that pose new risks to group privacy by revealing group-level information based on individual information, such as social media postings or fitness app usage. The paper describes the results of two experiments that empirically establish the concept of group privacy and shows that it affects user perceptions of and interactions with information technology. The findings serve as a call to developers to design tools for group privacy protection.

Power is an integral part of group dynamics and when deception is involved, it affects the process of group decision-making as well as the outcomes of those decisions. We examined 95 groups playing a popular party card game called Mafia in which some players were randomly assigned the role of Spies, and others, the role of Villagers. Spies concealed their identity and deceived the other naive players (the Villagers), who were assumed to be truthful. Data were collected from University students around the world, including in Israel, Singapore, Hong Kong, Fiji, Zambia, and three locations within the U.S. The results revealed differences in the way that players from those different cultural locations exhibited dominance and rated the trustworthiness of other players. In general, more dominant players were seen as more trustworthy, although that was moderated by the cultural location. Spies (deceivers) were viewed as less dominant than Villagers (truth-tellers). Males were rated as more dominant, especially when in the Spy role. The implications for this study on the study of groups and power relations among group members are discussed.

Identifying the visual focus of attention (VFOA) in multi-person discussions is related to many types of social interactions such as dominance/deference, like/dislike, and trust/distrust relationships. However, identifying the VFOA of a person is challenging since it changes rapidly in dynamic discussions. We propose ICAF (Iterative Collective Attention Focus), a system that simultaneously tracks the VFOA and speaking probabilities of all people. In order to apply the system previously unseen videos, we propose a lightly supervised technique to train the model in ICAF with performance which is only slightly worse than in the fully supervised case. Our system can visualize the predicted VFOA and speaking probabilities and interaction networks in videos.

This chapter begins with a case study of Strava, a fitness app that inadvertently exposed sensitive military information even while protecting individual users' information privacy. The case study is analyzed as an example of how recent advances in algorithmic group inference technologies threaten privacy, both for individuals and for groups. It then argues that while individual privacy from big data analytics is well understood, group privacy is not. Results of an experiment to better understand group privacy are presented. Findings show that group and individual privacy are psychologically distinct and uniquely affect people's evaluations, use, and tolerance for a fictitious fitness app. The chapter concludes with a discussion of group-inference technologies ethics and offers recommendations for fitness app designers.

Written by Michelle A. Amazeen, Fabrício Benevenuto, Nadia M. Brashier, Robert M. Bond, Lia C. Bozarth, Ceren Budak, Ullrich K. H. Ecker, Lisa K. Fazio, Emilio Ferrara, Andrew J. Flanagin, Ales-sandro Flammini, Deen Freelon, Nir Grinberg, Ralph Hertwig, Kathleen Hall Jamieson, Kenneth Jo-seph, Jason J. Jones, R. Kelly Garrett, Daniel Kreiss, Shannon McGregor, Jasmine McNealy, Drew Margolin, Alice Marwick, FiIippo Menczer, Miriam J. Metzger, Seungahn Nah, Stephan Lewan-dowsky, Philipp Lorenz-Spreen, Pablo Ortellado, Irene Pasquetto, Gordon Pennycook, Ethan Porter, David G. Rand, Ronald Robertson, Briony Swire-Thompson, Francesca Tripodi, Soroush Vosoughi, Chris Vargo, Onur Varol, Brian E. Weeks, John Wihbey, Thomas J. Wood, & Kai-Cheng Yang

An experiment using a representative sample of US. Internet users in the context of online encyclopedias is conducted to understand how the type of information source (expert-generated, user-genera...

Biases in language influence how we interact with each other and society at large. Language affirming gender stereotypes is often observed in various contexts today, from recommendation letters and Wikipedia entries to fiction novels and movie dialogue. Yet to date, there is little agreement on the methodology to quantify gender stereotypes in natural language (specifically the English language). Common methodology (including those adopted by companies tasked with detecting gender bias) rely on a lexicon approach largely based on the original BSRI study from 1974. In this paper, we reexamine the role of gender stereotype detection in the context of modern tools, by comparatively analyzing efficacy of lexicon-based approaches and end-to-end, ML-based approaches prevalent in state-of-the-art natural language processing systems. Our efforts using a large dataset show that even compared to an updated lexicon-based approach, end-to-end classification approaches are significantly more robust and accurate, even when trained by moderately sized corpora.

For millions of workers, online job listings provide the first point of contact to potential employers. As a result, job listings and their word choices can significantly affect the makeup of the responding applicant pool. Here, we study the effects of potentially gender-biased terminology in job listings, and their impact on job applicants, using a large historical corpus of 17 million listings on LinkedIn spanning 10 years. We develop algorithms to detect and quantify gender bias, validate them using external tools, and use them to quantify job listing bias over time. We then perform a user survey over two user populations (N 1=469 , N 2=273 ) to validate our findings and to quantify the end-to-end impact of such bias on applicant decisions. Our findings show gender-bias has decreased significantly over the last 10 years. More surprisingly, we find that impact of gender bias in listings is dwarfed by our respondents' inherent bias towards specific job types.

Today, the internet serves a wealth of news sources that encourages selective exposure to attitude-consistent and likeminded information. Several cues have been proposed to influence selective exposure, including partisanship, familiarity, and differential framing techniques. This study investigates the effects of news brand partisanship and news lead partisanship on selective exposure behaviors to internet news stories. With online news, it is possible that news brands believed to have a particular partisan bias may feature stories with an opposite partisan bias. This paper asks which of the two tested selective exposure cues used in this study participants respond to in an online news search environment. Using a non-college adult sample of 382 participants, this study confirms that selective exposure behavior is robust in a simulated news search environment and that news brand partisanship is a more powerful predictor of exposure than is news lead partisanship. The study finds, however, that the news br...

Comparatively optimistic people feel that they are less susceptible to risks than are others. This study investigated predictors and outcomes of comparative optimism about privacy risks on Facebook. Results from a nationally representative survey of adult U.S. Facebook users (N = 1,156) show that users exhibit comparative optimism in believing that they are less susceptible to privacy risks than are average users. However, unlike prior findings in offline contexts, this study finds that comparatively optimistic Facebook users do not appear to engage in riskier privacy behaviors. The findings of this study shed light on how privacy decision-making may be different in social networking contexts compared to other contexts due to the networked nature of the communication platform.

Fake news emerged as an apparent global problem during the 2016 U.S. Presidential election. Addressing it requires a multidisciplinary effort to define the nature and extent of the problem, detect fake news in real time, and mitigate its potentially harmful effects. This will require a better understanding of how the Internet spreads content, how people process news, and how the two interact. We review the state of knowledge in these areas and discuss two broad potential mitigation strategies: better enabling individuals to identify fake news, and intervention within the platforms to reduce the attention given to fake news. The cooperation of Internet platforms (especially Facebook, Google, and Twitter) with researchers will be critical to understanding the scale of the issue and the effectiveness of possible interventions., This is the accepted version of the article, posted to comply with public access mandates. The final published version is available at https://doi.org/10.1126/science.aao2998

Visual focus of attention in multi-person discussions is a crucial nonverbal indicator in tasks such as inter-personal relation inference, speech transcription, and deception detection. However, predicting the focus of attention remains a challenge because the focus changes rapidly, the discussions are highly dynamic, and the people's behaviors are inter-dependent. Here we propose ICAF (Iterative Collective Attention Focus), a collective classification model to jointly learn the visual focus of attention of all people. Every person is modeled using a separate classifier. ICAF models the people collectively---the predictions of all other people's classifiers are used as inputs to each person's classifier. This explicitly incorporates inter-dependencies between all people's behaviors. We evaluate ICAF on a novel dataset of 5 videos (35 people, 109 minutes, 7604 labels in all) of the popular Resistance game and a widely-studied meeting dataset with supervised prediction. See our demo at https://cs.dartmouth.edu/dsail/demos/icaf. ICAF outperforms the strongest baseline by 1%--5% accuracy in predicting the people's visual focus of attention. Further, we propose a lightly supervised technique to train models in the absence of training labels. We show that light-supervised ICAF performs similar to the supervised ICAF, thus showing its effectiveness and generality to previously unseen videos.

Older adults can form parasocial relationships with favorite television characters, and these relationships often serve as sources of companionship. Using the selection, optimization, and compensation model as a guiding framework, this study explored whether or not attachment anxiety and relationship quality with romantic partners and children moderate the associations between parasocial relationships and depressive symptoms. A series of three-way interactions emerged in predicting depressive symptoms. Among other findings, stronger parasocial relationships predicted an increase in depressive symptoms for anxious older adults in low-quality parent-child relationships. Older adults' individual characteristics and family relationships may qualify how they experience parasocial relationships with favorite television characters.