Shaping the Emerging Norms of Using Large Language Models in Social Computing Research

The emergence of Large Language Models (LLMs) has brought both excitement and concerns to social computing research. On the one hand, LLMs offer unprecedented capabilities in analyzing vast amounts of textual data and generating human-like responses, enabling researchers to delve into complex social phenomena. On the other hand, concerns are emerging regarding the validity, privacy, and ethics of the research when LLMs are involved. This SIG aims at offering an open space for social computing researchers who are interested in understanding the impacts of LLMs to discuss their current practices, perspectives, challenges when engaging with LLMs in their everyday work and collectively shaping the emerging norms of using LLMs in social computing research

Unraveling User Perceptions of Interorganizational Information Sharing

Collecting large amounts of user information is becoming an increasingly important source of value for businesses. Such data sets may be expanded through engaging in value co-creation with other organizations. Sharing user information across organizations, however, might evoke users’ privacy concerns. Existing mechanisms and concepts developed in prior information privacy research on sharing information between one user and one organization may no longer apply as multiple organizations become involved. This creates the necessity to understand more granularly how users perceive privacy situations that involve sharing their information across organizations – and how their concerns may be alleviated through control mechanisms. Employing the lens of Communication Privacy Management (CPM) theory, we conceptualize this phenomenon as Interorganizational Information Sharing (IIS) and theorize on perceived uncertainty and control to unravel user perceptions in IIS. We present our ideas for a research model, as well as our planned methodology for empirical validation

Linking Data Sovereignty and Data Economy: Arising Areas of Tension

In the emerging information economy, data evolves as an essential asset and personal data in particular is used for data-driven business models. However, companies frequently leverage personal data without considering individuals’ data sovereignty. Therefore, we strive to strengthen individuals’ position in data ecosystems by combining concepts of data sovereignty and data economy. Our research design comprises an approach to design thinking iteratively generating, validating, and refining such concepts. As a result, we identified ten areas of tension that arise when linking data sovereignty and data economy. Subsequently, we propose initial solutions to resolve these tensions and thus contribute to knowledge about the development of fair data ecosystems benefiting both individuals’ sovereignty and companies’ access to data

A Game Theoretic Approach to Balance Privacy Risks and Familial Benefits

As recreational genomics continues to grow in its popularity, many people are afforded the opportunity to share their genomes in exchange for various services, including third-party interpretation (TPI) tools, to understand their predisposition to health problems and, based on genome similarity, to find extended family members. At the same time, these services have increasingly been reused by law enforcement to track down potential criminals through family members who disclose their genomic information. While it has been observed that many potential users shy away from such data sharing when they learn that their privacy cannot be assured, it remains unclear how potential users’ valuations of the service will affect a population’s behavior. In this paper, we present a game theoretic framework to model interdependent privacy challenges in genomic data sharing online. Through simulations, we find that in addition to the boundary cases when (1) no player and (2) every player joins, there exist pure-strategy Nash equilibria when a relatively small portion of players choose to join the genomic database. The result is consistent under different parametric settings. We further examine the stability of Nash equilibria and illustrate that the only equilibrium that is resistant to a random dropping of players is when all players join the genomic database. Finally, we show that when players consider the impact that their data sharing may have on their relatives, the only pure strategy Nash equilibria are when either no player or every player shares their genomic data

Giving Users Control Over How Peers Handle Their Data: A Design Science Study

In today’s interconnected world, Internet users are increasingly concerned about losing control over the data they share with peers, which indicates a need for higher levels of control and notification mechanisms. We address this need by building on design science methodology and developing a socio-technical artifact, i.e., a peer-privacy-friendly online messaging service. We draw on Malhotra et al.’s (2004) Internet Users’ Information Privacy Concerns framework and refine and evaluate our artifact via focus groups, interviews, and a survey among users of online messaging services. Our artifact provides senders with the ability to control how their personal information is processed by peers and allows receivers to be made aware of the sender’s privacy expectations. We contribute to the growing literature on peer privacy concerns by developing and evaluating design requirements, principles, and an instantiation that can mitigate peer privacy concerns that go beyond concerns about organizational data practices

“I thought you were okay”: Participatory Design with Young Adults to Fight Multiparty Privacy Conflicts in Online Social Networks

International audienceWhile sharing multimedia content on Online Social Networks (OSNs) has many benefits, exposing other people without obtaining permission could cause Multiparty Privacy Conflicts (MPCs). Earlier studies developed technical solutions and dissuasive approaches to address MPCs. However, none of these studies involved OSN users who have experienced MPCs, in the design process, possibly overlooking the valuable experiences these individuals might have accrued. To fill this gap, we recruited participants specifically from this population of users, and involved them in participatory design sessions aiming at ideating solutions to reduce the incidence of MPCs. To frame the activities of our participants, we borrowed terminology and concepts from a well known framework used in the justice systems. Over the course of several design sessions, our participants designed 10 solutions to mitigate MPCs. The designed solutions leverage different mechanisms, including preventing MPCs from happening, dissuading users from sharing, mending the harm, and educating users about the community standards. We discuss the open design and research opportunities suggested by the designed solutions and we contribute an ideal workflow that synthesizes the best of each solution. This study contributes to the innovation of privacy-enhancing technologies to limit the incidences of MPCs in OSNs

Community detection for access-control decisions: Analysing the role of homophily and information diffusion in Online Social Networks

Access-Control Lists (ACLs) (a.k.a. “friend lists”) are one of the most important privacy features of Online Social Networks (OSNs) as they allow users to restrict the audience of their publications. Nevertheless, creating and maintaining custom ACLs can introduce a high cognitive burden on average OSNs users since it normally requires assessing the trustworthiness of a large number of contacts. In principle, community detection algorithms can be leveraged to support the generation of ACLs by mapping a set of examples (i.e. contacts labelled as “untrusted”) to the emerging communities inside the user’s ego-network. However, unlike users’ access-control preferences, traditional community-detection algorithms do not take the homophily characteristics of such communities into account (i.e. attributes shared among members). Consequently, this strategy may lead to inaccurate ACL configurations and privacy breaches under certain homophily scenarios. This work investigates the use of community-detection algorithms for the automatic generation of ACLs in OSNs. Particularly, it analyses the performance of the aforementioned approach under different homophily conditions through a simulation model. Furthermore, since private information may reach the scope of untrusted recipients through the re-sharing affordances of OSNs, information diffusion processes are also modelled and taken explicitly into account. Altogether, the removal of gatekeeper nodes is further explored as a strategy to counteract unwanted data dissemination