An official website of the United States government
Voluntary sharing of personal information is at the heart of user engagement on social media and central to platforms' business models. From the users' perspective, so-called self-disclosure is closely connected with both privacy risks and social rewards. Prior work has studied contextual influences on self-disclosure, from platform affordances and interface design to user demographics and perceived social capital. Our work takes a mixed-methods approach to understand the contextual information which might be integrated in the development of privacy-enhancing technologies. Through observational study of several Reddit communities, we explore the ways in which topic of discussion, group norms, peer effects, and audience size are correlated with personal information sharing. We then build and test a prototype privacy-enhancing tool that exposes these contextual factors. Our work culminates in a browser extension that automatically detects instances of self-disclosure in Reddit posts at the time of posting and provides additional context to users before they post to support enhanced privacy decision-making. We share this prototype with social media users, solicit their feedback, and outline a path forward for privacy-enhancing technologies in this space.
more »
« less
Content Sharing Design for Social Welfare in Networked Disclosure Game
This work models the costs and benefits of per- sonal information sharing, or self-disclosure, in online social networks as a networked disclosure game. In a networked population where edges rep- resent visibility amongst users, we assume a leader can influence network structure through content promotion, and we seek to optimize social wel- fare through network design. Our approach con- siders user interaction non-homogeneously, where pairwise engagement amongst users can involve or not involve sharing personal information. We prove that this problem is NP-hard. As a solution, we develop a Mixed-integer Linear Programming algorithm, which can achieve an exact solution, and also develop a time-efficient heuristic algo- rithm that can be used at scale. We conduct nu- merical experiments to demonstrate the properties of the algorithms and map theoretical results to a dataset of posts and comments in 2020 and 2021 in a COVID-related Subreddit community where privacy risks and sharing tradeoffs were particularly pronounced.
more »
« less
- Award ID(s):
- 2027757
- PAR ID:
- 10467854
- Publisher / Repository:
- In Uncertainty in Artificial Intelligence, Proceedings of Machine Learning Research
- Date Published:
- Page Range / eLocation ID:
- 973-983
- Format(s):
- Medium: X
- Location:
- https://proceedings.mlr.press/v216/jia23b/jia23b.pdf
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
We investigate relationships between online self-disclosure and received social support and user engagement during the COVID-19 crisis. We crawl a total of 2,399 posts and 29,851 associated comments from the r/COVID19_support subreddit and manually extract fine-grained personal information categories and types of social support sought from each post. We develop a BERT-based ensemble classifier to automatically identify types of support offered in users’ comments. We then analyze the effect of personal information sharing and posts’ topical, lexical, and sentiment markers on the acquisition of support and five interaction measures (submission scores, the number of comments, the number of unique commenters, the length and sentiments of comments). Our findings show that: (1) users were more likely to share their age, education, and location information when seeking both informational and emotional support as opposed to pursuing either one; (2) while personal information sharing was positively correlated with receiving informational support when requested, it did not correlate with emotional support; (3) as the degree of self-disclosure increased, information support seekers obtained higher submission scores and longer comments, whereas emotional support seekers’ self-disclosure resulted in lower submission scores, fewer comments, and fewer unique commenters; and (4) post characteristics affecting audience response differed significantly based on types of support sought by post authors. These results provide empirical evidence for the varying effects of self-disclosure on acquiring desired support and user involvement online during the COVID-19 pandemic. Furthermore, this work can assist support seekers hoping to enhance and prioritize specific types of social support and user engagement.more » « less
-
Well-intentioned users sometimes enable the spread of misinformation due to limited context about where the information originated and/or why it is spreading. Building upon recommendations based on prior research about tackling misinformation, we explore the potential to support media literacy through platform design. We develop and design an intervention consisting of a tweet trajectory-to illustrate how information reached a user-and contextual cues-to make credibility judgments about accounts that amplify, manufacture, produce, or situate in the vicinity of problematic content (AMPS). Using a research through design approach, we demonstrate how the proposed intervention can help discern credible actors, challenge blind faith amongst online friends, evaluate the cost of associating with online actors, and expose hidden agendas. Such facilitation of credibility assessment can encourage more responsible sharing of content. Through our findings, we argue for using trajectory-based designs to support informed information sharing, advocate for feature updates that nudge users with reflective cues, and promote platform-driven media literacy.more » « less
-
null (Ed.)Abstract Smartphone location sharing is a particularly sensitive type of information disclosure that has implications for users’ digital privacy and security as well as their physical safety. To understand and predict location disclosure behavior, we developed an Android app that scraped metadata from users’ phones, asked them to grant the location-sharing permission to the app, and administered a survey. We compared the effectiveness of using self-report measures commonly used in the social sciences, behavioral data collected from users’ mobile phones, and a new type of measure that we developed, representing a hybrid of self-report and behavioral data to contextualize users’ attitudes toward their past location-sharing behaviors. This new type of measure is based on a reflective learning paradigm where individuals reflect on past behavior to inform future behavior. Based on data from 380 Android smartphone users, we found that the best predictors of whether participants granted the location-sharing permission to our app were: behavioral intention to share information with apps, the “FYI” communication style, and one of our new hybrid measures asking users whether they were comfortable sharing location with apps currently installed on their smartphones. Our novel, hybrid construct of self-reflection on past behavior significantly improves predictive power and shows the importance of combining social science and computational science approaches for improving the prediction of users’ privacy behaviors. Further, when assessing the construct validity of the Behavioral Intention construct drawn from previous location-sharing research, our data showed a clear distinction between two different types of Behavioral Intention: self-reported intention to use mobile apps versus the intention to share information with these apps. This finding suggests that users desire the ability to use mobile apps without being required to share sensitive information, such as their location. These results have important implications for cybersecurity research and system design to meet users’ location-sharing privacy needs.more » « less
-
null (Ed.)Data and information privacy is a major concern of today’s world. More specifically, users’ digital privacy has become one of the most important issues to deal with, as advancements are being made in information sharing technology. An increasing number of users are sharing information through text messages, emails, and social media without proper awareness of privacy threats and their consequences. One approach to prevent the disclosure of private information is to identify them in a conversation and warn the dispatcher before the conveyance happens between the sender and the receiver. Another way of preventing information (sensitive) loss might be to analyze and sanitize a batch of offline documents when the data is already accumulated somewhere. However, automating the process of identifying user-centric privacy disclosure in textual data is challenging. This is because the natural language has an extremely rich form and structure with different levels of ambiguities. Therefore, we inquire after a potential framework that could bring this challenge within reach by precisely recognizing users’ privacy disclosures in a piece of text by taking into account - the authorship and sentiment (tone) of the content alongside the linguistic features and techniques. The proposed framework is considered as the supporting plugin to help text classification systems more accurately identify text that might disclose the author’s personal or private information.more » « less
