Disagreeable Privacy Policies: Mismatches between Meaning and Users’ Understanding

Privacy policies are verbose, difficult to understand, take too long to read, and may be the least-read items on most websites even as users express growing concerns about information collection practices. For all their faults, though, privacy policies remain the single most important source of information for users to attempt to learn how companies collect, use, and share data. Likewise, these policies form the basis for the self-regulatory notice and choice framework that is designed and promoted as a replacement for regulation. The underlying value and legitimacy of notice and choice depends, however, on the ability of users to understand privacy policies. This paper investigates the differences in interpretation among expert, knowledgeable, and typical users and explores whether those groups can understand the practices described in privacy policies at a level sufficient to support rational decision-making. The paper seeks to fill an important gap in the understanding of privacy policies through primary research on user interpretation and to inform the development of technologies combining natural language processing, machine learning and crowdsourcing for policy interpretation and summarization. For this research, we recruited a group of law and public policy graduate students at Fordham University, Carnegie Mellon University, and the University of Pittsburgh (“knowledgeable users”) and presented these law and policy researchers with a set of privacy policies from companies in the e-commerce and news & entertainment industries. We asked them nine basic questions about the policies’ statements regarding data collection, data use, and retention. We then presented the same set of policies to a group of privacy experts and to a group of non-expert users. The findings show areas of common understanding across all groups for certain data collection and deletion practices, but also demonstrate very important discrepancies in the interpretation of privacy policy language, particularly with respect to data sharing. The discordant interpretations arose both within groups and between the experts and the two other groups. The presence of these significant discrepancies has critical implications. First, the common understandings of some attributes of described data practices mean that semi-automated extraction of meaning from website privacy policies may be able to assist typical users and improve the effectiveness of notice by conveying the true meaning to users. However, the disagreements among experts and disagreement between experts and the other groups reflect that ambiguous wording in typical privacy policies undermines the ability of privacy policies to effectively convey notice of data practices to the general public. The results of this research will, consequently, have significant policy implications for the construction of the notice and choice framework and for the US reliance on this approach. The gap in interpretation indicates that privacy policies may be misleading the general public and that those policies could be considered legally unfair and deceptive. And, where websites are not effectively conveying privacy policies to consumers in a way that a “reasonable person” could, in fact, understand the policies, “notice and choice” fails as a framework. Such a failure has broad international implications since websites extend their reach beyond the United States

Designing for Understanding: Helping Older Adults Understand Over-the-Counter Medication Information

This research is motivated by some of the challenges faced by the healthcare community in communicating health information to the public and the potential for user-centered technology design to address some of these limitations. Each year, thousands die or are injured due to adverse-drug events due to both prescription and over-the-counter medications. The integration of technology has improved the incidence rate for adverse-drug events due to prescription medications. Similarly, personal health records and other consumer-based health applications have been shown to be beneficial for helping individuals manage their health. Despite this growing body of research, little to no research has been conducted to gauge the possible effectiveness of technology created through a user-centered design process to assist consumers in understanding similar events due to over-the-counter medications. This research explores the implications for the design of interactive technology to help older adults understand the possible risk of an adverse drug events resulting from taking over-the-counter (OTC) medications. A user-centered design process was employed, leveraging various techniques to design technology to assist older adults with over-the-counter medication information. The three studies conducted for this research are part of an Exploratory Mixed-Methods Study, designed to identify current practices and challenges, identify opportunities for technology integration, and to examine the usability and effectiveness of the resultant technological artifacts for assisting older adults with over-the-counter medication information. Data collection included semi-structure interviews, surveys, questionnaires, and observations. Results from each study suggest that the technologies evaluated are useful for assisting older adults with over-the-counter medication information. Design recommendations identified throughout each phase are presented to provide insight on the technology features found useful and not so useful by older adults throughout the process of this research

Man vs machine – Detecting deception in online reviews

This study focused on three main research objectives: analyzing the methods used to identify deceptive online consumer reviews, evaluating insights provided by multi-method automated approaches based on individual and aggregated review data, and formulating a review interpretation framework for identifying deception. The theoretical framework is based on two critical deception-related models, information manipulation theory and self-presentation theory. The findings confirm the interchangeable characteristics of the various automated text analysis methods in drawing insights about review characteristics and underline their significant complementary aspects. An integrative multi-method model that approaches the data at the individual and aggregate level provides more complex insights regarding the quantity and quality of review information, sentiment, cues about its relevance and contextual information, perceptual aspects, and cognitive material

Paving the Way to Simpler: Experiencing from Maximizing Enrollment States in Streamlining Eligibility and Enrollment

Since 2009, the eight states (Alabama, Illinois, Louisiana, Massachusetts, New York, Utah, Virginia, and Wisconsin) participating in the Robert Wood Johnson Foundation's Maximizing Enrollment program have worked to streamline and simplify enrollment systems, policies, and processes for children and those eligible for health coverage in 2014. The participating states aimed to reduce enrollment barriers for consumers and administrative burdens in processing applications and renewals for staff by making improvements and simplifications at every step of the enrollment process. Although the states began their work before the enactment of the Affordable Care Act (ACA), their efforts positioned them well for implementation in 2014, and offer experiences and lessons that other states may find useful in their efforts to improve efficiency, lower costs, and promote responsible stewardship of limited public resources

A Visual Approach to Improving the Experience of Health Information for Vulnerable Individuals

Many individuals with low health literacy (LHL) and limited English proficiency (LEP) have poor experiences consuming health information: they find it unengaging, unappealing, difficult to understand, and un-motivating. These negative experiences may blunt, or even sabotage, the desired effect of communicating health information: to increase engagement and ability to manage health. It is imperative to find solutions to improve poor experiences of health information, because such experiences heighten vulnerability to poor health outcomes. We aimed to address a gap in the health literacy literature by studying the patient experience of health information and how visualization might be able to help. Our four studies involved patients presented with health information in various settings to improve understanding and management of their care. We used semi-structured interviews and observations to understand patient experiences of receiving personal health information in the hospital. We learned that the return of results is desired and has the potential to promote patient engagement with care. We developed a novel method to analyze LHL, LEP caregiver experience and information needs in the community setting. The novel method increased our understanding and ability to detect differences in experiences within the same ethnic group, based on language preference. Next, we interrogated the literature for a solution to easily communicate complicated health information to disinterested, LHL, LEP individuals. We found that visualizations can help increase interest, comprehension, support faster communication, and even help broach difficult topics. Finally, our findings were used to develop a novel prototype to improve experiences of consuming genetic risk information for those having LHL and LEP. Unlike traditional approaches that focus on communicating risk numbers and probabilities, the novelty of our approach was that we focused on communicating risk as a feeling. We achieved this by leveraging vicarious learning via real patient experience materials (e.g., quotes, videos) and empathy with an emotive relational agent. We evaluated and compared the prototype to standard methods of communicating genetic risk information via a mixed methods approach that included surveys, questionnaires, interviews, observations, image analysis, and facial analysis. Main outcome variables were perceived ease of understanding, comprehension, emotional response, and motivation. We employed t-tests, ANOVAs, directed content analysis, correlation, regression, hierarchical clustering, and Chernoff faces to answer the research questions. All variables were significantly different for the prototype compared to the standard method, except for motivation as rated by 32 LHL, LEP community members. Findings revealed that LHL, LEP individuals have difficulty appropriately processing standard methods of communicating risk information, such as risk numbers supported by visual aids. Further, appealing visuals may inappropriately increase confidence in understanding of information. Visualizations affected emotions, which influenced perceived ease of understanding and motivation to take action on the information. Comprehension scores did not correlate with perceived ease of understanding, emotional response, or motivation. Findings suggest that providing access to comprehensible health information may not be enough to motivate patients to engage with their care; providing a good experience (taking into account the aesthetics and emotional response) of health information may be essential to optimize outcomes

eWOM for public institutions: application to the case of the Portuguese Army

Social media platforms provide easy access to the public opinion (called electronic word-of-mouth), which can be collected and analyzed to extract knowledge about the reputation of an organization. Monitoring this reputation in the public sector may bring several benefits for its institutions, especially in supporting decision-making and developing marketing campaigns. Thus, to offer a solution aimed at the needs of this sector, the goal of this research was to develop a methodology capable of extracting relevant information about eWOM in social media, using text mining and natural language processing techniques. Our goal was achieved through a methodology capable of handling the small amount of information regarding public state organizations in social media. Additionally, our work was validated using the context of the Portuguese Army and revealed the potential to provide indicators of institutional reputation. Our results present one of the first cases of the application of this type of techniques to an Army organization and to understand its negative reputation among the population.info:eu-repo/semantics/acceptedVersio

Tailored retrieval of health information from the web for facilitating communication and empowerment of elderly people

A patient, nowadays, acquires health information from the Web mainly through a “human-to-machine” communication process with a generic search engine. This, in turn, affects, positively or negatively, his/her empowerment level and the “human-to-human” communication process that occurs between a patient and a healthcare professional such as a doctor. A generic communication process can be modelled by considering its syntactic-technical, semantic-meaning, and pragmatic-effectiveness levels and an efficacious communication occurs when all the communication levels are fully addressed. In the case of retrieval of health information from the Web, although a generic search engine is able to work at the syntactic-technical level, the semantic and pragmatic aspects are left to the user and this can be challenging, especially for elderly people. This work presents a custom search engine, FACILE, that works at the three communication levels and allows to overcome the challenges confronted during the search process. A patient can specify his/her information requirements in a simple way and FACILE will retrieve the “right” amount of Web content in a language that he/she can easily understand. This facilitates the comprehension of the found information and positively affects the empowerment process and communication with healthcare professionals

eWOM for public institutions: application to the case of the Portuguese Army

Social media platforms provide easy access to the public opinion (called electronic word-of-mouth), which can be collected and analyzed to extract knowledge about the reputation of an organization. Monitoring this reputation in the public sector may bring several benefits for its institutions, especially in supporting decision-making and developing marketing campaigns. Thus, to offer a solution aimed at the needs of this sector, the goal of this research was to develop a methodology capable of extracting relevant information about eWOM in social media, using text mining and natural language processing techniques. Our goal was achieved through a methodology capable of handling the small amount of information regarding public state organizations in social media. Additionally, our work was validated using the context of the Portuguese Army and revealed the potential to provide indicators of institutional reputation. Our results present one of the first cases of the application of this type of techniques to an Army organization and to understand its negative reputation among the population

DESIGN AND EXPLORATION OF NEW MODELS FOR SECURITY AND PRIVACY-SENSITIVE COLLABORATION SYSTEMS

Collaboration has been an area of interest in many domains including education, research, healthcare supply chain, Internet of things, and music etc. It enhances problem solving through expertise sharing, ideas sharing, learning and resource sharing, and improved decision making. To address the limitations in the existing literature, this dissertation presents a design science artifact and a conceptual model for collaborative environment. The first artifact is a blockchain based collaborative information exchange system that utilizes blockchain technology and semi-automated ontology mappings to enable secure and interoperable health information exchange among different health care institutions. The conceptual model proposed in this dissertation explores the factors that influences professionals continued use of video- conferencing applications. The conceptual model investigates the role the perceived risks and benefits play in influencing professionals’ attitude towards VC apps and consequently its active and automatic use

Text as Environment: A Deep Reinforcement Learning Text Readability Assessment Model

Evaluating the readability of a text can significantly facilitate the precise expression of information in a written form. The formulation of text readability assessment demands the identification of meaningful properties of the text and correct conversion of features to the right readability level. Sophisticated features and models are being used to evaluate the comprehensibility of texts accurately. Still, these models are challenging to implement, heavily language-dependent, and do not perform well on short texts. Deep reinforcement learning models are demonstrated to be helpful in further improvement of state-of-the-art text readability assessment models. The main contributions of the proposed approach are the automation of feature extraction, loosening the tight language dependency of text readability assessment task, and efficient use of text by finding the minimum portion of a text required to assess its readability. The experiments on Weebit, Cambridge Exams, and Persian readability datasets display the model's state-of-the-art precision, efficiency, and the capability to be applied to other languages.Comment: 8 pages, 2 figures, 6 equations, 7 table