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Every day, patients access and generate online health content through a variety of channels, creating an ever-expanding sea of digital data. At the same time, proponents of public health have recently called for timely, granular, and actionable data to address a range of public health issues, stressing the need for social listening platforms that can identify and compile this valuable data. Yet previous attempts at social listening in healthcare have yielded mixed results, largely because they have failed to incorporate sufficient context to understand the communications they seek to analyze. Guided by activity theory to design HealthSense, we propose a platform for efficiently sensing and gathering data across the web for real-time analysis to support public health outcomes. HealthSense couples theory-guided content analysis and graph propagation with graph neural networks (GNNs) to assess the relevance and credibility of information, as well as intelligently navigate the complex online channel landscape, leading to significant improvements over existing social listening tools. We demonstrate the value of our artifact in gathering information to support two exemplar public health tasks: (1) performing postmarket drug surveillance for adverse reactions and (2) addressing the opioid crisis by monitoring for potent synthetic opioids released into communities. Our results across data, user, and event experiments show that effective design artifacts can enable better outcomes across both automated and human decision-making contexts, making social listening for public health possible, practical, and valuable. Through our design process, we extend activity theory to address the complexities of modern online communication platforms, where information resides not only in the collection of individual communication activities but also in the complex network of interactions among them. [ABSTRACT FROM AUTHOR]

Despite widespread awareness of risks, significant investments in cybersecurity protection, and substantial economic incentives to avoid security breaches, organizations remain vulnerable to phishing attacks. Phishing research has informed effective practical interventions to address phishing susceptibility that emphasize the importance of broadly applicable IT security knowledge. Yet employees still frequently fall victim to phishing attempts. To help understand why, we conceptualize phishing susceptibility as the failure to differentiate between deceptive and legitimate information processing requests that occur within the context of an employee's typical job responsibilities. We apply this contextual lens to identify characteristics of knowledge workers' organizational task and social context that may enhance or diminish performance in detecting deception in phishing email attempts. To test our hypotheses, we conducted a study in which employees of the finance division of a large university encountered simulated email-based phishing attempts as part of their normal work routine. We found evidence supporting our hypotheses that an individual's susceptibility to phishing attacks is influenced by their position in the knowledge flows of the organization and by the impact of workgroup responsibilities on their cognitive processing. We contend that phishing susceptibility is not merely a matter of IT security knowledge but is also influenced by contextualized, multilevel influences on information processing. As phishing attacks are increasingly targeted to specific organizational settings, it is even more important to incorporate this contextualized information processing view of phishing susceptibility. [ABSTRACT FROM AUTHOR]

In this study, the authors propose a set of key elements that impact the success of an active learning implementation: content delivery, active learning methods, physical environment, technology enhancement, incentive alignment, and educator investment. Through a range of metrics the authors present preliminary evidence that students in courses implementing these elements learn more while exerting similar effort, demonstrate greater understanding of course content, and establish closer connections with fellow students as well as instructors.

Echo chambers and filter bubbles are potent metaphors that encapsulate widespread public fear that the use of social media may limit the information that users encounter or consume online. Specifically, the concern is that social media algorithms combined with tendencies to interact with like-minded others both limits users’ exposure to diverse viewpoints and encourages the adoption of more extreme ideological positions. Yet empirical evidence about how social media shapes information consumption is inconclusive. We articulate how characteristics of platform algorithms and users’ online social networks may combine to shape user behavior. We bring greater conceptual clarity to this phenomenon by expanding beyond discussion of a binary presence or absence of echo chambers and filter bubbles to a richer set of outcomes incorporating changes in both diversity and slant of users’ information sources. Using a data set with over four years of web browsing history for a representative panel of nearly 200,000 U.S. adults, we analyzed how individuals’ social media usage was associated with changes in the information sources they chose to consume. We find differentiated impacts on news consumption by platform. Increased use of Facebook was associatedwith increased information source diversity and a shift toward more partisan sites in news consumption; increased use of Reddit with increased diversity and a shift toward more moderate sites; and increased use of Twitter with little to no change in either. Our results demonstrate the value of adopting a nuanced multidimensional view of how social media use may shape information consumption. [ABSTRACT FROM AUTHOR]

Adverse event detection is critical for many real-world applications including timely identification of product defects, disasters, and major socio-political incidents. In the health context, adverse drug events account for countless hospitalizations and deaths annually. Since users often begin their information seeking and reporting with online searches, examination of search query logs has emerged as an important detection channel. However, search context - including query intent and heterogeneity in user behaviors – is extremely important for extracting information from search queries, and yet the challenge of measuring and analyzing these aspects has precluded their use in prior studies. We propose DeepSAVE, a novel deep learning framework for detecting adverse events based on user search query logs. DeepSAVE uses an enriched variational autoencoder encompassing a novel query embedding and user modeling module that work in concert to address the context challenge associated with search-based detection of adverse events. Evaluation results on three large real-world event datasets show that DeepSAVE outperforms existing detection methods as well as comparison deep learning auto encoders. Ablation analysis reveals that each component of DeepSAVE significantly contributes to its overall performance. Collectively, the results demonstrate the viability of the proposed architecture for detecting adverse events from search query logs. [ABSTRACT FROM AUTHOR]

Background: As people increasingly turn to web-based sources for medical information, we offer some insight into what website traits influence patients' credibility assessment. Specifically, we control for brand and content length, while manipulating three website traits: authorship, format, and tone. Furthermore, we focus on medical skepticism to understand how patients with high levels of medical skepticism may react to web-based medical information differently. Medical skepticism is related to a patient's doubts about the value of conventional medical care; therefore, skeptics may have different practices and criteria when conducting their own web-based medical searches.Objective: The aim of this study is to evaluate how website traits affect the likelihood that patients follow web-based medical advice and how this varies among patients with differing levels of medical skepticism.Methods: This web-based experiment presented participants with a hypothetical medical situation about leg cramps and offered a website with treatment advice. We varied the websites the participants observed across three traits: authorship (patient or physician), format (article or discussion forum), and tone (objective or experience-based). The 2305 participants were randomly assigned to 1 of 8 possible conditions and then asked the extent to which they would follow the advice. Health care patterns and coverage, demographics, and the participants' level of medical skepticism were captured.Results: Our participants were selected to be demographically representative of the population of internet users in the United States. The 2305 complete responses were analyzed with ordinary least squares regression. Our analysis reveals that people are more likely to accept web-based medical advice authored by a physician (P<.001) and presented with an objective tone (P=.006), but these preferences erode as the levels of medical skepticism increase. Medical skepticism was measured by means of a previously established index on a 0 to 4 scale, and the average score was 2.26 (SD 0.84). Individuals with higher levels of medical skepticism were more likely to follow web-based medical advice in our experiment (P<.001). Individuals with low levels of medical skepticism found the discussion forum format more credible, whereas those with high levels of medical skepticism preferred the article format (P=.03). We discuss the interactions between medical skepticism and all 3 website traits manipulated in the experiment.Conclusions: Our findings suggest that, generally, physician authorship and an objective tone create more persuasive web-based medical advice. However, there are differences in how patients with high levels of medical skepticism react to web-based medical resources. Medical skeptics are less discerning regarding the author's credentials and the presentation tone of the information. Furthermore, patients with higher levels of medical skepticism prefer article format presentations, whereas those with lower levels of medical skepticism prefer discussion forum-style formatting. [ABSTRACT FROM AUTHOR]