Discrepancies between Objectively Assessed and Self-Reported Daily Social Media Time in the Age of Platform Swinging

Building on Junco’s (2013) study examining the accuracy of self-reported computer-specific time on Facebook, the current study investigates the accuracy of self-reported time on multiple social media (SM) platforms across multiple electronic devices and evaluates whether reporting accuracy is systematically associated with participant sex, individual SM platform in question, or total number of SM platforms used. Participants were 320 college students who downloaded software on their computers, tablets, and smartphones to track their active use of Facebook, Twitter, Instagram, and Snapchat over a 2-week surveillance period and then self-reported their daily average minutes on each platform immediately after. Larger proportions of students over- estimated than under-estimated their use, with the largest overestimations found on Snapchat and Instagram. Relative to males, females logged significantly more SM time and were less accurate in reporting. Overall, the likelihood of substantial inaccuracies in reporting total SM time and time on most individual platforms increased with each additional SM platform participants reported using. Findings from this study cast further doubt on the validity of self-report SM measures in the present SM landscape and underscore the need for either data analytic strategies to adjust for systematic reporting biases or a shift towards objective time-tracking methods.

Disruption Leads to Methodological and Analytic Innovation in Developmental Sciences: Recommendations for Remote Administration and Dealing With Messy Data

The COVID-19 pandemic has impacted data collection for longitudinal studies in developmental sciences to an immeasurable extent. Restrictions on conducting in-person standardized assessments have led to disruptive innovation, in which novel methods are applied to increase participant engagement. Here, we focus on remote administration of behavioral assessment. We argue that these innovations in remote assessment should become part of the new standard protocol in developmental sciences to facilitate data collection in populations that may be hard to reach or engage due to burdensome requirements (e.g., multiple in-person assessments). We present a series of adaptations to developmental assessments (e.g., Mullen) and a detailed discussion of data analytic approaches to be applied in the less-than-ideal circumstances encountered during the pandemic-related shutdown (i.e., missing or messy data). Ultimately, these remote approaches actually strengthen the ability to gain insight into developmental populations and foster pragmatic innovation that should result in enduring change.

Ethical and Regulatory Challenges of Emerging Health Technologies

The advances in biotechnology and computer and data sciences opened the way for innovative approaches to human healthcare. Meanwhile, they created many ethical and regulatory dilemmas such as pervasive global inequalities and security and risk to data privacy. The assessment of health technology is a systematic multidisciplinary process that aims to examine the benefits and risks associated with its use including medical, social, economic, and ethical impacts. It is used to inform policy and optimize decision-making. The advance of technology is creating significant challenges to healthcare regulators who strive to balance patient safety to fostering innovation. The FDA and EMA are modernizing their regulatory approaches to foster innovation in digital technology and improve safety and applicability to patients. On the other hand, data analytic technologies have been introduced into regulatory decision processes.

Exploring Big Data Analytic Approaches to Cancer Blog Text Analysis

In this article, the authors explore the potential of a big data analytics approach to unstructured text analytics of cancer blogs. The application is developed using Cloudera platform's Hadoop MapReduce framework. It uses several text analytics algorithms, including word count, word association, clustering, and classification, to identify and analyze the patterns and keywords in cancer blog postings. This article establishes an exploratory approach to involving big data analytics methods in developing text analytics applications for the analysis of cancer blogs. Additional insights are extracted through various means, including the development of categories or keywords contained in the blogs, the development of a taxonomy, and the examination of relationships among the categories. The application has the potential for generalizability and implementation with health content in other blogs and social media. It can provide insight and decision support for cancer management and facilitate efficient and relevant searches for information related to cancer.

Similar Trajectory Search with Spatio-Temporal Deep Representation Learning

Similar trajectory search is a crucial task that facilitates many downstream spatial data analytic applications. Despite its importance, many of the current literature focus solely on the trajectory’s spatial similarity while neglecting the temporal information. Additionally, the few papers that use both the spatial and temporal features based their approach on a traditional point-to-point comparison. These methods model the importance of the spatial and temporal aspect of the data with only a single, pre-defined balancing factor for all trajectories, even though the relative spatial and temporal balance can change from trajectory to trajectory. In this article, we propose the first spatio-temporal, deep-representation-learning-based approach to similar trajectory search. Experiments show that utilizing both features offers significant improvements over existing point-to-point comparison and deep-representation-learning approach. We also show that our deep neural network approach is faster and performs more consistently compared to the point-to-point comparison approaches.

Handling Cellwise Outliers by Sparse Regression and Robust Covariance

We propose a data-analytic method for detecting cellwise outliers. Given a robust covariance matrix, outlying cells (entries) in a row are found by the cellFlagger technique which combines lasso regression with a stepwise application of constructed cutoff values. The penalty term of the lasso has a physical interpretation as the total distance that suspicious cells need to move in order to bring their row into the fold. For estimating a cellwise robust covariance matrix we construct a detection-imputation method which alternates between flagging outlying cells and updating the covariance matrix as in the EM algorithm. The proposed methods are illustrated by simulations and on real data about volatile organic compounds in children.