4,823 research outputs found
Understanding the Dynamics between Vaping and Cannabis Legalization Using Twitter Opinions
Cannabis legalization has been welcomed by many U.S. states but its role in
escalation from tobacco e-cigarette use to cannabis vaping is unclear.
Meanwhile, cannabis vaping has been associated with new lung diseases and
rising adolescent use. To understand the impact of cannabis legalization on
escalation, we design an observational study to estimate the causal effect of
recreational cannabis legalization on the development of pro-cannabis attitude
for e-cigarette users. We collect and analyze Twitter data which contains
opinions about cannabis and JUUL, a very popular e-cigarette brand. We use
weakly supervised learning for personal tweet filtering and classification for
stance detection. We discover that recreational cannabis legalization policy
has an effect on increased development of pro-cannabis attitudes for users
already in favor of e-cigarettes.Comment: Published at ICWSM 202
Social media mental health analysis framework through applied computational approaches
Studies have shown that mental illness burdens not only public health and productivity but also established market economies throughout the world. However, mental disorders are difficult to diagnose and monitor through traditional methods, which heavily rely on interviews, questionnaires and surveys, resulting in high under-diagnosis and under-treatment rates. The increasing use of online social media, such as Facebook and Twitter, is now a common part of people’s everyday life. The continuous and real-time user-generated content often reflects feelings, opinions, social status and behaviours of individuals, creating an unprecedented wealth of person-specific information. With advances in data science, social media has already been increasingly employed in population health monitoring and more recently mental health applications to understand mental disorders as well as to develop online screening and intervention tools. However, existing research efforts are still in their infancy, primarily aimed at highlighting the potential of employing social media in mental health research. The majority of work is developed on ad hoc datasets and lacks a systematic research pipeline. [Continues.]</div
The American Academy of Health Behavior 2021 Annual Scientific Meeting: Transforming the Narrative to Meet Emerging Health Behavior Challenges
The American Academy of Health Behavior (AAHB) hosted it\u27s 21th Annual Scientific Meeting online in March 2021. The meeting\u27s theme was Transforming the Narrative to Meet Emerging Health Behavior Challenges . This publication describes the meeting theme and includes the refereed abstracts presented at the 2021 Annual Scientific Meeting
Causally Regularized Learning with Agnostic Data Selection Bias
Most of previous machine learning algorithms are proposed based on the i.i.d.
hypothesis. However, this ideal assumption is often violated in real
applications, where selection bias may arise between training and testing
process. Moreover, in many scenarios, the testing data is not even available
during the training process, which makes the traditional methods like transfer
learning infeasible due to their need on prior of test distribution. Therefore,
how to address the agnostic selection bias for robust model learning is of
paramount importance for both academic research and real applications. In this
paper, under the assumption that causal relationships among variables are
robust across domains, we incorporate causal technique into predictive modeling
and propose a novel Causally Regularized Logistic Regression (CRLR) algorithm
by jointly optimize global confounder balancing and weighted logistic
regression. Global confounder balancing helps to identify causal features,
whose causal effect on outcome are stable across domains, then performing
logistic regression on those causal features constructs a robust predictive
model against the agnostic bias. To validate the effectiveness of our CRLR
algorithm, we conduct comprehensive experiments on both synthetic and real
world datasets. Experimental results clearly demonstrate that our CRLR
algorithm outperforms the state-of-the-art methods, and the interpretability of
our method can be fully depicted by the feature visualization.Comment: Oral paper of 2018 ACM Multimedia Conference (MM'18
The Effects of Digitally Delivered Nudges in a Corporate Wellness Program
We investigate how two digitally delivered nudges, namely light social support (nonverbal cues such as kudos or likes) and motivational messaging, affect employees’ self-reported physical activity in an online, corporate wellness program. Within this unique field setting, using data from several years, we found evidence that both types of nudges provide benefits beyond the effect of cash incentives. However, the effects vary by individual, depending on whether the employee is actively engaging in physical activity, and by time, depending on how long the employee has been in the wellness program. We found light social support to be less effective over time, while motivational messages were found to be more effective with the duration in the program and generally more effective for physically inactive users. Our findings have implications for the design of wellness systems, suggesting different approaches depending on an employee’s current activity level and tenure in the progra
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