25 research outputs found
Computational and Causal Approaches on Social Media and Multimodal Sensing Data: Examining Wellbeing in Situated Contexts
A core aspect of our lives is often embedded in the communities we are situated in. The interconnectedness of our interactions and experiences intertwines our situated context with our wellbeing. A better understanding of wellbeing will help us devise proactive and tailored support strategies. However, existing methodologies to assess wellbeing suffer from limitations of scale and timeliness. These limitations are surmountable by social and ubiquitous technologies. Given its ubiquity and wide use, social media can be considered a âpassive sensorâ that can act as a complementary source of unobtrusive, real-time, and naturalistic data to infer wellbeing. This dissertation leverages social media in concert with multimodal sensing data, which facilitate analyzing dense and longitudinal behavior at scale. This work adopts machine learning, natural language, and causal inference analysis to infer wellbeing of individuals and collectives, particularly in situated communities, such as college campuses and workplaces.
Before incorporating sensing modalities in practice, we need to account for confounds. One such confound that might impact behavior change is the phenomenon of âobserver effectâ --- that individuals may deviate from their typical or otherwise normal behavior because of the awareness of being âmonitoredâ. I study this problem by leveraging the potential of longitudinal and historical behavioral data through social media. Focused on a multimodal sensing study, I conduct a causal study to measure observer effect in social media behavior, and explain the observations through existing theory in psychology and social science. The findings provide recommendations to correcting biases due to observer effect in social media sensing for human behavior and wellbeing.
The novelties and contributions of this dissertation are four-fold. First, I use social media data that uniquely captures the behavior of situated communities. Second, I adopt theory-driven computational and causal methods to make conclusive research claims on wellbeing dynamics. Third, I address major challenges with methods to combine social media with multimodal sensing data for a comprehensive understanding of human behavior. Fourth, I draw interpretations and explanations of online-data-driven offline inferences. This dissertation situates the findings in an interdisciplinary context, including psychology and social science, and bears implications from theoretical, practical, design, methodological, and ethical perspectives catering to various stakeholders, including researchers, practitioners, and policymakers.Ph.D
Social media discussions predict mental health consultations on college campuses
The mental health of college students is a growing concern, and gauging the mental health needs of college students is difficult to assess in real-time and in scale. While social media has shown potential as a viable âpassive sensorâ of mental health, the construct validity and in-practice reliability of such computational assessments remain largely unexplored. Towards this goal, we study how assessing the mental health of college students using social media data correspond with ground-truth data of on-campus mental health consultations. For a large U.S. public university, we obtained ground-truth data of on-campus mental health consultations between 2011â2016, and collected 66,000 posts from the universityâs Reddit community. We adopted machine learning and natural language methodologies to measure symptomatic mental health expressions of depression, anxiety, stress, suicidal ideation, and psychosis on the social media data. Seasonal auto-regressive integrated moving average (SARIMA) models of forecasting on-campus mental health consultations showed that incorporating social media data led to predictions with r=0.86 and SMAPE=13.30, outperforming models without social media data by 41%. Our language analyses revealed that social media discussions during high mental health consultations months consisted of discussions on academics and career, whereas months of low mental health consultations saliently show expressions of positive affect, collective identity, and socialization. This study reveals that social media data can improve our understanding of college studentsâ mental health, particularly their mental health treatment needs
Predicting Opioid Use Outcomes in Minoritized Communities
Machine learning algorithms can sometimes exacerbate health disparities based
on ethnicity, gender, and other factors. There has been limited work at
exploring potential biases within algorithms deployed on a small scale, and/or
within minoritized communities. Understanding the nature of potential biases
may improve the prediction of various health outcomes. As a case study, we used
data from a sample of 539 young adults from minoritized communities who engaged
in nonmedical use of prescription opioids and/or heroin. We addressed the
indicated issues through the following contributions: 1) Using machine learning
techniques, we predicted a range of opioid use outcomes for participants in our
dataset; 2) We assessed if algorithms trained only on a majority sub-sample
(e.g., Non-Hispanic/Latino, male), could accurately predict opioid use outcomes
for a minoritized sub-sample (e.g., Latino, female). Results indicated that
models trained on a random sample of our data could predict a range of opioid
use outcomes with high precision. However, we noted a decrease in precision
when we trained our models on data from a majority sub-sample, and tested these
models on a minoritized sub-sample. We posit that a range of cultural factors
and systemic forms of discrimination are not captured by data from majority
sub-samples. Broadly, for predictions to be valid, models should be trained on
data that includes adequate representation of the groups of people about whom
predictions will be made. Stakeholders may utilize our findings to mitigate
biases in models for predicting opioid use outcomes within minoritized
communities
Partisan US News Media Representations of Syrian Refugees
We investigate how representations of Syrian refugees (2011-2021) differ
across US partisan news outlets. We analyze 47,388 articles from the online US
media about Syrian refugees to detail differences in reporting between left-
and right-leaning media. We use various NLP techniques to understand these
differences. Our polarization and question answering results indicated that
left-leaning media tended to represent refugees as child victims, welcome in
the US, and right-leaning media cast refugees as Islamic terrorists. We noted
similar results with our sentiment and offensive speech scores over time, which
detail possibly unfavorable representations of refugees in right-leaning media.
A strength of our work is how the different techniques we have applied validate
each other. Based on our results, we provide several recommendations.
Stakeholders may utilize our findings to intervene around refugee
representations, and design communications campaigns that improve the way
society sees refugees and possibly aid refugee outcomes
Health-seeking behaviour of stroke patients in a rural area of Bangladesh
Background: Improper health-seeking behaviours (HSB) have been correlated with detrimental health outcomes, elevated rates of illness and mortality. The study aimed to investigate how stroke patients in a rural community of Bangladesh seek health care.
Methods: A cross-sectional survey was conducted in the Raiganj sub-district of Sirajganj district from January to June 2016, using a validated screening tool to identify stroke patients at the household level. Neurologists confirmed the diagnosis after examining all suspected cases. Out of the 419 suspected cases identified during the screening process, 186 cases were officially reported after undergoing a confirmed diagnosis. Information on health-seeking behaviour was collected through face-to-face interviews with patients or their attendants.
Results: After experiencing a stroke, approximately 35% of patients received treatment from unregistered care providers and over 40% received treatment outside of a hospital setting. Males were significantly more likely than females to receive treatment from registered physicians or hospitals (P<.05 and P<.01). A significantly higher proportion of educated individuals sought healthcare from registered physicians or hospitals (P<.05). Although better health-seeking behaviour was observed among higher-income groups, the findings were not statistically significant. Around 67% of patients were found to be hypertensive, with about one-third of them not taking any medication for their elevated blood pressure. Approximately 37% of patients had elevated blood glucose levels but only 22% were taking medication.
Conclusion: A notable proportion of stroke patients in rural Bangladesh sought treatment from unqualified service providers. Health-seeking behaviour was associated with factors such as gender, education, and economic condition.
Bangabandhu Sheikh Mujib Medical University Journal 2023;16(2): 75-8
COVID-19 vaccine perceptions in the initial phases of US vaccine roll-out: an observational study on reddit.
BACKGROUND: Open online forums like Reddit provide an opportunity to quantitatively examine COVID-19 vaccine perceptions early in the vaccine timeline. We examine COVID-19 misinformation on Reddit following vaccine scientific announcements, in the initial phases of the vaccine timeline. METHODS: We collected all posts on Reddit (reddit.com) from January 1 2020 - December 14 2020 (n=266,840) that contained both COVID-19 and vaccine-related keywords. We used topic modeling to understand changes in word prevalence within topics after the release of vaccine trial data. Social network analysis was also conducted to determine the relationship between Reddit communities (subreddits) that shared COVID-19 vaccine posts, and the movement of posts between subreddits. RESULTS: There was an association between a Pfizer press release reporting 90% efficacy and increased discussion on vaccine misinformation. We observed an association between Johnson and Johnson temporarily halting its vaccine trials and reduced misinformation. We found that information skeptical of vaccination was first posted in a subreddit (r/Coronavirus) which favored accurate information and then reposted in subreddits associated with antivaccine beliefs and conspiracy theories (e.g. conspiracy, NoNewNormal). CONCLUSIONS: Our findings can inform the development of interventions where individuals determine the accuracy of vaccine information, and communications campaigns to improve COVID-19 vaccine perceptions, early in the vaccine timeline. Such efforts can increase individual- and population-level awareness of accurate and scientifically sound information regarding vaccines and thereby improve attitudes about vaccines, especially in the early phases of vaccine roll-out. Further research is needed to understand how social media can contribute to COVID-19 vaccination services
Recommended from our members
Gender Role Attitudes and Female Labour Participation: Evidence from Egypt
Causal Factors of Effective Psychosocial Outcomes in Online Mental Health Communities
Online mental health communities enable people to seek and provide support, and growing evidence shows the efficacy of community participation to cope with mental health distress. However, what factors of peer support lead to favorable psychosocial outcomes for individuals is less clear. Using a dataset of over 300K posts by âŒ39K individuals on an online community TalkLife, we present a study to investigate the effect of several factors, such as adaptability, diversity, immediacy, and the nature of support. Unlike typical causal studies that focus on the effect of each treatment, we focus on the outcome and address the reverse causal question of identifying treatments that may have led to the outcome, drawing on case-control studies in epidemiology. Specifically, we define the outcome as an aggregate of affective, behavioral, and cognitive psychosocial change and identify Case (most improved) and Control (least improved) cohorts of individuals. Considering responses from peers as treatments, we evaluate the differences in the responses received by Case and Control, per matched clusters of similar individuals. We find that effective support includes complex language factors such as diversity, adaptability, and style, but simple indicators such as quantity and immediacy are not causally relevant. Our work bears methodological and design implications for online mental health platforms, and has the potential to guide suggestive interventions for peer supporters on these platforms
Recommended from our members