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 “passive sensor” 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 “observer effect” --- that individuals may deviate from their typical or otherwise normal behavior because of the awareness of being “monitored”. 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 “passive sensor” 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 2011–2016, and collected 66,000 posts from the university’s 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 students’ 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

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 ∼39K 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

Computational and Causal Approaches on Social Media and Multimodal Sensing Data: Examining Wellbeing in Situated Contexts [Koustuv Saha]

Presented online on February 18, 2021 at 12:30 p.m.Ceara Byrne is a 6th year PhD Candidate in Human Computer Interaction at Georgia Tech. She is a designer and developer who believes in creating things around empathy before everything be it physical or digital. She uses a data-driven approach to develop products that put people and their needs first.Azra Ismail is a PhD candidate in Human-Centered Computing at Georgia Tech, advised by Dr. Neha Kumar. She is also the co-founder of MakerGhat, an organization based in Mumbai that creates safe and open makerspaces to support communities in realizing their ideas for local social, economic, and political change.Koustuv Saha is a Computer Science Ph.D. candidate in the School of Interactive Computing at Georgia Tech. He works in the Social Dynamics and Wellbeing Lab, and is advised by Dr. Munmun De Choudhury. He did my undergraduate in Computer Science and Engineering from IIT Kharagpur, and hold an overall industry research experience of five years. He is interested in the interdisciplinary areas of computational social science and social computing.Runtime: 50:41 minutesCeara Byrne: Technology for Working and Service Dogs In the Animal Computer Interaction (ACI) lab, I create and study technologies that improve communication between working dogs, such as dogs trained for search and rescue, and their handlers. In particular, my research focuses on improving the outcomes of service and working dog training. Not all dogs that go into these training programs as puppies have the temperament to become successful assistance and working animals. However, it is very difficult to determine if a dog has a temperament suitable for a service or working animal early on in life. That is where my research comes in. In my work, I investigate how aspects of canine temperament can be detected from interactions with sensors, often placed inside of dog toys that I design and build. After running tests where dogs interact with these sensors, I develop models that use sensor data to predict the success of assistance dogs in advanced training.Azra Ismail: Human-Centered Design of Artificial Intelligence Systems for Frontline Health There has been growing interest in the application of Artificial Intelligence (AI) in frontline health, motivated by a shortage of skilled medical experts and medical equipment, particularly in the Global South. The global COVID-19 pandemic has drawn attention to the potential for these efforts, but also their many limitations. These systems may increase the work burden on frontline health workers, many of whom are women engaged in underpaid and invisible care and data work. In this talk, I will examine the AI for Global Health discourse, the gaps in current efforts, and opportunities for design, while centering the perspectives of frontline health workers. I will draw on data from three years of ethnographic fieldwork that I have conducted with women frontline health workers and women from underserved communities in Delhi (India), as well as an extensive literature review of ongoing AI efforts in this space. Finally, I will draw on a rich body of literature on Human-Computer Interaction for Development (HCI4D), post-development critique, and transnational feminist theory to discuss lessons for AI efforts that target social good more broadly.Koustuv Saha: Computational and Causal Approaches on Social Media and Multimodal Sensing Data: Examining Wellbeing in Situated Contexts A core aspect of our social lives is often embedded in situated communities, such as our workplaces, neighborhoods, localities, and school/college campuses. The inter-connectedness and inter-dependencies of our interactions, experiences, and concerns intertwine our situated context with our wellbeing. A better understanding of our wellbeing and psychosocial dynamics will help us devise strategies to address our wellbeing through proactive and tailored support strategies. However, existing methodologies to assess wellbeing suffer from limitations of scale and timeliness. Parallelly, given its ubiquity and widespread use, social media can be considered a “passive sensor” that can act as a complementary source of unobtrusive, real-time, and naturalistic data to infer wellbeing. This talk will present an overview of computational and causal approaches for leveraging social media in concert with complementary multimodal sensing data to examine wellbeing in situated contexts. This talk will show how theory-driven computational methods can be applied on unique social media and complementary multimodal sensing data to capture attributes of human behavior and psychosocial dynamics in situated communities

JobLex: A Lexico-Semantic Knowledgebase of Occupational Information Descriptors

Technological advancements in several work sectors have influenced evolution of the landscape of work at an unprecedented speed, leading to the demand of continuous skill development [1,8]. In turn, this interests a number of stakeholders spanning across academia and industry in a number of disciplines including labor economics, who leverage large-scale data available from a variety of offline and online sources (e.g., resumes, job portals, professional social networking such as LinkedIn, search engine, job databases, etc.) [9,11,12]. On these data streams, describing job aspects and skills vary extensively, confounded by factors such as self-presentation, subjective perspectives on soft and hard skills, audience, and intrinsic traits such as personality and mindset [2,4,7,15,17]. Such data analyses require a taxonomy of keywords that are associated with skills per job description or type. However, most databases are only limited — they do not capture variants, typos, abbreviations, or internet slangs that are used on social media or in informal settings [6]. To facilitate research in this space, our work builds on a well-validated dictionary of occupational descriptors (O*Net) to propose a method, and correspondingly a knowledgebase, JobLex of occupational descriptors that can be used in computational social science and organizational studies [13]. We publish both our script and an example lexicon (for Twitter) for purposes of research and practical application. Item Description: This work publishes a methodology to build knowledgebase of occupational information descriptors. We also provide access to the codebase and example lexicon