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Öğe Applying machine learning to online data?: Beware! Computational social science requires care(IGI Global, 2022) Bayram, UlyaThe immense impact of social media on contemporary cultural evolution is undeniable, consequently declaring them an essential data source for computational social science studies. Alongside the advancements in natural language processing and machine learning disciplines, computational social science researchers continuously adapt new techniques to the data collected from social media. Although these developments are imperative for studying the sociological transformations in many communities, there are some inconspicuous problems on the horizon. This chapter addresses issues that may arise from the use of social media data, like biased models. It also discusses various obstacles associated with machine learning methods while also providing possible solutions and recommendations to overcome these struggles from an interdisciplinary perspective. In the long term, this chapter will guide computational social science researchers in their future studies, from things to be aware of with data collection to assembling an accurate experimental design. © 2022 by IGI Global. All rights reserved.Öğe Determining a Person's Suicide Risk by Voting on the Short-Term History of Tweets for the CLPsych 2021 Shared Task(Association for Computational Linguistics (ACL), 2021) Bayram, Ulya; Benhiba, LamiaIn this shared task, we accept the challenge of constructing models to identify Twitter users who attempted suicide based on their tweets 30 and 182 days before the adverse event's occurrence. We explore multiple machine learning and deep learning methods to identify a person's suicide risk based on the short-term history of their tweets. Taking the real-life applicability of the model into account, we make the design choice of classifying on the tweet level. By voting the tweet-level suicide risk scores through an ensemble of classifiers, we predict the suicidal users 30-days before the event with an 81.8% true-positives rate. Meanwhile, the tweet-level voting falls short on the six-month-long data as the number of tweets with weak suicidal ideation levels weakens the overall suicidal signals in the long term.Öğe Emotionally-Informed Models for Detecting Moments of Change and Suicide Risk Levels in Longitudinal Social Media Data(Association for Computational Linguistics (ACL), 2022) Bayram, Ulya; Benhiba, LamiaIn this shared task, we focus on detecting mental health signals in Reddit users’ posts through two main challenges: A) capturing mood changes (anomalies) from the longitudinal set of posts (called timelines), and B) assessing the users’ suicide risk-levels. Our approaches leverage emotion recognition on linguistic content by computing emotion/sentiment scores using pre-trained BERTs on users’ posts and feeding them to machine learning models, including XGBoost, Bi-LSTM, and logistic regression. For Task-A, we detect longitudinal anomalies using a sequence-to-sequence (seq2seq) autoencoder and capture regions of mood deviations. For Task-B, our two models utilize the BERT emotion/sentiment scores. The first computes emotion bandwidths and merges them with n-gram features, and employs logistic regression to detect users’ suicide risk levels. The second model predicts suicide risk on the timeline level using a Bi-LSTM on Task-A results and sentiment scores. Our results outperformed most participating teams and ranked in the top three in Task-A. In Task-B, our methods surpass all others and return the best macro and micro F1 scores. © 2022 Association for Computational Linguistics.Öğe Machine Learning and Medical Data: Predicting ICU Mortality and Re-admission Risks(Çanakkale Onsekiz Mart Üniversitesi, 2024) Bayram, Ulya; Roy, RuniaIntensive care units (ICUs) are divisions where critically ill patients are treated by medical experts. The unmet and vital need for automated clinical decision-making mechanisms is critical to maneuvering the large influx of patients. This became more apparent after the COVID-19 pandemic. Existing studies focus on determining the probability of patients dying in the ICUs and prioritizing patients in dire need. Only a few studies have calculated the patient's probability of returning to the ICUs after discharge. These studies reduce the problem into a binary task of predicting mortality or re-admission only. However, this is unrealistic since both outcomes are highly possible for each patient. In this interdisciplinary study, two main contributions are proposed for the automated clinical decision-making state-of-the-art: (1) using the real-life data collected from thousands of ICU patients by healthcare professionals, three possibilities (recovery, mortality, and returning to the intensive care unit within 30 days) are predicted for patients in intensive care instead of just one possibility. (2) A novel feature extraction approach is proposed by the biomedical expert in our team. Four machine learning algorithms are applied to the finalized feature set to understand the difference between the binary and the multi-class classification problems. Obtained results reach 78% success, proving the possibility of developing better clinical decision-making mechanisms for ICUs.Öğe Revealing the Reflections of the Pandemic by Investigating COVID-19 Related News Articles Using Machine Learning and Network Analysis(2022) Bayram, UlyaSocial media data can provide a general idea of people’s response towards the COVID-19 outbreak and its reflections, but it cannot be as objective as the news articles as a source of information. They are valuable sources of data for natural language processing research as they can reveal various paradigms about different phenomena related to the pandemic. This study uses a news collection spanning nine months from 2019 to 2020, containing COVID-19 related articles from various organizations around the world. The investigation conducted on the collection aims at revealing the repercussions of the pandemic at multiple levels. The first investigation discloses the most mentioned problems covered during the pandemic using statistics. Meanwhile, the second investigation utilizes machine learning to determine the most prevalent topics present within the articles to provide a better picture of the pandemic-induced issues. The results show that the economy was among the most prevalent problems. The third investigation constructs lexical networks from the articles, and reveals how every problem is related through nodes and weighted connections. The findings exhibit the need for more research using machine learning and natural language processing techniques on similar data collections to unveil the full repercussions of the pandemic.Öğe Structural inequalities exacerbate infection disparities(Nature Portfolio, 2025) Sajjadi, Sina; Toranj Simin, Pourya; Shadmangohar, Mehrzad; Taraktas, Basak; Bayram, Ulya; Ruiz-Blondet, Maria V.; Karimi, FaribaDuring the COVID-19 pandemic, the world witnessed a disproportionate infection rate among marginalized and low-income groups. Despite empirical evidence suggesting that structural inequalities in society contribute to health disparities, there has been little attempt to offer a computational and theoretical explanation to establish its plausibility and quantitative impact. Here, we focus on two aspects of structural inequalities: wealth inequality and social segregation. Our computational model demonstrates that (a) due to the inequality in self-quarantine ability, the infection gap widens between the low-income and high-income groups, and the overall infected cases increase, (b) social segregation between different socioeconomic status (SES) groups intensifies the disease spreading rates, and (c) the second wave of infection can emerge due to a false sense of safety among the medium and high SES groups. By performing two data-driven analyses, one on the empirical network and economic data of 404 metropolitan areas of the United States and one on the daily Covid-19 data of the City of Chicago, we verify that higher segregation leads to an increase in the overall infection cases and higher infection inequality across different ethnic/socioeconomic groups. These findings together demonstrate that reducing structural inequalities not only helps decrease health disparities but also reduces the spread of infectious diseases overall.Öğe The unknown knowns: a graph-based approach for temporal COVID-19 literature mining(Emerald, 2021) Bayram, Ulya; Roy, Runia; Assalil, Aqil; BenHiba, LamiaPurpose - The COVID-19 pandemic has sparked a remarkable volume of research literature, and scientists are increasingly in need of intelligent tools to cut through the noise and uncover relevant research directions. As a response, the authors propose a novel framework. In this framework, the authors develop a novel weighted semantic graph model to compress the research studies efficiently. Also, the authors present two analyses on this graph to propose alternative ways to uncover additional aspects of COVID-19 research. Design/methodology/approach - The authors construct the semantic graph using state-of-the-art natural language processing (NLP) techniques on COVID-19 publication texts (>100,000 texts). Next, the authors conduct an evolutionary analysis to capture the changes in COVID-19 research across time. Finally, the authors apply a link prediction study to detect novel COVID-19 research directions that are so far undiscovered. Findings - Findings reveal the success of the semantic graph in capturing scientific knowledge and its evolution. Meanwhile, the prediction experiments provide 79% accuracy on returning intelligible links, showing the reliability of the methods for predicting novel connections that could help scientists discover potential new directions. Originality/value - To the authors' knowledge, this is the first study to propose a holistic framework that includes encoding the scientific knowledge in a semantic graph, demonstrates an evolutionary examination of past and ongoing research and offers scientists with tools to generate new hypotheses and research directions through predictive modeling and deep machine learning techniques.Öğe Toward Suicidal Ideation Detection with Lexical Network Features and Machine Learning(Binghamton University Libraries, 2022) Bayram, Ulya; Lee, William; Santel, Daniel; Minai, Ali A.; Clark, Peggy O.; Glauser, Tracy; Pestian, JohnIn this study, we introduce a new network feature for detecting suicidal ideation from clinical texts and conduct various additional experiments to enrich the state of knowledge. We evaluate statistical features with and without stopwords, use lexical networks for feature extraction and classification, and compare the results with standard machine learning methods using a logistic classifier, a neural network, and a deep learning method. We utilize three text collections. The first two contain transcriptions of interviews conducted by experts with suicidal (n=161 patients that experienced severe ideation) and control subjects (n=153). The third collection con-sists of interviews conducted by experts with epilepsy patients, with a few of them admitting to experiencing suicidal ideation in the past (32 suicidal and 77 control). The selected methods detect suicidal ideation with an average area under the curve (AUC) score of 95% on the merged collection with high suicidal ideation, and the trained models generalize over the third collection with an average AUC score of 69%. Results reveal that lexical networks are promising for classification and feature extraction as successful as the deep learning model. We also observe that a logistic classifier’s performance was comparable with the deep learning method while promising explainability. © 2022, Binghamton University Libraries. All rights reserved.
