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Can Conversations on Reddit Forecast Future Economic Uncertainty? An Interpretable Machine Learning Approach

2023 | International Conference on Information Systems | Citations: 0
Authors: Jiang, Jinhang; Feng, Mei; Srinivasan, Karthik

Abstract: In recent years, social media has become an indispensable source of information ... Expand

Abstract: In recent years, social media has become an indispensable source of information through which public attitudes, opinions, and concerns can be studied and quantified. This paper proposes an interpretable machine learning framework for predicting the Equity Market-related Economic Uncertainty Index using features generated from a popular discussion forum on Reddit. Our framework consists of a series of custom preprocessing and analytics methods, including BERTopic for latent topic identification and regularized linear models. Using our framework, we evaluate explanatory models with different configurations over a large corpus of Reddit posts belonging to the personal finance category. Our analysis generates valuable insights about discussion topics on Reddit and their efficacy in accurately predicting future economic uncertainty. The study demonstrates the potential of using social media data and interpretable machine learning to inform economic forecasting research. Collapse

Semantic filters: uniform manifold approximation and projection

Topics: Reddit social media explainable artificial intelligence Python permutation feature importance

Methods: descriptive statistic time series analysis longitudinal research topic model experiment
A decision support framework to incorporate textual data for early student dropout prediction in higher education
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2023 | Decision Support Systems | Citations: 0
Authors: Phan, Minh; De Caigny, Arno; Coussement, Kristof

Abstract: Managing student dropout in higher education is critical, considering its substa ... Expand

Abstract: Managing student dropout in higher education is critical, considering its substantial impacts on students’ lives, academic institutions, and society as a whole. Using predictive modeling can be instrumental for this task, as a means to identify dropouts proactively on the basis of student characteristics and their academic performance. To enhance these predictions, textual student feedback also might be relevant; this article proposes a hybrid decision support framework that combines predictive modeling with student segmentation efforts. A real-life data set from a French higher education institution, containing information of 14,391 students and 62,545 feedback documents, confirms the superior performance of the proposed framework, in terms of the area under the curve and top decile lift, compared with various benchmarks. In contributing to decision support system research, this study (1) proposes a new framework for automatic, data-driven segmentation of students based on textual data; (2) compares multiple text representation methods and confirms that incorporating student textual feedback data improves the predictive performance of student dropout models; and (3) establishes useful insights to help decision-makers anticipate and manage student dropout behaviors. Collapse

Semantic filters: uniform manifold approximation and projection

Topics: decision support decision support system electronic mail

Methods: machine learning doc2vec data transformation BERT computational algorithm
Heterogeneous Information Fusion based Topic Detection from Social Media Data
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2023 | Information Systems Frontiers | Citations: 0
Authors: Rani, Seema; Kumar, Mukesh

Abstract: Due to the pervasive nature of social networking platforms, as well as the proli ... Expand

Abstract: Due to the pervasive nature of social networking platforms, as well as the proliferation of user generated content, the internet has become a repository of unstructured multimedia data. The use of this huge data for user experience enhancement is still a problem, where topic detection is one of the solutions to solve this issue, not having been explored in the literature for this application. Videos with similar content or related to the same topic can be grouped together with the help of topic detection methods. In this paper, a framework for topic detection using web videos textual metadata has been developed. The key contribution in this paper is to leverage multimedia metadata to find web video topics using a two-step process . First, we used transformer-based model to perform topic modeling for identification of topics from the heterogeneous textual data of web videos. Second, topic-based video retrieval has been accomplished using a classification approach. Further, experiments are carried out on a publicly available dataset to assess the performance of the proposed method. The proposed work is compared to the state-of-the-art methods Discriminative Probabilistic Models (DPM), Event clustering based method (ECBM),Multi-Modality Based Method (MMBM), Side-Information Based Method (SIBM), and Similarity Cascades(SC), which shows that the proposed system outperforms others in terms of Precision, Recall, F-measure and Accuracy. The experimental results demonstrates the effectiveness of proposed method for topic detection. Collapse

Semantic filters: uniform manifold approximation and projection

Topics: social media

Methods: machine learning term frequency–inverse document frequency BERT computational algorithm topic model
A Practical and Empirical Comparison of Three Topic Modeling Methods using a COVID-19 Corpus: LSA, LDA, and Top2Vec

2023 | HICSS | Citations: 0
Authors: Zengul, F.; Bulut, A.; Oner, N.; Ahmed, A.; Yadav, M.; Gray, H.; Ozaydin, B.

Abstract: This study was prepared as a practical guide for researchers interested in usin ... Expand

Abstract: This study was prepared as a practical guide for researchers interested in using topic modeling methodologies. This study is specially designed for those with difficulty determining which methodology to use. Many topic modeling methods have been developed \variation and combination of these techniques. For researchers from disciplines other than computer science may find it challenging to select a topic modeling methodology. We compared a recently developed topic modeling algorithm Top2Vec with two of the most conventional and frequently-used methodologiesLSA and LDA. As a study sample, we used a corpus of 65,292 COVID-19-focused abstracts. Among the 11 topics we identified in each methodology, we found high levels of correlation between LDA and Top2Vec results, followed by LSA and LDA and Top2Vec and LSA. We also provided information on computational resources we used to perform the analyses and provided practical guidelines and recommendations for researchers. Collapse

Semantic filters: uniform manifold approximation and projection

Topics: Python laptop computer cloud computing pandemic

Methods: topic model data transformation uniform manifold approximation and projection Gensim descriptive statistic
MediCoSpace: Visual Decision-Support for Doctor-Patient Consultations using Medical Concept Spaces from EHRs
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2023 | ACM Transactions on Management Information Systems | Citations: 0
Authors: van der Linden, Sanne; Sevastjanova, Rita; Funk, Mathias; El-Assady, Mennatallah

Abstract: Healthcare systems are under pressure from an aging population, rising costs, an ... Expand

Abstract: Healthcare systems are under pressure from an aging population, rising costs, and increasingly complex conditions and treatments. Although data are determined to play a bigger role in how doctors diagnose and prescribe treatments, they struggle due to a lack of time and an abundance of structured and unstructured information. To address this challenge, we introduce MediCoSpace, a visual decision-support tool for more efficient doctor-patient consultations. The tool links patient reports to past and present diagnoses, diseases, drugs, and treatments, both for the current patient and other patients in comparable situations. MediCoSpace uses textual medical data, deep-learning supported text analysis and concept spaces to facilitate a visual discovery process. The tool is evaluated by five medical doctors. The results show that MediCoSpace facilitates a promising, yet complex way to discover unlikely relations and thus suggests a path toward the development of interactive visual tools to provide physicians with more holistic diagnoses and personalized, dynamic treatments for patients. Collapse

Semantic filters: uniform manifold approximation and projection

Topics: decision support knowledge base visual analytics knowledge representation healthcare data

Methods: qualitative interview qualitative content analysis exploratory data analysis ontological modelling usability testing
Towards a Generation of Artificially Intelligent Strategy Tools: The SWOT Bot

2022 | European Conference On Information Systems | Citations: 0
Authors: Au, Christian; Winkler, Till; Paul, Herbert

Abstract: Strategy tools are widely used to inform the complex and unstructured decision ... Expand

Abstract: Strategy tools are widely used to inform the complex and unstructured decision making of firms. Although software has evolved to support strategy analysis, such digital strategy tools still require heavy manual work especially on the data input and processing levels, making their use time-intensive, costly, and susceptible to biases. This design research presents the 'SWOT Bot', a digital strategy tool that exploits recent advances in natural language processing (NLP) to perform a SWOT (strengths, weaknesses, opportunities, threats) analysis. Our artifact uses a feed reader, an NLP pipeline, and a visual interface to automatically extract information from a text corpus (e.g., analyst reports) and present it to the user. We argue that the SWOT Bot reduces time and adds objectivity to strategy analyses while allowing the human-in-the-loop to focus on value-adding tasks. Besides providing a functioning prototype, our work provides three general design principles for the development of next-generation digital strategy tools.Strategy tools-such as a SWOT, Porter's Five Forces, or the PEST model-are widely used among managers, analysts, and consultants in today's business practice (e.g., Jarzabkowski and Kaplan, 2014;Wright et al., 2013;Vaara andWhittington, 2012 andor Clark, 1997). While the term 'tool' generally refers to anything that helps perform a specific task, management tools cover any model, concept, framework, method, or technique that helps to solve a managerial problem (Hakala and Vuorinen, 2020; Jarzabkowski and Kaplan, 2014;Knott, 2008). Strategy tools are a subset of management tools that deal with strategic challenges, such as understanding a company's positioning and developing a new competitive strategy. Strategy tools are cognitive as well as material artifacts (Paroutis et al., 2015) that shape the mental models of strategists and hence affect both content and process of strategy work (Vuorinen et al., 2018). The application of strategy tools in practice is based on quantitative and qualitative inputs, where qualitative information seems to dominate. Although the use of strategy tools has evolved from pen and paper drawings to digital templates and dedicated software suites over the past decades (Ain et al., 2019;Arnott et al., 2017), our initial analysis found that digital tools for strategy available on the market lack support on the input, processing, and output levels. Contemporary digital strategy tools heavily rely on human tasks because strategic analysis is a complex process that integrates knowledge and data from heterogeneous and often unstructured sources (Hakala and Vuorinen, 2020;Jarzabkowski and Kaplan, 2015). While human involvement in strategy processes matters (Reeves and Ueda, 2016), it also comes with certain costs (Grant, 2016). Collapse

Semantic filters: uniform manifold approximation and projection

Topics: information systems strategy application programming interface Google+ SWOT analysis usability

Methods: natural language processing design artifact design science design requirement BERT
Social influence-based contrast language analysis framework for clinical decision support systems
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2022 | Decision Support Systems | Citations: 0
Authors: Yang, Xingwei; Joukova, Alexandra; Ayanso, Anteneh; Zihayat, Morteza

Abstract: Depression is a leading mental health problem affecting 300 million people globa ... Expand

Abstract: Depression is a leading mental health problem affecting 300 million people globally. Recent studies show that social networks provide a tremendous potential for mental health professionals as a source of supplemental information about their patients. This study presents a methodological framework for clinical decision support systems (CDSSs) through analysis of social network data to distinguish the language usage of individuals with early signs of depression (i.e., contrast language analysis). By analyzing the contrast language patterns of different user groups, we are able to uncover constructive and actionable insights into the pain points and characteristics of users with signs of depression as decision support mechanisms for clinicians during interven tion, (early) diagnosis and treatment plans. First, we discover terms that represent contrasting language by analyzing the percentage difference of terms in two user groups, labeled as”depressed” and”non-depressed” for ease of reference. Second, by building topic models based on social network contents, the topic-level contrast features are discovered. Finally, we consider the structure of the social network to discover the network-level contrast features. To illustrate the effectiveness of the proposed framework, we present a case study on early depression detection using a real-world dataset. The proposed framework has methodological contributions in enhancing the features and functionalities of CDSS for clinicians. It also contributes to evidence-based health research through cost-effective data and analytical insights that can supplement or improve the traditional survey and time-consuming interview methods. Collapse

Semantic filters: uniform manifold approximation and projection

Topics: social influence Facebook social network decision making social media

Methods: topic model experiment computational algorithm decision tree classification data transformation
Leveraging Natural Language Processing to Analyze Scientific Content: Proposal of an NLP pipeline for the field of Computer Vision

2021 | International Conference on Business Informatics | Citations: 0
Authors: Kortum, Henrik; Leimkühler, Max; Thomas, Oliver

Abstract: In this paper we elaborate the opportunity of using natural language processing ... Expand

Abstract: In this paper we elaborate the opportunity of using natural language processing to analyze scientific content both, from a practical as well as a theoretical point of view. Firstly, we conducted a literature review to summarize the status quo of using natural language processing for analyzing scientific content. We could identify different approaches, e.g., with the aim of clustering and tagging publications or to summarize scientific papers. Secondly, we conducted a case study where we used our proposed natural language processing pipeline to analyze scientific content about computer vision available at the database IEEE. Our method helped us to identify emerging trends in the recent years and give an overview of the field of research. Collapse

Semantic filters: uniform manifold approximation and projection

Topics: digital ecosystem open innovation database system Python

Methods: natural language processing literature study image processing case study deep learning
MD-Manifold: A Medical Distance Based Manifold Learning Approach for Heart Failure Readmission Prediction
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2021 | HICSS | Citations: 0
Authors: Wang, Shaodong; Li, Qing; Zhang, Wenli

Abstract: Dimension reduction is considered as a necessary technique in Electronic Healthc ... Expand

Abstract: Dimension reduction is considered as a necessary technique in Electronic Healthcare Records (EHR) data processing. However, no existing work addresses both of the two points: 1) generating low-dimensional representations for each patient visit; and 2) taking advantage of the well-organized medical concept structure as the domain knowledge. Hence, we propose a new framework to generate lowdimensional representations for medical data records by combining the concept-structure based distance with manifold learning. To demonstrate the efficacy, we generated low-dimensional representations for hospital visits of heart failure patients, which was further used for a 30-day readmission prediction. The experiments showed a great potential of the proposed representations (AUC = 60.7%) that has comparative predictive power of the state-of-the-art methods, including one hot encoding representations (AUC = 60.1%) and PCA representations (AUC = 58.3%), with much less training time (improved by 99%). The proposed framework can also be generalized to various healthcare-related prediction tasks, such as mortality prediction. Collapse

Semantic filters: uniform manifold approximation and projection

Topics: database system healthcare data

Methods: data transformation machine learning uniform manifold approximation and projection experiment principal component analysis

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Found 9 articles

Can Conversations on Reddit Forecast Future Economic Uncertainty? An Interpretable Machine Learning Approach

A decision support framework to incorporate textual data for early student dropout prediction in higher education

Heterogeneous Information Fusion based Topic Detection from Social Media Data

A Practical and Empirical Comparison of Three Topic Modeling Methods using a COVID-19 Corpus: LSA, LDA, and Top2Vec

MediCoSpace: Visual Decision-Support for Doctor-Patient Consultations using Medical Concept Spaces from EHRs

Towards a Generation of Artificially Intelligent Strategy Tools: The SWOT Bot

Social influence-based contrast language analysis framework for clinical decision support systems

Leveraging Natural Language Processing to Analyze Scientific Content: Proposal of an NLP pipeline for the field of Computer Vision

MD-Manifold: A Medical Distance Based Manifold Learning Approach for Heart Failure Readmission Prediction