A Theory-based Deep-Learning Approach to Detecting Disinformation in Financial Social Media

2023 | Information Systems Frontiers | Citations: 0

Authors: Chung, Wingyan; Zhang, Yinqiang; Pan, Jia

Abstract: The spreading of disinformation in social media threatens cybersecurity and unde ... Expand

Abstract: The spreading of disinformation in social media threatens cybersecurity and undermines market efficiency. Detecting disinformation is challenging due to large volumes of social media content and a rapidly changing environment. This research developed and validated a theory-based, novel deep-learning approach (called TRNN) to disinformation detection. Grounded in social and psychological theories, TRNN uses deep-learning and data-centric augmentation to enhance disinformation detection in financial social media. Temporal and contextual information is encoded as specific knowledge about human-validated disinformation, which was identified from our unique collection of 745,139 financial social media messages about four U.S. high-tech company stocks and their fine-grained trading data. TRNN uses multiple series of long short-term memory (LSTM) recurrent neurons to learn dynamic and hidden patterns to support disinformation detection. Our experimental findings show that TRNN significantly outperformed widely-used machine learning techniques in terms of precision, recall, F-score and accuracy, achieving consistently better classification performance in disinformation detection. A case study of Apple Inc.’s stock price movement demonstrates the potential usability of TRNN for secure knowledge management. The research contributes to developing novel approach and model, producing new information systems artifacts and dataset, and providing empirical findings of detecting online disinformation. Collapse

Topics: disinformation social media Twitter fake news usability

Methods: machine learning experiment design artifact deep learning recurrent neural network

Theories: psychological theory

Using Isolation Forest and Alternative Data Products to Overcome Ground Truth Data Scarcity for Improved Deep Learning-based Agricultural Land Use Classification Models

2023 | HICSS | Citations: 0

Authors: García Pereira, Agustín; Porwol, Lukasz; Ojo, Adegboyega

Abstract: High-quality labelled datasets represent a cornerstone in the development of dee ... Expand

Abstract: High-quality labelled datasets represent a cornerstone in the development of deep learning models for land use classification. The high cost of data collection, the inherent errors introduced during data mapping efforts, the lack of local knowledge, and the spatial variability of the data hinder the development of accurate and spatially-transferable deep learning models in the context of agriculture. In this paper, we investigate the use of Isolation Forest (IF), an anomaly detection algorithm, to reduce noise in a large-scale, low-resolution alternative ground truth dataset used to train land use deep learning models. We use a modest-size, high-resolution and high-fidelity manually collected ground-truth dataset to calibrate Isolation Forest parameters and evaluate our approach, highlighting the relatively low cost of the methodology. Our data-centric methodology demonstrates the efficacy of deep learning methods coupled with IF to create mid-resolution land-use models and map products for agriculture using an alternative ground-truth dataset. Moreover, we compare our deep learning approach with a traditional algorithm used in remote sensing and evaluate the spatial transferability of the created models. Finally, we reflect upon the lessons learnt and future work. Collapse

Topics: cloud computing Python programming language open source geographic information system

Methods: experiment deep learning computational algorithm isolation forest random forest classification

Mechanical ventilation settings advisory system Odyn

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Grąbczewski, Krzysztof; Adamczak, Rafał; Grochowski, Marek; Joachimiak, Michal; Sokolov, Oleksandr; Kołodziejczak, Michalina; Kowalski, Piotr; Sierakowska, Katarzyna

Abstract: COVID-19 pandemic impaired function of healthcare systems globally by increased ... Expand

Abstract: COVID-19 pandemic impaired function of healthcare systems globally by increased workload at intensive care units in hospitals that underwent a massive influx of patients in critical condition in need of tailored and specialized care. Prevalent respiratory failure increased the demand for ventilators and respiratory therapists. Due to personnel shortages, doctors and nurses, not trained in the field of intensive care, were expected to provide adequate care for patients, supporting themselves with help of consulting specialists and navigating equipment shortages. To deal with this problem we present the research result, conducted in cooperation between computer scientists and anesthesiologists, namely the advisory system that supports physicians who do not have expertise in operating ventilators for typical COVID-19 patients. The system has been implemented at two hospitals. We present the system architecture and some internals of its recommendation modules based on decision graphs and recurrent neural networks. Further research will embrace analysis and optimization of the modules to provide yet more accurate support, as early as possible and in a broader spectrum of patient's conditions. Collapse

Topics: healthcare data application programming interface artificial intelligence systems development enterprise architecture

Methods: time series analysis longitudinal research artificial neural network computational algorithm data imputation

What Can Online Doctor Reviews Tell Us? A Deep Learning Assisted Study of Telehealth Service

2023 | HICSS | Citations: 0

Authors: Hao, H.; Zhang, B.; Zhan, Y.; Wu, J.

Abstract: The present study develops a novel deep learning method which assists text mini ... Expand

Abstract: The present study develops a novel deep learning method which assists text mining of online doctor reviews to extract underlying sentiment scores. Those scores can be used to estimate a healthcare service quality model to investigate how the online doctor reviews impact the online doctor consultation demand. Based on the data from the largest online health platforms in China, our model results show that the underlying sentiment scores have statistically significant impacts on the demand of online doctor consultation. Theoretically, the present study constructs an innovative deep learning algorithm with a better performance than four widely used text mining methods, which can be applied to text mining of many online forums or social media texts. Empirically, our model results show what factors impact the health service quality and online doctor consultation demand, and following those factors, healthcare professionals can improve their service. Collapse

Topics: service quality online review social media Python user-generated content

Methods: sentiment analysis natural language processing deep learning experimental group doc2vec

Design for Social Sharing: The Case of Mobile Apps

2023 | Information Systems Research | Citations: 1

Authors: Karanam, Subrahmanyam Aditya; Agarwal, Ashish; Barua, Anitesh

Abstract: With a large number of mobile apps on platforms such as iOS and Android, app dev ... Expand

Abstract: With a large number of mobile apps on platforms such as iOS and Android, app developers face a significant challenge in generating market demand. Apps can incorporate social features to share information and create awareness. We focus on the impact of these features and their interplay with intrinsic features in the head, body, and tail of the demand distribution. Using a panel of version release notes from the iOS platform, we develop a novel hierarchical deep learning model to extract intrinsic and social features. Our results suggest that social features help increase the demand for tail apps and are also useful for head apps in informing users about new intrinsic features. To explore possible mechanisms, we analyze how different types of social features (personal and platform), intrinsic features (common versus differentiating), and app quality influence demand. We find that social features that allow sharing on platforms with large audiences are more effective at increasing demand than those on personal messaging systems. Furthermore, within platforms, social features perform better on those with stronger ties, such as Facebook as opposed to Twitter. Our analysis of different types of intrinsic features reveals that social features can help increase the demand for all apps when introduced with differentiating or less common intrinsic features. Furthermore, we find that there is a negative effect on the demand of tail apps when differentiating intrinsic features are combined with platform-based social features that have weaker ties. However, this negative effect is limited to low-quality tail apps only. Our results underscore the differences in the effect of different types of social and intrinsic features in various parts of the demand distribution. Our study provides managerial guidance to app developers in enabling social sharing through design choices and generating higher demand.History: Kai Lung Hui, Senior Editor; Beibei Li, Associate Editor.Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2022.1151. Collapse

Topics: mobile application Facebook Twitter iOS accounting

Methods: deep learning robustness check computational algorithm word embedding natural language processing

AI Labor Markets: Toward a Dynamic Skills-Based Approach to Measurement

2023 | International Conference on Information Systems | Citations: 0

Authors: Kim, Jeongmin; Rai, Arun; Lin, Yu-Kai

Abstract: Artificial intelligence (AI) is transforming the nature of work and reshaping l ... Expand

Abstract: Artificial intelligence (AI) is transforming the nature of work and reshaping labor markets. Viewing labor as a bundle of skills, recent research has analyzed AI skills and offered important insights about the impacts of AI on labor markets. We add to this ongoing discourse and argue that taking a dynamic skill-based approach to measurement is critical: just like the development of AI is emergent and ever-evolving, so are AI skills. Taking stock of the literature, we show that existing studies tend to take a static approach to measuring AI skills, which fails to fully reflect the dynamic phenomenon of AI skills and could cause measurement errors. We propose a dynamic co-occurrence method and demonstrate that it performs better than the extant static methods, which can cause severe Type I and II errors, omit emerging AI skills, and temporally over-and underestimate the demands for AI skills and jobs. Collapse

Topics: artificial intelligence online labor market artificial general intelligence expert system

Methods: literature study machine learning robustness check artificial neural network natural language processing

Improved healthcare disaster decision-making utilizing information extraction from complementary social media data during the COVID-19 pandemic

2023 | Decision Support Systems | Citations: 0

Authors: Kellner, Domenic; Lowin, Maximilian; Hinz, Oliver

Abstract: Managing an extreme event like a healthcare disaster requires accurate informati ... Expand

Abstract: Managing an extreme event like a healthcare disaster requires accurate information about the event's circum­ stances to comprehend the full consequences of acting. However, information quality is rarely optimal since it takes time to determine the information of relevance. The COVID-19 pandemic showed that even official data sources are far from optimal since they suffer from reporting delays that slow decision-making. To support decision-makers with timely information, we utilize data from online social networks to propose an adaptable information extraction solution to create indices helping to forecast COVID-19 case numbers and hospitalization rates. We show that combining heterogeneous data sources like Twitter and Reddit can leverage these sources' inherent complementarity and yield better predictions than those using a single data source alone. We further show that the predictions run ahead of the official COVID-19 incidences by up to 14 days. Additionally, we highlight the importance of model adjustments whenever new information becomes available or the underlying data changes by observing distinct changes in the presence of specific symptoms on Reddit. Collapse

Topics: Twitter Reddit social media pandemic website

Methods: BERT information extraction computational algorithm autoregressive integrated moving average time series analysis

Multi-Channel Convolutional Neural Network for the Identification of Eyewitness Tweets of Disaster

2023 | Information Systems Frontiers | Citations: 0

Authors: Kumar, Abhinav; Singh, Jyoti Prakash; Rana, Nripendra P.; Dwivedi, Yogesh K.

Abstract: During a disaster, a large number of disaster-related social media posts are wid ... Expand

Abstract: During a disaster, a large number of disaster-related social media posts are widely disseminated. Only a small percentage of disaster-related information is posted by eyewitnesses. The post of a disaster eyewitness offers an accurate depiction of the disaster. Therefore, the information posted by the eyewitness is preferred over the other source of information as it is more effective at helping organize rescue and relief operations and potentially saving lives. In this work, we propose a multi-channel convolutional neural network (MCNN) that uses three different word-embedding vectors together to classify disaster-related tweets into eyewitness, non-eyewitness, and don’t know classes. We compared the performance of the proposed multi-channel convolutional neural network with several attention-based deep-learning models and conventional machine learning-models such as recurrent neural network, gated recurrent unit, Long-Short-Term-Memory, convolutional neural network, logistic regression, support vector machine, and gradient boosting. The proposed multi-channel convolutional neural network achieved an F1-score of 0.84, 0.88, 0.84, and 0.86 with four disaster-related datasets of floods, earthquakes, hurricanes, and wildfires, respectively. The experimental results show that the training MCNN model with different word embedding together performs better than the conventional machine-learning models and several other deep-learning models. Collapse

Topics: Twitter earthquake hurricane crowdsourcing social media

Methods: machine learning deep learning convolutional neural network experiment GloVe

Use of Natural Language Processing Techniques in the Construct and Instrument Development Process

2023 | European Conference On Information Systems | Citations: 0

Authors: Larsen, Kai; Sharma, Rajeev; Queiroz, Magno; Arnulf, Jan; Pillet, Jean-Charles

Abstract: The construct and instrument development process relies significantly on human ... Expand

Abstract: The construct and instrument development process relies significantly on human judgment in the initial stages of the process, specifically in developing construct definition statements, and in developing measurement instruments with high content validity. Natural language processing (NLP) techniques can be used to support human judgment and improve the quality of constructs and instruments employed in research. This paper describes the use of NLP techniques in the construct and instrument development process and presents illustrative results from the use of those techniques. We develop an NLP-based algorithm and illustrate its use to assess measurement instruments. The data used to train the algorithm was 37 years of constructs published in premier IS journals during the period of 1980-2016. The empirical illustrations support our premise that the use of NLP techniques can improve the rigor of the process and improve the quality of constructs and instruments employed in research. Collapse

Topics: website ease of use application programming interface perceived usefulness perceived ease of use

Methods: natural language processing survey design computational algorithm machine learning survey

Champions for Social Good: How Can We Discover Social Sentiment and Attitude-Driven Patterns in Prosocial Communication?

2023 | Journal of the Association for Information Systems | Citations: 0

Authors: Mukkamala, Raghava; Kauffman, Robert; Henriksen, Helle

Abstract: The UN High Commissioner on Refugees (UNHCR) is pursuing a social media strateg ... Expand

Abstract: The UN High Commissioner on Refugees (UNHCR) is pursuing a social media strategy to inform people about displaced populations and refugee emergencies. It is actively engaging public figures to increase awareness through its prosocial communications and improve social informedness and support for policy changes in its services. We studied the Twitter communications of UNHCR social media champions and investigated their role as high-profile influencers. In this study, we offer a design science research and data analytics framework and propositions based on the social informedness theory we propose in this paper to assess communication about UNHCR's mission. Two variables-refugee-emergency and champion type-relate to the informedness of UNHCR champions' followers. Based on a Twitter sentiment and attitude corpus, we applied a five-step design science analytics framework involving machine learning and natural language processing to test how the emergency type and champion type impact social communication patterns. Positive and neutral sentiment dominated the tweets of the champions and their followers for most refugee-emergency types. High participation-intensity champions emphasized high-intensity emergencies with tweet patterns reflecting dominant positive or neutral sentiment and sharing/liking attitudes. However, we found that sports figures effects were limited in spreading UNHCR's message, despite their millions of followers. We demonstrate the power of data science for prosocial policy based on refugee crisis awareness and instantiate our methods and knowledge contributions in a research framework that derives knowledge, decisions, and actions from behavioral, design, and economics of information systems perspectives. Collapse

Topics: Twitter social media analytical information system big data application programming interface

Methods: design science BERT natural language processing unsupervised learning artificial neural network

Theories: Lewin's field theory