Data Science for Social Good

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

Authors: Abbasi, Ahmed; Chiang, Roger; Xu, Jennifer

Abstract: Data science has been described as the fourth paradigm of scientific discovery. ... Expand

Abstract: Data science has been described as the fourth paradigm of scientific discovery. The latest wave of data science research, pertaining to machine learning and artificial intelligence (AI), is growing exponentially and garnering millions of annual citations. However, this growth has been accompanied by a diminishing emphasis on social good challenges-our analysis reveals that the proportion of data science research focusing on social good is less than it has ever been. At the same time, the proliferation of machine learning and generative AI has sparked debates about the sociotechnical prospects and challenges associated with data science for human flourishing, organizations, and society. Against this backdrop, we present a framework for "data science for social good" (DSSG) research that considers the interplay between relevant data science research genres, social good challenges, and different levels of sociotechnical abstraction. We perform an analysis of the literature to empirically demonstrate the paucity of work on DSSG in information systems (and other related disciplines) and highlight current impediments. We then use our proposed framework to introduce the articles appearing in the JAIS special issue on data science for social good. We hope that this editorial and the special issue will spur future DSSG research and help reverse the alarming trend across data science research over the past 30-plus years in which social good challenges are attracting proportionately less attention with each passing day. Collapse

Topics: societal evaluation criteria digital dashboard productivity social media pandemic

Methods: word embedding literature study literature sample longitudinal research design science

An Investigation of Domain-based Social Influence on ChatGPT-Related Twitter Data

2023 | International Conference on Information Systems | Citations: 0

Authors: Ahangama, Supunmali

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Topics: Twitter social influence social media social identity robotic

Methods: chatGPT sentiment analysis multiple linear regression term frequency–inverse document frequency NLTK

sPAN-pROP: Combatting Contextualized Social Media State-Linked Propaganda in the Middle East

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Aleroud, Ahmed; Melhem, Abdullah Bani; Alhussien, Nour; Albert, Craig

Abstract: Social Cyber-attacks such as propaganda, conspiracy theories, anger, and hate d ... Expand

Abstract: Social Cyber-attacks such as propaganda, conspiracy theories, anger, and hate discourse are very old phenomena that inflict harm to humans, organizations, national security, public officials, democratization efforts, careers, and policies. There have been significant efforts to identify anti-US speech on social media which includes propaganda. Such efforts largely ignore the attempts by other countries to manipulate social media in some regions including the Middle East. Research in this area is computational and solely focuses on fine tuning language models to detect general propaganda attacks. This paper addresses a new category of propaganda attacks that are tied to state-linked accounts that spread anti-US propaganda by taking advantages of specific geo-political crises in the Middle East. We investigated the role of general language models and training data to detect those forms of propaganda. Our study concludes that existing propaganda training data is unable to successfully detect targeted propaganda. We propose a contextualized span detection approach to identify these types of propaganda and show that our targeted training models work significantly better compared to the existing general propaganda detection approaches. Collapse

Topics: Twitter IT security threat social media armed conflict misinformation

Methods: language model experiment deep learning PyTorch literature study

At What Price? Exploring the Potential and Challenges of Differentially Private Machine Learning for Healthcare

2023 | HICSS | Citations: 1

Authors: Aslan, Aycan; Matschak, Tizian; Greve, Maike; Trang, Simon; Kolbe, Lutz

Abstract: The increased generation of data has become one of the main drivers of technolog ... Expand

Abstract: The increased generation of data has become one of the main drivers of technological innovation in healthcare. This applies in particular to the adoption of Machine Learning models that are used to generate value from the growing available healthcare data. However, the increased processing of sensitive healthcare data comes with challenges in terms of data privacy. Differential privacy, the method of adding randomness to the data to increase privacy, has gained popularity in the last few years as a possible solution. However, while the addition of randomness increases privacy, it also reduces overall model performance, generating a privacy-utility trade-off. Examining this trade-off, we contribute to the literature by providing an empirical paper that experimentally evaluates two prominent and innovative methods of differentially private Machine Learning on medical image and text data to deepen the understanding of the existing potential and challenges of such methods for the healthcare domain. Collapse

Topics: privacy differential privacy healthcare data knowledge base data protection

Methods: experiment computational algorithm machine learning blood pressure PyTorch

Rumor Classification through a Multimodal Fusion Framework and Ensemble Learning

2023 | Information Systems Frontiers | Citations: 0

Authors: Azri, Abderrazek; Favre, Cécile; Harbi, Nouria; Darmont, Jérôme; Noûs, Camille

Abstract: The proliferation of rumors on social media has become a major concern due to it ... Expand

Abstract: The proliferation of rumors on social media has become a major concern due to its ability to create a devastating impact. Manually assessing the veracity of social media messages is a very time-consuming task that can be much helped by machine learning. Most message veracity verification methods only exploit textual contents and metadata. Very few take both textual and visual contents, and more particularly images, into account. Moreover, prior works have used many classical machine learning models to detect rumors. However, although recent studies have proven the effectiveness of ensemble machine learning approaches, such models have seldom been applied. Thus, in this paper, we propose a set of advanced image features that are inspired from the field of image quality assessment, and introduce the Multimodal fusiON framework to assess message veracIty in social neTwORks (MONITOR), which exploits all message features by exploring various machine learning models. Moreover, we demonstrate the effectiveness of ensemble learning algorithms for rumor detection by using five metalearning models. Eventually, we conduct extensive experiments on two real-world datasets. Results show that MONITOR outperforms state-of-the-art machine learning baselines and that ensemble models significantly increase MONITOR’s performance. Collapse

Topics: Twitter social network Python microblog logistics management

Methods: computational algorithm machine learning experiment meta learning ensemble learning

The Sound of Progress: Investigating the Representation of Artificial Intelligence in Music

2023 | International Conference on Business Informatics | Citations: 0

Authors: Banh, Leonardo; Strobel, Gero

Abstract: The role of artificial intelligence in daily life is constantly advancing and h ... Expand

Abstract: The role of artificial intelligence in daily life is constantly advancing and has become an important topic of discussion. With music and its lyrics being a vehicle to express topics of society, this paper investigates how artificial intelligence is perceived by musicians and reflected in their songs. By analyzing the lyrics of over 1,200 songs over three decades, this work applies sentiment analysis to extract polarities and emotions. The results provide insights into how musicians view and reflect the impact of artificial intelligence on society and how this is reflected in their song texts. The findings show an increase in songs mentioning artificial intelligence-related terms, with a trend of more songs implying negativity, such as anger. However, minor increases in positive emotions indicate musicians' ambivalent views on hopes and fear of artificial intelligence. Collapse

Topics: artificial intelligence deepfake database system

Methods: sentiment analysis deep learning machine learning language model simple linear regression

Theories: communication theory

Give me 3W1H: A Bibliometric View on Accountable AI

2023 | International Conference on Information Systems | Citations: 0

Authors: Bartsch, Sebastian; Schmidt, Jan-Hendrik

Abstract: Accountability is crucial to make stakeholders of Artificial Intelligence (AI)- ... Expand

Abstract: Accountability is crucial to make stakeholders of Artificial Intelligence (AI)-based systems justify their actions, thereby explaining the harm such systems cause to AI users. Due to the importance of accountability in the context of AI, accountability was introduced into IS research through literature reviews. Therefore, while IS research's understanding of accountability covers the necessary depth, it comes at the expense of its essential breadth. Using a bibliometric analysis with 19,978 English-language papers, we shed light on the essential breadth posing three W-and one H-questions (When, What, Whereof, and How). Therefore, we contribute to IS research by highlighting the urgent need to revise existing definitions of accountability in the context of AI and establish them in IS research. We argue that a missing revision leads to non-transferrable findings within IS research. Accordingly, this study serves as a starting point for adapting definitions and creating a shared understanding in IS research. Collapse

Topics: artificial intelligence explainable artificial intelligence legal context database system IT security

Methods: word embedding word2vec bibliometric analysis machine learning supervised learning

Augmenting Social Bot Detection with Crowd-Generated Labels

2023 | Information Systems Research | Citations: 0

Authors: Benjamin, Victor; Raghu, T. S.

Abstract: Social media platforms are facing increasing numbers of cyber-adversaries seekin ... Expand

Abstract: Social media platforms are facing increasing numbers of cyber-adversaries seeking to manipulate online discourse by using social bots (i.e., social media software robots) to help automate and scale their attacks. Likewise, some social media users can identify social bot activity at varying degrees of confidence. In this research, human reactions to social bot messages are used to augment existing social bot detection capabilities. Speech act theory is used to inspire a framework for assessing the credibility of instances where users identify potential bot activity, as not all user responses are of equal credibility for assisting with the bot detection task. The framework is then operationalized through deep learning methodologies to develop a computational system for identifying social bots. Real-world performance and practicality of the developed framework is demonstrated on a live, crowd-sourced data set collected from a real-world social media platform. Results show that consideration of crowd reactions to suspected bots can significantly improve bot detection performance. Furthermore, consideration of speech acts to evaluate crowd reactions can even further augment the system’s performance, although speech acts themselves are not necessary to observe performance boost through crowd intelligence. This study serves as a grounding point for future work that can explore an augmented model for detecting other forms of algorithmically generated content within social media platforms.History: Olivia Sheng, Senior Editor; Jesse Bockstedt, Associate Editor.Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2022.1136. Collapse

Topics: Reddit social media crowdsourcing IT productivity social media analytics

Methods: experiment BERT machine learning support vector machine random forest classification

Theories: speech act theory

Business Inferences and Risk Modeling with Machine Learning; The case of Aviation Incidents

2023 | HICSS | Citations: 0

Authors: Cankaya, B.; Topuz, K.; Glassman, A.

Abstract: Machine learning becomes truly valuable only when decision-makers begin to depe ... Expand

Abstract: Machine learning becomes truly valuable only when decision-makers begin to depend on it to optimize decisions. Instilling trust in machine learning is critical for businesses in their efforts to interpret and get insights into data, and to make their analytical choices accessible and subject to accountability. In the field of aviation, the innovative application of machine learning and analytics can facilitate an understanding of the risk of accidents and other incidents. These occur infrequently, generally in an irregular, unpredictable manner, and cause significant disruptions, and hence, they are classified as "highimpact, low-probability" (HILP) events. Aviation incident reports are inspected by experts, but it is also important to have a comprehensive overview of incidents and their holistic effects. This study provides an interpretable machine-learning framework for predicting aircraft damage. In addition, it describes patterns of flight specifications detected through the use of a simulation tool and illuminates the underlying reasons for specific aviation accidents. As a result, we can predict the aircraft damage with 85% accuracy and 84% in-class accuracy. Most important, we simulate a combination of possible flight-type, aircraft-type, and pilot-expertise combinations to arrive at insights, and we recommend actions that can be taken by aviation stakeholders, such as airport managers, airlines, flight training companies, and aviation policy makers. In short, we combine predictive results with simulations to interpret findings and prescribe actions.A machine-learning (ML) algorithm was chosen for its fast performance, short run-time, and interpretability. The ML algorithms were compared and evaluated on a confusion matrix for their mean receiver operating characteristic curve (ROC), Collapse

Topics: decision making decision support system graphical user interface usability missing data

Methods: simulation machine learning multinomial logistic regression computational algorithm support vector machine

A Multi-Label Classification with an Adversarial-Based Denoising Autoencoder for Medical Image Annotation

2023 | ACM Transactions on Management Information Systems | Citations: 0

Authors: Chai, Yidong; Liu, Hongyan; Xu, Jie; Samtani, Sagar; Jiang, Yuanchun; Liu, Haoxin

Abstract: Medical image annotation aims to automatically describe the content of medical i ... Expand

Abstract: Medical image annotation aims to automatically describe the content of medical images. It helps doctors to understand the content of medical images and make better informed decisions like diagnoses. Existing methods mainly follow the approach for natural images and fail to emphasize the object abnormalities, which is the essence of medical images annotation. In light of this, we propose to transform the medical image annotation to a multi-label classification problem, where object abnormalities are focused directly. However, extant multi-label classification studies rely on arduous feature engineering, or do not solve label correlation issues well in medical images. To solve these problems, we propose a novel deep learning model where a frequent pattern mining component and an adversarial-based denoising autoencoder component are introduced. Extensive experiments are conducted on a real retinal image dataset to evaluate the performance of the proposed model. Results indicate that the proposed model significantly outperforms image captioning baselines and multi-label classification baselines. Collapse

Topics: workstation

Methods: data transformation autoencoder experiment deep learning literature study