Fine-grained classification of drug trafficking based on Instagram hashtags

2023 | Decision Support Systems | Citations: 0

Authors: Hu, Chuanbo; Liu, Bin; Ye, Yanfang; Li, Xin

Abstract: Social networks have become important platforms for the marketing and sale of il ... Expand

Abstract: Social networks have become important platforms for the marketing and sale of illicit drugs. Hashtags make it easier for users to engage in drug trafficking, further increasing the risk of drug abuse. However, there are significant challenges in the detection and management of drug trafficking activities. In addition, the rapid legalization of some drugs has required a fine-grained classification of drugs to distinguish them from those that are illegal. Motivated by these observations, in this paper, our aim is to develop a methodology using the latest advances in AI technology to classify hashtags from posts advertising illicit drugs for sale on social networks. We present a semi-supervised deep learning approach to classify hashtags from posts advertising illicit drugs. An elegant combination of Bidirectional Encoder Representations from Transformers (BERT) with Graph Convolu­ tional Network (GCN) allows us to analyze the characteristics (e.g., shipping region and platform self-regulation) of illegal drug trafficking. Our BERT+GCN model achieved the best performance with more than 75% accuracy compared to the other three baseline models. Then, fine-grained hashtags identified are applied to explore the characteristics of drug trafficking. Finally, we report our results for further exploration of shipping regions and self-regulation of drug trafficking on the platform in two analysis scenarios. Our developed approach has shown its effectiveness in detecting hashtags for different types of drugs from illegal drug sellers. Based on hashtag classification, we also provide two case studies that indicate that (1) there are differences in self-regulation for different types of drugs on social media, (2) there are regional differences in the demand for different types of drugs. Collapse

Topics: Instagram social media Google+ artificial intelligence advertising management

Methods: BERT case study machine learning deep learning semi-supervised learning

Spatial Analytics with Hospitality Big Data: Examining the Impact of Locational Determinants on Customer Satisfaction in the U.S. Hotel Market

2023 | HICSS | Citations: 0

Authors: Lee, Minwoo; Kim, Jinwon; Shin, Hyejo

Abstract: Although hotel location has been recognized as one of the important factors affe ... Expand

Abstract: Although hotel location has been recognized as one of the important factors affecting hotel selection and guest satisfaction, relatively few studies have examined guest satisfaction with hotel location and its locational determinants at a macro level. This study aims to identify the locational determinants of hotel guest satisfaction through big data spatial analytics via a case study of 5,302 hotels in 151 cities in the U.S. Based on the framework of hotel location satisfaction, we classified all location-related factors into three categories: accessibility to points of interest, transport convenience, and surrounding environment. Our findings indicated that hotel property’s proximity to city area, landmark, park, shopping center, and highway as well as, attraction-driven tourism industry specialization, and hotel industry agglomeration were significant determinants. Furthermore, the impacts of these factors were spatially heterogeneous. These findings can provide geographical insights that are critical for developing a customer service experience and satisfaction model. Collapse

Topics: big data geographic information system customer satisfaction online review criminality

Methods: regression analysis method spatial analysis spatial regression case study network analysis

The Hierarchy of Green Information Systems Capability in Organizations to Enhance and Ensure Green Performance: An Operant Resources Perspective

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

Authors: Ning, Xue; Khuntia, Jiban

Abstract: Green information systems (green IS) have evolved and expanded over the years, ... Expand

Abstract: Green information systems (green IS) have evolved and expanded over the years, now having a broad scope in organizations. Green IS in these organizations function to help to address climate change challenges. The growing complexity of green IS in organizations needs a conceptual framework that can articulate its different impacts at various levels while taking into consideration a holistic and integrated perspective. This research focuses on how to enhance and ensure green IS-driven green performance in organizations from an operant resources hierarchy perspective. A conceptual model is proposed to examine the role of management support for green initiatives and the role of information assurance in addressing IS misuse and process automation in relationship to green IS capability and green performance. The current study tests the effects of management support as composite operant resources and information assurance as interconnected operant resources for green IS capability in enhancing and ensuring organizations' green performance using a matched dataset of 73 organizations. We illustrate the application of the proposed conceptual model in two real-world scenarios. This study contributes theoretically by illustrating the application of the operant resources hierarchy perspective in green IS research and provides practical action suggestions for organizations adopting sustainable practices to achieve Sustainable Development Goal (SDG) 13. Collapse

Topics: information technology capability green IS IT security threat environmental sustainability business process automation

Methods: statistical hypothesis test descriptive statistic sensitivity analysis conceptual modelling spatial analysis

Introduction to the Minitrack on GIS, Industry 4.0, and Sustainability

2023 | HICSS | Citations: 0

Authors: Shin, Namchul; Farkas, Daniel

Abstract: ... Expand

Abstract: Collapse

Methods: spatial analysis vehicle routing problem geographic analytics

How Does AI Fail Us? A Typological Theorization of AI Failures

2023 | International Conference on Information Systems | Citations: 0

Authors: Zhan, Xinhui; Sun, Heshan; Miranda, Shaila M.

Abstract: AI incidents, often resulting from the complex interplay of algorithms, human a ... Expand

Abstract: AI incidents, often resulting from the complex interplay of algorithms, human agents, and situations, violate norms and can cause minor or catastrophic errors. This study systematically examines these incidents by developing a typology of AI failure and linking these modes to AI task types. Using a computationally intensive grounded theory approach, we analyzed 466 unique reported real-world AI incidents from 2013 to 2023. Our findings reveal an AI failure typology with six modes, including artifact malfunction, artifact misuse, algorithmic bias, agency oversight, situational unresponsiveness, and value misalignment. Furthermore, we explore the relationship between these failure modes and the tasks performed by AI, uncovering four propositions that provide a framework for future research. Our study contributes to the literature by offering a more holistic perspective on the challenges faced by AI-powered systems, beyond the critical challenges of fairness, transparency, and responsibility noted by the literature. Collapse

Topics: artificial intelligence algorithmic bias database system Python missing data

Methods: qualitative coding computational algorithm grounded theory machine learning spatial analysis

A Climate Change Vulnerability Assessment Design Framework: The Case of Small-scale Farmers in Western Honduras

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

Authors: Li, Yan; Caceres, Claudia; Bazarah, Ali Mohammed

Abstract: ... Expand

Abstract: Collapse

Topics: climate change mobile application digital dashboard web application decision support

Methods: case study qualitative interview design framework spatial analysis personal interview

Geospatial Clustering Analysis on Drug Abuse Emergencies

2022 | HICSS | Citations: 0

Authors: Lee, Jinha; Choi, Jung Im; Yeh, Arthur; Lan, Qizhen; Kang, Hyojung

Abstract: The epidemic of drug abuse is a serious public health issue in the U.S. The numb ... Expand

Abstract: The epidemic of drug abuse is a serious public health issue in the U.S. The number of overdose deaths involving prescription opioids and illicit drugs has continuously increased over the last few years. The objective of this study is to develop a geospatial model that identifies geospatial clusters in terms of socioeconomic, and demographic characteristics with an unsupervised machine learning algorithm. Then, we suggest the most important features affecting heroin overdose both negatively and positively. The findings of this study may inform policymakers about strategies to mitigate the drug overdose crisis. Collapse

Topics: geographic information system criminality open source Python database system

Methods: cluster analysis computational algorithm machine learning k-means clustering random forest classification

An anticrime information support system design: Application of K-means-VMD-BiGRU in the city of Chicago

2022 | Information & Management | Citations: 0

Authors: Zhu, Qing; Zhang, Fan; Liu, Shan; Li, Yuze

Abstract: The sharp rise in urban crime rates is becoming one of the most important issues ... Expand

Abstract: The sharp rise in urban crime rates is becoming one of the most important issues of public security, affecting many aspects of social sustainability, such as employment, livelihood, health care, and education. Therefore, it is critical to develop a predictive model capable of identifying areas with high crime intensity and detecting trends of crime occurrence in such areas for the allocation of scarce resources and investment in the prevention and reduction of criminal strategies. This study develops a predictive model based on K-means clustering, signal decomposition technique, and neural networks to identify crime distribution in urban areas and accurately forecast the variation tendency of the number of crimes in each area. We find that the time series of the number of crimes in different areas show a correlation in the long term, but this long-term effect cannot be reflected in the short period. Therefore, we argue that short-term joint law enforcement has no theoretical basis because data show that spatial heterogeneity and time lag cannot be timely reflected in short-term prediction. By combining the temporal and spatial effects, a high-precision anticrime information support system is designed, which can help the police to implement more targeted crime prevention strategies at the micro level. Collapse

Topics: criminality Google+ strategic management big data

Methods: longitudinal research time series analysis cluster analysis k-means clustering augmented Dickey–Fuller test

Theories: activity theory

QUANTIFYING THE IMPACT OF LOCATION DATA FOR REAL ESTATE APPRAISAL – A GIS-BASED DEEP LEARNING APPROACH

2021 | European Conference On Information Systems | Citations: 0

Authors: Kucklick, Jan-Peter; Müller, Jennifer; Beverungen, Daniel; Mueller, Oliver

Abstract: Appraising real estate is challenging since a property's characteristics, locat ... Expand

Abstract: Appraising real estate is challenging since a property's characteristics, location, and neighborhood make up for substantial shares of its price. Current Computer-Assisted Mass Appraisal (CAMA) approaches rely on hedonic models and structured real estate data. Recent work proposed to include geospatial or image data to improve CAMA's predictive accuracy. However, the value of these data sources and their preparation costs remain unclear. In this study, we use Geographic Information Systems (GIS) and Convolutional Neural Networks (CNNs) to appraise 71,000 properties in Philadelphia. We find that geospatial data requires substantial preprocessing but increases CAMA's predictive accuracy by 12%, while image data come with higher computational complexity and an improvement of 16%. Using both datatypes is complex to optimize but offers increased robustness towards extreme outliers. Beyond real estate appraisal, our study is one of the first to quantify the value of geospatial data for data-driven services in the information systems discipline. Collapse

Topics: geographic information system IS discipline decision support Bing price management

Methods: machine learning artificial neural network random forest classification computational algorithm linear regression analysis

Social media crowdsourcing for rapid damage assessment following a sudden-onset natural hazard event

2021 | International Journal of Information Management | Citations: 0

Authors: Li, Lingyao; Bensi, Michelle; Cui, Qingbin; Baecher, Gregory B.; Huang, You

Abstract: Rapid appraisal of damages related to hazard events is of importance to first re ... Expand

Abstract: Rapid appraisal of damages related to hazard events is of importance to first responders, government agencies, insurance industries, and other private and public organizations. While satellite monitoring, ground-based sensor systems, inspections and other technologies provide data to inform post-disaster response, crowdsourcing through social media is an additional and novel data source. In this study, the use of social media data, prin­ cipally Twitter postings, is investigated to make approximate but rapid early assessments of damages following a disaster. The goal is to explore the potential utility of using social media data for rapid damage assessment after sudden-onset hazard events and to identify insights related to potential challenges. This study defines a textbased damage assessment scale for earthquake damages, and then develops a text classification model for rapid damage assessment. Although the accuracy remains a challenge compared to ground-based instrumental readings and inspections, the proposed damage assessment model features rapidity with large amounts of data at spatial densities that exceed those of conventional sensor networks. The 2019 Ridgecrest, California earthquake sequence is investigated as a case study. Collapse

Topics: Twitter earthquake social media natural hazard Wikipedia

Methods: term frequency–inverse document frequency word embedding descriptive statistic statistical hypothesis test deep learning