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

DEVELOPMENT STRATEGY AND MANAGEMENT OF AI-BASED VULNERABILITY DETECTION APPLICATIONS IN ENTERPRISE SOFTWARE ENVIRONMENT

2023 | European Conference On Information Systems | Citations: 0

Authors: Behfar, Stefan

Abstract: Industries are now struggling with high level of security-risk vulnerabilities ... Expand

Abstract: Industries are now struggling with high level of security-risk vulnerabilities in their software environment which mainly originate from open-source dependencies. Industries' percentage of open source in codebases is about 54% whereas ones with high security risks is about 30% (Synopsys 2018).While there are existing solutions for application security analysis, these typically only detect a limited subset of possible errors based on pre-defined rules. With the availability of open-source vulnerability resources, it is now possible to use data-driven techniques to discover vulnerabilities. Although there are a few AI-based solutions available, but there are some associated challenges: 1) use of artificial intelligence for application security (AppSec) towards vulnerability detection has been very limited and definitely not industry oriented, 2) the strategy to develop, use and manage such AppSec products in enterprises have not been investigated; therefore cybersecurity firms do not use even limited existing solutions. In this study, we aim to address these challenges with some strategies to develop such AppSec, their use management and economic values in enterprise environment. Collapse

Topics: artificial intelligence new product development source code open source software metric

Methods: machine learning artificial neural network

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

Stand for Something or Fall for Everything: Predict Misinformation Spread with Stance-Aware Graph Neural Networks

2023 | International Conference on Information Systems | Citations: 0

Authors: Chen, Zihan; Sun, Jingyi; Liu, Rong Emily; Mai, Feng

Abstract: Although pervasive spread of misinformation on social media platforms has becom ... Expand

Abstract: Although pervasive spread of misinformation on social media platforms has become a pressing challenge, existing platform interventions have shown limited success in curbing its dissemination. In this study, we propose a stance-aware graph neural network (stance-aware GNN) that leverages users' stances to proactively predict misinformation spread. As different user stances can form unique echo chambers, we customize four information passing paths in stance-aware GNN, while the trainable attention weights provide explainability by highlighting each structure's importance. Evaluated on a realworld dataset, stance-aware GNN outperforms benchmarks by 32.65% and exceeds advanced GNNs without user stance by over 4.69%. Furthermore, the attention weights indicate that users' opposition stances have a higher impact on their neighbors' behaviors than supportive ones, which function as social correction to halt misinformation propagation. Overall, our study provides an effective predictive model for platforms to combat misinformation, and highlights the impact of user stances in the misinformation propagation. Collapse

Topics: misinformation Twitter filter bubble social media social influence

Methods: machine learning RoBERTa experiment deep learning graph neural network

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

A Bitter Pill to Swallow? The Consequences of Patient Evaluation in Online Health Question-and-Answer Platforms

2023 | Information Systems Research | Citations: 0

Authors: Chen, Chen; Walker, Dylan

Abstract: Online health question-and-answer (Q&A) platforms (OHQPs), where patients post h ... Expand

Abstract: Online health question-and-answer (Q&A) platforms (OHQPs), where patients post health-related questions, evaluate advice from multiple doctors, and direct a bounty (monetary reward) to their most preferred answer, have become a prominent channel for patients to receive medical advice in China. To explore the quality of medical advice on these platforms, we analyzed data on patients’ evaluation of ∼497,000 answers to ∼114,000 questions on one of the most popular OHQPs, 120ask.com, over a three-month period. We assembled a panel of independent physicians and instructed them to evaluate the quality of ∼13,000 answers. We found that the quality of medical advice offered on the platform was high on average, and that low-quality answers were rare (6%). However, our results also indicate that patients lacked the ability to discriminate advice quality. They were as likely to choose the best answer as the worst. The medical accuracy of patient evaluation was worse in critical categories (cancer, internal medicine) and for vulnerable subpopulations (pediatrics). Given that millions of patients seek medical advice from OHQPs in China annually, the social and economic implications of this finding are troubling. To understand how patients evaluate advice, we trained deep neural networks to think like patients, allowing us to identify patients’ positive and negative responses to different heurist cues. Although our results indicate that OHQPs perform well, we identified several concerns that should be addressed through platform design and policy changes. Because the Q&A process lacks peer review mechanisms, signals of advice quality are not conveyed to patients, forcing them to rely on heuristic cues, which cannot effectively guide them toward the best advice. We also found that the platform reputation metric was not correlated with the quality of the advice giver’s advice, may effectively encourage patients to select lesser quality medical advice, and increased the risk of moral hazard for malicious players to intentionally provide less accurate but more agreeable advice for personal gain. Our analysis revealed bad actors on the platform, including drug promoters and spammers. Finally, we found that OHQPs exacerbated care avoidance. We discuss several potential policy changes to address these shortcomings. History: Param Singh, Senior Editor; Idris Adjerid, Associate Editor. Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2022.1158. Collapse

Topics: decision making local interpretable model agnostic explanation digital platform health information system pandemic

Methods: artificial neural network psychometrics survey deep learning natural language processing

Theories: lemon market theory

ICU Outcome Predictions Using Real-Time Signals with Wavelet-Transform-based Convolutional Neural Network

2023 | HICSS | Citations: 0

Authors: Jiang, Yiqun; Wang, Shaodong; Li, Qing; Zhang, Wenli

Abstract: Intensive care units (ICUs) serve patients with life-threatening conditions. The ... Expand

Abstract: Intensive care units (ICUs) serve patients with life-threatening conditions. The limited ICU resources cause severe economic and healthcare burdens worldwide. It is critical to conduct ICU outcome predictions at an early stage and promote efficient use of ICU resources. However, all the current prediction methods have limitations such as unsatisfactory accuracy and depending on resource-demanding laboratory tests or expert domain knowledge. In this research, we design a wavelet-transformed-based convolutional neural network, WTCNN, which only requires patients’ vital sign series and information at ICU admission for real-time ICU outcome predictions. The model is evaluated using a large real-world ICU database and outperforms state-of-art baselines on both ICU mortality and length-of-stay prediction tasks. We conduct LIME for model interpretation and prescriptive analysis. Our work provides an efficient tool for ICU outcome predictions, allowing healthcare providers to take action promptly on patients at risk and reduce the negative impacts on patient outcomes. Collapse

Topics: local interpretable model agnostic explanation database system missing data societal evaluation criteria healthcare data

Methods: machine learning longitudinal research time series analysis deep learning electrocardiography

Building a deep learning-based QA system from a CQA dataset

2023 | Decision Support Systems | Citations: 0

Authors: Jin, Sol; Lian, Xu; Jung, Hanearl; Park, Jinsoo; Suh, Jihae

Abstract: A man-made machine-reading comprehension (MRC) dataset is necessary to train the ... Expand

Abstract: A man-made machine-reading comprehension (MRC) dataset is necessary to train the answer extraction part of existing Question Answering (QA) systems. However, a high-quality and well-structured dataset with questionparagraph-answer pairs is not usually found in the real world. Furthermore, updating or building an MRC dataset is a challenging and costly affair. To address these shortcomings, we propose a QA system that uses a large-scale English Community Question Answering (CQA) dataset (i.e., Stack Exchange) composed of 3,081,834 questionanswer pairs. The QA system adopts a classifier-retriever-summarizer structure design. The question classifier and the answer retriever part are based on a Bidirectional Encoder Representations from Transformers (BERT) Natural Language Processing (NLP) model by Google, and the summarizer part introduces a deep learning-based Text-to-Text Transfer Transformer (T5) model to summarize the long answers. We instantiated the proposed QA system with 140 topics from the CQA dataset (including topics such as biology, law, politics, etc.) and conducted human and automatic evaluations. Our system presented encouraging results, considering that it provides highquality answers to the questions in the test set and satisfied the requirements to develop a QA system without MRC datasets. Our results show the potential of building automatic and high-performance QA systems without being limited by man-made datasets, a significant step forward in the research of open-domain or specificdomain QA systems. Collapse

Topics: privacy decision making online community IT productivity Google+

Methods: BERT chatGPT computational algorithm experiment natural language processing

Cyber threat detection: Unsupervised hunting of anomalous commands (UHAC)

2023 | Decision Support Systems | Citations: 0

Authors: Kayhan, Varol O.; Agrawal, Manish; Shivendu, Shivendu

Abstract: The cyber security industry is rapidly adopting threat hunting as a proactive to ... Expand

Abstract: The cyber security industry is rapidly adopting threat hunting as a proactive tool for early and faster detection of suspected malicious actors. In this paper, we propose a machine learning-based method, Unsupervised Hunting of Anomalous Commands (UHAC), to detect text-based anomalous commands in security information and event management (SIEM) logs that are good candidates for threat hunting. A unique feature of the proposed method is that it first creates a feature set based on the augmentation of document-term and document-character matrices. Then, an autoencoder-based detector is trained on this feature set using a custom loss function. UHAC consis­ tently outperforms other feature sets and algorithms such as one-class support vector machine, density-based spatial clustering of applications with noise, and word-embedding based models such as word2vec. The UHAC detector identifies 84–89% of anomalies in the top 10% of the data. Findings have implications for cybersecurity analysts who perform threat hunting in SIEM logs for process auditing on endpoint devices. Collapse

Topics: IT security artificial intelligence Python decision support system email phishing

Methods: autoencoder data transformation DBSCAN anomaly detection machine learning

Unsupervised Threat Hunting using Continuous Bag of Terms and Time (CBoTT)

2023 | International Conference on Information Systems | Citations: 0

Authors: Kayhan, Varol; Shivendu, Shivendu; Behnia, Rouzbeh; Daniel, Clinton; Agrawal, Manish

Abstract: Threat hunting is sifting through system logs to detect malicious activities th ... Expand

Abstract: Threat hunting is sifting through system logs to detect malicious activities that might have bypassed existing security measures. It can be performed in several ways, one of which is based on detecting anomalies. We propose an unsupervised framework, called continuous bag-of-terms-and-time (CBoTT), and publish its application programming interface (API) to help researchers and cybersecurity analysts perform anomaly-based threat hunting among SIEM logs geared toward process auditing on endpoint devices. Analyses show that our framework consistently outperforms benchmark approaches. When logs are sorted by likelihood of being an anomaly (from most likely to least), our approach identifies anomalies at higher percentiles (between 1.82-6.46) while benchmark approaches identify the same anomalies at lower percentiles (between 3.25-80.92). This framework can be used by other researchers to conduct benchmark analyses and cybersecurity analysts to find anomalies in SIEM logs. Collapse

Topics: IT security application programming interface Hadoop Distributed File System website web analytics

Methods: natural language processing artificial neural network support vector machine term frequency–inverse document frequency DBSCAN