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Techniques and Tools for Information Extraction: Application to Social Media
(Computational Semantics IntechOpen, 2025-10) Lossan Bonde; Severin Dembele
This chapter presents a comprehensive and systematic review of the various tech niques and tools used for information extraction (IE), focusing on social media. The aim of the review is triple: (1) identify the techniques and tools used for IE applied to social media, with a recommendation on when to use these techniques and tools; (2) determine what applications along with the social networks use IE from social media; and (3) identify the challenges associated with IE from social media. We combined Kitchenham and the PRISMA methodologies to conduct the systematic review. Guided by a review protocol and quality assessment, 58 papers were selected and analysed. This review highlights the potential of social media information extraction, empha sizing the central role of Machine Learning alongside emerging techniques like trans formers. Applications span sentiment analysis, health, security, marketing, and misinformation detection. Twitter dominates research, though other platforms are studied. Key challenges include ethics, privacy, data scarcity, accuracy, and computa tional costs, necessitating refined methods and ethical focus.
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Improving Credit Card Fraud Detection with Ensemble Deep Learning-Based Models: A Hybrid Approach Using SMOTE-ENN
(Journal of Computing Theories and Applications (JCTA), 2025-02-12) Lossan Bonde; Abdoul Karim Bichanga
Advances in information and internet technologies have significantly transformed the business environment, including the financial sector. The COVID-19 pandemic has further accelerated this digital adoption, expanding the e-commerce industry and highlighting the necessity for secure online transactions. Credit Card Fraud Detection (CCFD) stands critical as the prevalence of fraudulent activities continues to rise with the increasing volume of online transactions. Traditional methods for detecting fraud, such as rule-based systems and basic machine learning models, tend to fail to keep pace with fraudsters' evolving tactics. This study proposes a novel ensemble deep learning-based approach that combines Convolutional Neural Networks (CNN), Gated Recurrent Units (GRU), and Multilayer Perceptron (MLP) with the Synthetic Minority Oversampling Technique and Edited Nearest Neighbors (SMOTE-ENN) to address class imbalance and improve detection accuracy. The method ology integrates CNN for feature extraction, GRU for sequential transaction analysis, and Multilayer Perceptron (MLP) as a meta-learner in a stacking framework. By leveraging SMOTE-ENN, the proposed approach enhances data balance and prevents overfitting. With synthetic data, the robustness and accuracy of the model have been improved, particularly in scenarios where fraudulent examples are scarce. The experiments conducted on real-world credit card transaction datasets have established that our approach outperforms existing methods, achieving higher metrics performance.
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Challenges of recommender systems in finance and banking: a systematic review
(IAES International Journal of Artificial Intelligence (IJ-AI), 2025) Lossan Bonde; Abdoul Karim Bichanga
Recommender systems are widely applied in various domains, including e-commerce, marketing, and education. Despite their popularity, recommender systems are not widely used in finance and banking. This paper aims to identify the challenges associated with using recommender systems in finance and banking and recommend directions for future research. Using a systematic literature review (SLR) method, 52 papers were selected and analyzed. A three-step process was used to make the selection. First, a keyword search was made to identify a seed list of sources. A snowball technique with specific inclusion and exclusion criteria was applied to expand the list. Finally, a quick study was made to produce the final list of sources to consider. Through the study of the 52 relevant papers, three main challenges: i) transparency, ethics, and data privacy; ii) handling complex content information and accounting for multiple user behaviors; and iii) explainability of AI models were identified. This study has established the barriers to adopting recommender systems in the finance and banking industry. Specific subjects of concern identified include cold-start problems, personalization, fraud detection, transparency, and data privacy. The study recommends further research leveraging advanced machine learning models and emerging technologies to fill the gap.
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Pre-Retirement Financial Behavior and Preparedness of Seventh-day Adventist Pastors in Africa: A Study of Graduate Ministerial Students
(Pan-African Journal of Education and Social Sciences (PAJES), 2025) Angela E. Nwaomah; Sampson M. Nwaomah
Retirement is an inevitable end for people engaged in full-time employment. But the quality of life after retirement is a grave concern for many because retirement is not well prepared for. Seventh-day Adventist Church pastors in Africa traditionally rely on church-sustaining benefits for retirement. However, the inadequacy of such provisions has left many pastors in penury at retirement. This study investigated the pre-retirement financial behaviorsand preparedness of Seventh-day Adventist pastors in Africa. It focused on Seventh-day Adventist (SDA) pastors studying at the Theological Seminary of the Adventist University of Africa (AUA), Kenya. The study adopted a descriptive, quantitative research design. A total of 105 pastors participated in the study. The data were analyzed using descriptive statistics, such as frequency and percentage. The findings revealed positive pre-retirement behaviour but low financial literacy and inadequate financial preparedness for retirement. Therefore, activities and policies that can increase financial education and pension scheme literacy should be implemented in the ministerial education. Furthermore, ongoing educational initiatives and collaborations with financial experts to mentor pastors are worthwhile and beneficial for enhancing pre-retirement behavior and financial preparedness among SDA pastors in Africa.
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Social Justice and Information Management: A Biblical Perspective
(Pan-African Journal of Theology, 2026) Sampson M. Nwaomah; Angela E. Nwaomah
come one of the most powerful resources in shaping society, influencing public opinion, policies, decisions, determining access to opportunities, providing the basis for openness and accountability, protecting individual rights, and enforcing legal obligations. Nonetheless, the global explosion of information and its importance for organizational and individual openness and accountability for social transformation and social justice also provides the possibility of its manipulation and suppression. This situation warrants an ethical response, especially from a Christian worldview. Consequently, a Christian biblical worldview on information management could offer a balanced ethical stance in relation to social justice. This research utilized a documentary content analysis approach by analyzing biblical and secondary data on social justice and its interaction with information management. The authors identified some significant resources for this research, established their authenticity, credibility, and representativeness. It then divided the material into units to discuss and establish meanings for the theme studied. It was argued that the biblical principles of truth, integrity, access, equity, equality, confidentiality, privacy and stewardship, and accountability in information management are significant to social justice. Therefore, the paper concluded that from a biblical perspective, social justice has connections with information management. The paper encourages responsible use of information as a moral and spiritual duty that reflects God’s concern for justice, truth, and the flourishing of all people.