School of Science and Technology

An Examination of Threats and Countermeasures Relating to Healthcare Cyber Risks. The Case of Kenyatta National Hospital (KNH)

2026-05-11T12:47:14Z

An Examination of Threats and Countermeasures Relating to Healthcare Cyber Risks. The Case of Kenyatta National Hospital (KNH) Stephen, Ario Okongo The increasing reliance on digital technologies in Kenya’s healthcare sector has heightened the need for robust cybersecurity measures to protect sensitive patient data and ensure operational continuity. This study examined cybersecurity threats and countermeasures at Kenyatta National Hospital (KNH), the country’s largest public referral hospital, to develop a contextually relevant framework for enhancing data protection and institutional resilience. Specifically, the research investigated perceived cyber risks, the influence of the Kenya Cybercrime Act, and ethical data protection practices on the effectiveness of the hospital’s cybersecurity framework. Guided by the Socio-Technical Systems (STS) Theory, the study adopted a descriptive cross-sectional research design, utilizing a quantitative approach. A stratified random sample of 370 KNH staff, including ICT personnel, clinicians, and health records and admins, were surveyed using structured questionnaires, achieving a 98.6% response rate (365 valid responses). Data analysis involved descriptive statistics, correlation, and multiple regression techniques, ensuring robust insights into the relationships among key variables. Findings on cybersecurity threats revealed high perceived risks, particularly from external attacks (M = 4.18, SD = 1.09, Var = 1.19), device vulnerabilities (M = 3.99), and insider threats (M = 3.92). Correlation analysis showed that threats were strongly associated with the Cybercrime Act (r = 0.602, p < 0.01) and moderately with ethical guidelines (r = 0.485, p < 0.01), but insignificantly with the cybersecurity framework itself (r = 0.055, p = 0.297). Regression confirmed a significant negative coefficient for threats (B = -0.182, p = 0.007), indicating that heightened threats weaken the framework’s effectiveness. Analysis of the Kenya Cybercrime Act demonstrated moderate correlations with ethical guidelines (r = 0.539, p < 0.01) and a weaker positive correlation with the cybersecurity framework (r = 0.191, p < 0.01). In regression, the Act had a positive but marginally insignificant coefficient (B = 0.136, p = 0.054; Beta = 0.129), suggesting that while legal provisions support cybersecurity, their influence is not yet robust. For ethical guidelines, results showed a moderate correlation with the cybersecurity framework (r = 0.294, p < 0.01). Regression identified ethical guidelines as the most influential predictor (B = 0.303, p < 0.001; Beta = 0.309), confirming their pivotal role in strengthening KNH’s cybersecurity posture. The overall regression model was statistically significant (F(3,361) = 14.267, p < 0.001) with R = 0.326, R² = 0.106, and Adjusted R² = 0.099, indicating that the three predictors jointly explained about 10.6% of the variance in the hospital’s cybersecurity framework. The study concludes that ethical data protection guidelines are the strongest determinant of a resilient cybersecurity framework, while rising threats undermine readiness and the Cybercrime Act contributes moderately. It recommends strengthening ethical enforcement, embedding role-based staff training, enhancing legal compliance, and allocating resources to mitigate threats. Future research should simulate incident scenarios and assess patient data literacy across hospitals. Keywords: Cyber Threats, Cybersecurity, Data Security Framework, Ethical Data Protection, Healthcare, Kenyatta National Hospital, Kenya Cybercrime Act, Socio-Technical Systems Theory

Effect Of Zai Pits, Mulch and Manure on the Growth and Yield of Green Grams in Maragua Sub County

2026-05-11T12:28:17Z

Effect Of Zai Pits, Mulch and Manure on the Growth and Yield of Green Grams in Maragua Sub County Wilson, Gitau Kamau Green grams are crucial for human nutrition and environmental sustainability. Abundant in protein, fiber, and other nutrients, they enhance global food security, especially in areas with restricted protein consumption. Their cultivation boosts soil health and fertility, diminishes reliance on synthetic fertilizers via nitrogen fixation, and bolsters agricultural sustainability. Green grams are essential for human health, environmental sustainability, and agricultural success. However, erratic rainfall and temperature patterns significantly affect agricultural productivity, especially in arid areas such as Maragua Subcounty, Kenya. The research was directed by the subsequent objectives: (i) Evaluation of the impact of zai pits, mulch, and manure on the growth parameters and yield of green gram production, (ii) Investigation of the impacts of mulch and manure on the yield of green gram production. The data gathered throughout the experimental phase encompassed growth metrics of green gram, including leaf count, girth, height, and yield. The field experiments employed a randomized complete block design (RCBD) to guarantee the reliability and robustness of the results. The study employed an experiment to assess the impact of various treatments on the growth and yield of green gram production. Two replicates, each with its corresponding experimental units. Each replication comprises eight primary plots. The total number of plots per experimental site will be 16, yielding 96 sub-plots as each plot is divided into two sub-plots to accommodate the 8 types; this configuration will constitute a split-plot design. Each plot spans 2 meters by 2 meters, and each sub-plot has treatments implemented within an area of 60 cm by 60 cm, with a spacing of 80 cm by 20 cm between treatments. The analysis and data management of the collected data were performed using SPSS. An ANOVA test was performed to statistically evaluate the significance of the observed variations in plant height among the various treatments. The results indicated that the treatments had a statistically significant impact on plant height (p < 0.001), but site and block effects were not significant, demonstrating uniformity in the treatment response across the experimental conditions. Yield statistics corroborated these findings, indicating incremental gains from traditional farming to the integrated zai pit, manure, and mulch treatments. The traditional treatment yielded the least, whereas zai pit-based treatments, particularly when supplemented with manure and mulch, yielded the most. Duncan’s multiple range test identified seven unique subsets, demonstrating incremental and statistically significant yield enhancements with each additional treatment component. The research concludes that the incorporation of zai pits with organic soil amendments such as manure and mulch markedly enhances green gram development and yield in semi-arid conditions. The integration of these strategies improves soil moisture retention, nutrient accessibility, and general plant health, leading to enhanced vegetative growth and optimal yields. Smallholder farmers in semi-arid regions should implement integrated zai pit technology alongside organic inputs, such as manure and mulch, to optimize green gram productivity and enhance resilience to moisture stress.

Factors Affecting Beef Cattle Production Among Pastoral Communities of Marsabit County

2026-05-11T12:21:48Z

Factors Affecting Beef Cattle Production Among Pastoral Communities of Marsabit County Galm, Roba Waqo Beef cattle production in Kenya is a vital sector of the agricultural industry, contributing significantly to the country's economy and food security. The industry is dominated by indigenous breeds such as Zebu and Boran, which are well-adapted to Kenya's arid and semi-arid regions. This study focused on establishing the factors affecting beef cattle production among pastoral communities in Marsabit County Kenya. The following specific objectives guided the research: To determine the effects of inter-pastoral communities conflicts on beef cattle production, to determine the effects of livestock diseases on beef cattle production among pastoral communities in Marsabit County, to determine the effects of feed supplementation on beef cattle production among pastoral communities in Marsabit County, and to examine the effects of market prices on beef cattle production among pastoral communities in Marsabit County. Anchored under the Basic Needs Theory, Resilience and Food Production Theory and Livelihood Diversification Theory. The study adopted descriptive survey research design. The target population comprised of 1210 beef cattle farmers in Marsabit county from which a sample of 320 households was established through judgmental and stratified random sampling method. The distribution of the questionnaires was guided by the perceived level of engagement of respondents in beef cattle farming. Further, to corroborate the responses from the respondents on the variables of study. Data was collected through drop and pick and analyzed by use of SPSS version 27. Validity and reliability of the questionnaire constructs was confirmed before its use. Test for Multicollinearity Test, Test for Heteroscedasticity and Normality Test were conducted before multivariate regression analysis. A reliability coefficient of Cronbach alpha of over 0.7 was returned for all constructs of the independent variables. Descriptive statistics were used to explain the findings. Correlation analysis revealed that Inter community conflicts, livestock diseases, feed supplements and market price were positively and significantly correlated to Beef Cattle Production. Regression analysis results indicated that the factors under study explained 90.2% of the variation in Beef Cattle Production in Marsabit county with effects of Inter community conflicts not statistically significant (β=-0.024; p=0.455) as were effects of feed supplements (β= 0.022, p=0.593) while those of Livestock diseases (β=-0.112; p=0.014) and market price (β=0.820; p=0.000). The ANOVA results confirmed that the model was significant in predicting beef cattle production. It was concluded that all the independent variables collectively influenced beef cattle production. It was recommended that there is a need for proactive measures to mitigate inter-community conflicts, including conflict resolution initiatives, community dialogues, and improved security measures to safeguard both livestock and human lives. Future research should consider these areas can contribute to the development of evidence-based policies and interventions aimed at promoting sustainable beef cattle production and enhancing the resilience of pastoral communities in Marsabit County.

The Role of Explicit Knowledge Management on Promoting Organizational Decision-Making at Norwegian Refugee Council, Somalia

2026-05-06T06:21:31Z

The Role of Explicit Knowledge Management on Promoting Organizational Decision-Making at Norwegian Refugee Council, Somalia Abdifatah, Abdi Ali Organizational decision-making in humanitarian contexts is increasingly compromised by inadequate knowledge management systems. The Norwegian Refugee Council (NRC) Somalia has experienced substantial deviations from humanitarian benchmarks, including 50% increased emergency response times and 35% decline in program implementation efficiency. This study investigated how explicit knowledge management practices influenced organizational decision-making effectiveness at NRC Somalia, aiming to develop evidence-based recommendations enhancing humanitarian response capabilities through improved decision-making processes. The study objectives examined how documented knowledge acquisition processes, formal knowledge storage mechanisms, standardized knowledge sharing practices, and systematic knowledge utilization influenced organizational decision-making at NRC Somalia. The theoretical framework was anchored in Nonaka and Takeuchi's SECI model of knowledge conversion and contemporary decision-making theory by Nutt and Wilson. The study was conducted within NRC's operations in Somalia, encompassing coordination offices in Mogadishu and field offices across South Central Somalia, Puntland, and Somaliland. An explanatory sequential mixed-methods design was utilized, grounded in pragmatism philosophical underpinning. The target population comprised 100 NRC Somalia staff across five organizational levels. A census approach was employed for quantitative data collection, while purposive sampling selected 17 key informants for qualitative interviews. Data collection utilized structured questionnaires, semi-structured interview guides, and document analysis protocols. Validity was established through expert review and cognitive interviewing, while reliability was assessed using Cronbach's alpha coefficients exceeding 0.70 for all scales. The response rate was 89% (N=89). Quantitative data was analyzed using descriptive statistics, Pearson correlation analysis, and multiple linear regression, while qualitative data underwent thematic analysis following Braun and Clarke's framework. Major findings revealed documented knowledge acquisition processes operated in fragmented, reactive episodes rather than systematic approaches, with 52.8% agreement on after-action review documentation but significant gaps in stakeholder engagement (24.7%) and validation mechanisms (24.7%). Formal knowledge storage mechanisms demonstrated systematic failure across twelve fragmented platforms, with only 33.7% agreement on adequate backup procedures and 9.0% agreement on digital repository functionality. Standardized knowledge sharing practices showed paradoxical relationships between formal and informal mechanisms, with 47.2% agreement on regular meetings but poor cross-regional exchanges (22.0%) and tracking mechanisms (14.6%). Systematic knowledge utilization revealed critical weaknesses, with only 31.5% agreement on lesson adaptation and 15.7% agreement on application monitoring systems. The combined knowledge management practices explained 64.8% of variance in organizational decision-making effectiveness (R² = 0.648, F (4,84) = 38.67, p < 0.001). The study concluded that fragmented knowledge management systems created fundamental barriers to effective decision-making, with informal networks compensating for formal system inadequacies while operating outside institutional visibility. Knowledge sharing practices emerged as the most influential factor for decision outcomes (r = 0.72), while systematic utilization represented the most critical weakness requiring comprehensive organizational culture changes. The study recommended implementing integrated knowledge management systems that formalize successful informal mechanisms, establish mandatory knowledge consultation requirements, and develop hybrid approaches balancing security imperatives with accessibility requirements. This study provided the first comprehensive empirical analysis of knowledge management's influence on humanitarian decision-making in Somalia's volatile context, contributing novel insights for both theoretical frameworks and humanitarian practice optimization.

The Role of Records Maintenance in Promoting Citizen Participation in Open Governance at the Meru County Assembly, Kenya

2026-05-06T06:13:44Z

The Role of Records Maintenance in Promoting Citizen Participation in Open Governance at the Meru County Assembly, Kenya Fridah, Mariu Kajuju This research explored how records management practices shape citizen engagement in open governance within the Meru County Assembly, Kenya. Although constitutional provisions guarantee access to public information and participatory rights, persistent challenges including weak documentation systems, limited adoption of digital platforms, fragmented policy guidelines, and insufficient staff expertise have constrained meaningful involvement. The overarching purpose was to establish the extent to which improvements in records maintenance could reinforce transparency and expand civic participation. Specifically, the objectives were to evaluate the status of digitization, examine the robustness of policy frameworks, analyze the regularity and credibility of audit practices, and assess staff competencies in sustaining accurate and accessible records. The investigation was grounded in the Records Continuum Theory alongside participatory governance principles, both of which underscore the importance of systematic records stewardship as a foundation for accountability and public empowerment. Employing a descriptive mixed-methods design, the study was situated in Meru County Assembly. The target population comprised 424 individuals, from which a stratified random sample of 270 respondents was selected to capture representation from staff, legislators, and community members. Data were gathered through structured questionnaires, key-informant interviews, and documentary analysis. Instrument validity was established through expert review, while reliability was confirmed via a pilot test yielding a Cronbach’s alpha coefficient exceeding 0.70. Quantitative evidence was processed using descriptive statistics frequencies, percentages, means, and standard deviations together with inferential tests such as correlation and regression. Qualitative inputs were subjected to thematic interpretation. A response rate of 92% was achieved. Findings revealed that digitization efforts were partial and inconsistent, which limited timely access to records and undermined citizen participation. Policy instruments were disjointed and unevenly enforced, diminishing institutional transparency. Records audits were sporadic, compliance-oriented, and rarely publicized, thereby weakening accountability. Inadequate staff capacity, especially in digital literacy, further reduced the reliability of records and restricted accessibility. Consequently, citizen participation remained minimal due to poor information flow and weak record systems. The study concluded that incomplete digitization, incoherent policy frameworks, irregular auditing, and limited staff competence collectively constrained participatory governance. It recommended comprehensive deployment of electronic records systems, harmonization of management policies with constitutional mandates, institution of independent and publicly shared audits, and ongoing professional training for staff with emphasis on digital proficiencies. The originality of this study lies in demonstrating that improved records maintenance is not only an administrative necessity but also a strategic enabler of transparency, accountability, and inclusive citizen engagement within devolved governance structure.

An Assessment of Eco-Friendly Digital Records Management Practices for Promoting Environmental Sustainability: A Case Study of the Marsabit County Teaching and Referral Hospital

2026-04-13T08:45:33Z

An Assessment of Eco-Friendly Digital Records Management Practices for Promoting Environmental Sustainability: A Case Study of the Marsabit County Teaching and Referral Hospital Diba, Bilinga Kosi Environmental sustainability is a growing global concern, driving institutions to adopt eco-friendly practices in their daily operations. This study examined how paperless communication, digital archiving, cloud storage, and e-waste management contribute to sustainability at Marsabit County Teaching & Referral Hospital (MCTRH). Anchored on the Green Information Technology (Green IT) theory, a descriptive survey design was applied. Data were collected from 117 staff members through structured questionnaires and from three top managers via key informant interviews. Random sampling was used for staff, while key informants were purposively selected. Quantitative data were analyzed using descriptive statistics, and qualitative insights were thematically analyzed. Instrument validity was ensured through expert review and pre-testing, and reliability confirmed with Cronbach’s Alpha values above 0.7. The study achieved a 97% response rate. Results indicated that paperless communication is moderately adopted, cutting paper use and costs while supporting sustainability. Digital archiving improved accessibility and reduced physical storage needs, though adoption was inconsistent. Cloud storage enhanced collaboration and accessibility, offering strong sustainability benefits despite infrastructural challenges. E-waste management practices were partial, signaling the need for structured recycling and safe disposal. Other initiatives, including solar energy, green campaigns, and electronic medical records, were evident though unevenly adopted. The study concludes that eco-friendly digital records management significantly fosters environmental sustainability among healthcare. It recommends stronger policies to institutionalize paperless communication, investment in reliable archiving and cloud systems, and robust e-waste management frameworks. These findings contribute to the growing body of knowledge on sustainable healthcare management while offering practical implications for policymakers and administrators aiming to integrate green technologies into health information systems.

The Relationship Between User Education and Undergraduate Students’ Perception of University Libraries in Meru County

2026-03-05T09:14:53Z

The Relationship Between User Education and Undergraduate Students’ Perception of University Libraries in Meru County Chepkurui, Kibos Jane In the context of rapid technological advancements, information digitization, and the increasing availability of e-resources, effective user education has become crucial for enabling students to navigate and utilize university library resources. Despite these advancements, the two university libraries in Meru County, Kenya, have experienced suboptimal usage, potentially due to students' perceptions of the library. This study aimed to investigate the impact of user education programs on undergraduate students' perceptions and consequently library usage. The research was guided by objectives focusing on the types of user education programs offered, the extent of student participation, students' perceptions of the quality of these programs, and the barriers affecting user education. The literature was reviewed based on the research objectives. The study employed descriptive statistics and was anchored on the Expectancy-Confirmation Theory by Richard L. Oliver. The study was conducted in Meru County, focusing on two chartered universities: Kenya Methodist University (KeMU), a private university and Meru University of Science and Technology (MUST), a public university. The study employed descriptive statistics. The target population was 6138 first-year undergraduate students enrolled in the academic year 2023/2024. The study employed stratified sampling techniques based on academic schools. The study used Krejcie and Morgan (1970) table to determine the sample size, which was 364 students. The researcher purposively sampled a total of 12 out of 46 library staff. Data was collected from students using questionnaires and interviews for the staff. Pretesting of research instruments was done at Mount Kenya University, Meru Campus. Permission to collect data was sought from the National Commission for Science, Technology, and Innovation (NACOSTI). The computation of descriptive statistics was in the form of mean, mode, median, percentages, and standard deviation. The findings were presented using descriptive tables, figures, and narratives for ease of understanding the results. The findings revealed that library orientation and instruction sessions had high participation rates and were considered effective by the majority of students. Active participation in ongoing user education sessions was moderate, indicating that there was potential for improvement in terms of student involvement. Students generally had positive perceptions of the quality of user education programs. The programs were seen as significant to their educational pursuits, with high satisfaction levels regarding the relevance and adequacy of the resources provided. Barriers to user education included inadequate session time allotment and a lack of current digital resources. Recommendations include increasing the duration and frequency of user education sessions, updating digital resources, and utilizing promotional techniques such as social media for broader outreach. Future research could explore the long-term impact of user education on academic performance. This study contributes new insights into the relationship between user education and library perception, highlighting the importance of tailored educational interventions in enhancing library usage.

Social Media Promotion Strategies for Enhancing Student Engagement with Library Services: A Case of Strathmore and Riara University Libraries in Kenya

2026-02-12T09:59:05Z

Social Media Promotion Strategies for Enhancing Student Engagement with Library Services: A Case of Strathmore and Riara University Libraries in Kenya Segel, Winner Naisula University libraries are increasingly adopting social media as a means of promoting their services, yet the effectiveness of these strategies remains underexplored in the Kenyan private higher education context. This study examined how social media promotion strategies enhance library service provision to students at Strathmore and Riara University Libraries. The study focused on four strategies: content creation, user engagement mechanisms, targeted advertising, and gamification. A descriptive mixed-methods design was applied, involving a survey of 300 undergraduate students in Information Technology and Computer Science, of whom 255 responded (85%), and interviews with 36 librarians, of whom 20 participated (55.6%). Questionnaires were used for students, while semi-structured interviews captured insights from librarians. Quantitative data were analyzed using descriptive and inferential statistics, while qualitative data underwent thematic analysis. Reliability was confirmed through Cronbach’s Alpha coefficients above 0.7. The findings revealed that content creation, particularly infographics and regular posts, moderately improved student awareness (mean = 3.15). User engagement remained weak (mean = 2.25), as libraries mainly used platforms for information rather than interaction. Targeted advertising showed minimal impact (mean = 2.88), limited by financial and technical barriers. Gamification emerged as the most effective strategy, with quizzes and contests significantly motivating student participation (mean = 3.42). The study concludes that while social media enhances library visibility, its full potential remains underutilized. Practical recommendations include staff training in digital content creation, investment in interactive tools, and integration of gamification beyond orientations into ongoing library activities. The study contributes to policy and practice by providing an evidence-based framework for optimizing social media strategies in Kenyan university libraries.

Deep Learning Approach for Detection and Prediction of Pest Infections On Plants in Greenhouses

2026-02-12T09:32:39Z

Deep Learning Approach for Detection and Prediction of Pest Infections On Plants in Greenhouses Sambu, Bridgite Mueni Pest infestations remain a serious threat to greenhouse agriculture ultimately resulting in reduced yields, increased cost of production, and food safety concerns. Traditional pest monitoring strategies are predominantly manual in nature, which can be laborious, time-consuming, and exhibit human error. These downfalls can affect timely action on issues and a positive relationship management with pest monitoring can mean excessive pesticide use with financial and/or environmental impacts. This paper proposed an AI-enabled hybrid deep learning model to automate pest detection and estimate the probability of a pest outbreak. The hybrid model combined Convolutional Neural Networks (CNNs) for spatial (image-based) pest identification with Long Short-Term Memory (LSTM) networks to estimate probabilities of outbreaks based on sequences of environmental ((local) environmental data temperature, humidity, as well as, recorded counts of pests) and pest counts to help improve timing of interventions and proactive pest management solutions. The study made use of both primary and secondary datasets. The primary dataset was collected from three greenhouses in Limuru, Naivasha, and Thika, Kenya as well as high-resolution crop images (48 mega-pixels) representing various pest infections. Local environmental data for temperature, humidity, and pest counts were collected from the greenhouses to use as sequential variables in the LSTM component of the workflow and assist with estimating the accuracy of predicted outbreaks. Secondary datasets of pre-annotated pest images and historical climate records, including PlantVillage and IP102, provided bulked training data that improved models’ robustness and generalizability to unseen datasets. The researcher ensured stratified sampling to capture representation of all greenhouse types, farm sizes, and agro-climatic conditions. As part of preprocessing, all datasets underwent the following steps: image augmentation, noise removal and feature normalization. All model training, including hyperparameter tuning, occurred in a GPU-enabled Google Colab environment, and early stopping was used to avoid overfitting. The hybrid CNN-LSTM model produced a classification accuracy of 94.7%, precision of 93.9%, recall of 93.8%, and F1-score of 93.2%. The time-series predictive component of the LSTM model produced strong predictive performance with a mean absolute error (MAE) of 0.14, and R² value of 0.89. This showed that the environmental and measured sequence data of pests improve outbreak prediction. It was shown that the hybrid model accurately identifies pest infections, as well as predicting their outbreaks, which will contribute to providing early and timely interventions in the pest control system. Both high-resolution image data and measurements of local environments support a scalable and resilient choice for greenhouse pest management - the use of less pesticides, leading to sustainability in agriculture. In conclusion, this study has demonstrated the effectiveness of a hybrid deep learning framework for integrated pest management with operational and financial implications.

Development of A Machine Learning-Based Model Using a Decision Tree for Detecting Fake News: Analyzing Techniques for Accurate Content Verification

2026-02-12T09:23:42Z

Development of A Machine Learning-Based Model Using a Decision Tree for Detecting Fake News: Analyzing Techniques for Accurate Content Verification Tomba, Kinkosi Esther With the growing spread of information through social media and online news, identifying fake news has become increasingly important. To explore this issue, the Pew Research Center conducted surveys in the U.S. to examine how adults access news on social platforms, focusing on the behaviors and demographics of users who rely on these channels. This research sought to address a major gap in traditional fake news detection approaches, which were largely manual and lacked the sophistication of advanced machine learning and AI methods. Such conventional techniques struggle to handle the complexity and contextual manipulation of information, where accurate facts can be framed misleadingly. To overcome these shortcomings, the study developed a machine learning–based model to detect fake news by analyzing article content and recognizing patterns of misinformation. It utilized advanced natural language processing (NLP) techniques and supervised learning algorithms. Decision Trees (achieving 99.67% accuracy), Logistic Regression (99.13%), and Random Forest (99.15%). Processes such as tokenization and TF-IDF were applied to train the model on the ISO Fake News dataset, which combined real news from Reuters.com with fake news from unreliable sources flagged by PolitiFact and Wikipedia. Model performance was evaluated using accuracy, precision, recall, and F1-score, all reaching 99.67%, demonstrating exceptional detection capability. This work contributes to the field of machine learning by enhancing NLP methods and improving the effectiveness of fake news detection models. Future research is encouraged to expand datasets, incorporate multiple languages, employ deep learning like RNNs, CNNs, and Transformers (e.g., BERT and RoBERTa) for richer contextual understanding, and establish benchmarks based on real-world case studies.