Good jobs, scam jobs: Detecting, normalizing, and internalizing online job scams during the COVID-19 pandemic

Ravenelle, Alexandrea J.; Janko, Erica; Kowalski, Ken Cai

doi:10.1177/14614448221099223

Cited by 10 publications

(3 citation statements)

References 28 publications

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“…Goyal et al (2023) find that email addresses are commonly used to approach victims, with mentions of telecommuting jobs and COVID-19 being frequent among victims. Ravenelle et al (2022) identify early detection strategies for fraudulent job postings, such as requests for personal information, poor wording in the advertisement, jobs with significant benefits, and persistent communication through digital media platforms. Carmen et al (2010) suggest identifying misspelled words, vague information, and incorrect details in job postings as indicators of fraudulent advertisements.…”

Section: Fraudulent In Employmentmentioning

confidence: 99%

Machine Learning Approach in Predicting Fraudulent Job Advertisement

Mohd Hanif,

Maarop,

Kamaruddin

et al. 2024

IJARBSS

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As the world population grows, the demand for workers increases, leading to a rise in online job advertisements to connect employers with potential employees on a national scale. However, this shift also brings the risk of falling victim to fraud. Reported commercial crimes in Malaysia saw a 15.3% increase in 2021, with fraud being the highest among them. Several studies have proposed Machine Learning models to classify genuine and fraudulent job advertisements, but the analysis of certain techniques remains limited. The paper aims to develop a predictive model for identifying fraudulent job advertisements using selected features from imbalanced and balanced datasets. The Employment Scam Aegean Dataset was utilized to build Machine Learning classification models using Logistic Regression, Support Vector Machine, Decision Tree, and Naïve Bayes algorithms. These models were combined with different vectorizers like Term Frequency-Inverse Document Frequency, Bag of Words, and Hash. The Decision Tree model with Bag of Words vectorizer on a balanced dataset outperformed other models, achieving an accuracy of 0.705, precision of 0.73, recall of 0.70, F1-score of 0.71, and Area Under Curve score of 0.68. This model shows promise in effectively identifying fraudulent job advertisements, safeguarding job seekers from scams in the online job market.

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Section: Fraudulent In Employmentmentioning

confidence: 99%

Machine Learning Approach in Predicting Fraudulent Job Advertisement

Mohd Hanif,

Maarop,

Kamaruddin

et al. 2024

IJARBSS

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“…By having lower vetting standards for customers and other actors who pay money for fees/services brokered by platform owners, platforms privilege customers (e.g., clients on TaskRabbit, riders on Lyft, buyers on eBay) (Rahman and Valentine, 2021;Maffie, 2022b). This dynamic has led studies to highlight the prevalence of "scammers" and unethical actors on platforms (Grohmann et al, 2022;, especially during the height of the COVID-19 pandemic (Ravenelle et al, 2022).…”

Section: Bottom-up Accountability Through Strict Adherence To Platfor...mentioning

confidence: 99%

Taming Platform Power: Taking Accountability Into Account In The Management Of Platforms

Rahman,

Karunakaran,

Cameron

2023

Preprint

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Research on multi-sided platforms emphasizes how platform owners have accumulated significant power over other platform actors, such as producers and customers, arguing for the need to balance such power with accountability. We review two perspectives on platform accountability: a) a bottom-up, emergent perspective that focuses on the collective action taken by lower-powered platform actors such as producers (e.g., gig workers, app developers) to enhance rule adequacy and push back against platform owners’ power, and b) a top-down, institutional perspective that emphasizes the importance of preventing extractive opportunism and maintaining a level playing field among different platform actors by enabling legal, regulatory, and governance changes. The bottom-up perspective’s overarching focus is on procedural (rulefocused) fairness, while the top-down perspective’s focus is largely on distributive (outcome-focused) fairness. While both perspectives are important, they have limitations regarding platform accountability, especially given the power and informational asymmetries inherent among platform actors. Therefore, synthesizing across literatures, we provide a framework for platform accountability that accounts for both procedural and distributive fairness, and is based on a fundamental premise: multi-sided platforms require multi-sided accountability systems. Thus, our review proposes an approach for enforcing platform accountability that has the potential to rebalance the power between high-powered and low-powered platform actors.

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“…Although the leisure patterns and sports values of people from different urban living backgrounds may vary [25], some scholars believe that maintaining positive leisure sports values can provide stable and efficient human resources and promote urban development [26,27]. In addition, maintaining a positive employment risk perception is beneficial for job seekers to obtain satisfactory welfare [28]. Furthermore, positive employment risk perceptions can help job seekers avoid workplace pitfalls and unfair treatment [29], which can help the public obtain sufficient and stable resources to meet their survival needs and improve their quality of life [30].…”

Section: Introductionmentioning

confidence: 99%

Under the Risk of COVID-19 Epidemic: A Study on the Influence of Life Attitudes, Leisure Sports Values, and Workplace Risk Perceptions on Urban Development and Public Well-Being

Zheng

Lin

et al. 2023

Sustainability

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This study examined the impact of attitudes toward life, recreational sports values, and workplace risk perceptions on urban development and public well-being under the risk of the COVID-19 epidemic in China. A mixed-method research study was conducted, and 2400 valid questionnaires were collected via purposeful sampling. The questionnaires were analyzed using the SPSS 26.0 statistical software and validated with basic statistical methods and Pearson’s correlation analysis. Semi-structured interviews were then conducted to collect the opinions of 12 respondents on the questionnaire results, including academics, foreign entrepreneurs and employees, local entrepreneurs and employees, and official institutions. Finally, all the data collected were discussed via triangulation analysis. The survey found that different cities’ social and industrial development and job market needs lead to different attitudes toward life, leisure values, and perceptions of job-seeking and employment risks. The smoother a person’s growing-up background, the better their learning history, the better their learning and working experience, and the richer their life experience; consequently, positive attitudes toward life, correct leisure values, and positive attitudes toward job hunting and employment can be cultivated. In addition, people with positive attitudes toward life, leisure values, and job hunting and employment can improve their city’s economy and environment for sustainable development, thereby improving their quality of life and increasing their well-being.

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Good jobs, scam jobs: Detecting, normalizing, and internalizing online job scams during the COVID-19 pandemic

Cited by 10 publications

References 28 publications

Machine Learning Approach in Predicting Fraudulent Job Advertisement

Machine Learning Approach in Predicting Fraudulent Job Advertisement

Taming Platform Power: Taking Accountability Into Account In The Management Of Platforms

Under the Risk of COVID-19 Epidemic: A Study on the Influence of Life Attitudes, Leisure Sports Values, and Workplace Risk Perceptions on Urban Development and Public Well-Being

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