A Survey of Scam Exposure, Victimization, Types, Vectors, and Reporting in 12 Countries
- URL: http://arxiv.org/abs/2407.12896v1
- Date: Wed, 17 Jul 2024 14:35:56 GMT
- Title: A Survey of Scam Exposure, Victimization, Types, Vectors, and Reporting in 12 Countries
- Authors: Mo Houtti, Abhishek Roy, Venkata Narsi Reddy Gangula, Ashley Marie Walker,
- Abstract summary: The present study addresses this gap through a nationally representative survey on scam exposure, victimization, types, vectors, and reporting in 12 countries.
We find, first, that residents of less affluent countries suffer financial loss from scams more often.
Second, we find that the internet plays a key role in scams across the globe, and that GNI per-capita is strongly associated with specific scam types and contact vectors.
- Score: 3.2545498077804083
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Scams are a widespread issue with severe consequences for both victims and perpetrators, but existing data collection is fragmented, precluding global and comparative local understanding. The present study addresses this gap through a nationally representative survey (n = 8,369) on scam exposure, victimization, types, vectors, and reporting in 12 countries: Belgium, Egypt, France, Hungary, Indonesia, Mexico, Romania, Slovakia, South Africa, South Korea, Sweden, and the United Kingdom. We analyze 6 survey questions to build a detailed quantitative picture of the scams landscape in each country, and compare across countries to identify global patterns. We find, first, that residents of less affluent countries suffer financial loss from scams more often. Second, we find that the internet plays a key role in scams across the globe, and that GNI per-capita is strongly associated with specific scam types and contact vectors. Third, we find widespread under-reporting, with residents of less affluent countries being less likely to know how to report a scam. Our findings contribute valuable insights for researchers, practitioners, and policymakers in the online fraud and scam prevention space.
Related papers
- An Explorative Study of Pig Butchering Scams [18.980991664884556]
We provide the first comprehensive study of pig-butchering scams from multiple vantage points.
Our study analyzes the direct victims' narratives shared on multiple social media platforms, public abuse report databases, and case studies from news outlets.
In total, we approximated losses of over $521 million related to such scams.
arXiv Detail & Related papers (2024-12-19T22:15:50Z) - Tracing the Unseen: Uncovering Human Trafficking Patterns in Job Listings [9.450459784653196]
We analyze over a quarter million job postings collected from eight relevant regions across the United States, spanning nearly two decades (2006-2024)
Our investigation into the types of advertised opportunities, the modes of preferred contact, and the frequency of postings uncovers the patterns characterizing suspicious ads.
This research underscores the imperative for a deeper dive into how online job boards and communication platforms could be unwitting facilitators of human trafficking.
arXiv Detail & Related papers (2024-06-18T10:18:15Z) - News and Misinformation Consumption in Europe: A Longitudinal
Cross-Country Perspective [49.1574468325115]
This study investigated information consumption in four European countries.
It analyzed three years of Twitter activity from news outlet accounts in France, Germany, Italy, and the UK.
Results indicate that reliable sources dominate the information landscape, although unreliable content is still present across all countries.
arXiv Detail & Related papers (2023-11-09T16:22:10Z) - Human Behavior in the Time of COVID-19: Learning from Big Data [71.26355067309193]
Since March 2020, there have been over 600 million confirmed cases of COVID-19 and more than six million deaths.
The pandemic has impacted and even changed human behavior in almost every aspect.
Researchers have been employing big data techniques such as natural language processing, computer vision, audio signal processing, frequent pattern mining, and machine learning.
arXiv Detail & Related papers (2023-03-23T17:19:26Z) - Tainted Love: A Systematic Review of Online Romance Fraud [68.8204255655161]
Romance fraud involves cybercriminals engineering a romantic relationship on online dating platforms.
We characterise the literary landscape on romance fraud, advancing the understanding of researchers and practitioners.
Three main contributions were identified: profiles of romance scams, countermeasures for mitigating romance scams, and factors that predispose an individual to become a scammer or a victim.
arXiv Detail & Related papers (2023-02-28T20:34:07Z) - ForestEyes Project: Conception, Enhancements, and Challenges [68.8204255655161]
This work presents a Citizen Science project called ForestEyes.
It uses volunteer's answers through the analysis and classification of remote sensing images to monitor deforestation regions in rainforests.
To evaluate the quality of those answers, different campaigns/workflows were launched using remote sensing images from Brazilian Legal Amazon.
arXiv Detail & Related papers (2022-08-24T17:48:12Z) - Misinformation, Believability, and Vaccine Acceptance Over 40 Countries:
Takeaways From the Initial Phase of The COVID-19 Infodemic [11.737540072863405]
This paper presents findings from a global survey on the extent of worldwide exposure to the COVID-19 infodemic.
We find a strong association between perceived believability of misinformation and vaccination hesitancy.
We discuss implications of our findings on public campaigns that proactively spread accurate information to countries that are more susceptible to the infodemic.
arXiv Detail & Related papers (2021-04-22T05:09:25Z) - Country Image in COVID-19 Pandemic: A Case Study of China [79.17323278601869]
Country image has a profound influence on international relations and economic development.
In the worldwide outbreak of COVID-19, countries and their people display different reactions.
In this study, we take China as a specific and typical case and investigate its image with aspect-based sentiment analysis on a large-scale Twitter dataset.
arXiv Detail & Related papers (2020-09-12T15:54:51Z) - Twitter Interaction to Analyze Covid-19 Impact in Ghana, Africa from
March to July [0.0]
We use text mining to draw insights from data collected from Twitter.
We observe that the engagement of users of this social network was initially high in March but declined from April to July.
We also found certain words in these tweets that enabled us to understand the sentiments and mental state of individuals at the time.
arXiv Detail & Related papers (2020-08-27T17:29:36Z) - Cross-lingual Transfer Learning for COVID-19 Outbreak Alignment [90.12602012910465]
We train on Italy's early COVID-19 outbreak through Twitter and transfer to several other countries.
Our experiments show strong results with up to 0.85 Spearman correlation in cross-country predictions.
arXiv Detail & Related papers (2020-06-05T02:04:25Z) - CoronaSurveys: Using Surveys with Indirect Reporting to Estimate the
Incidence and Evolution of Epidemics [29.03294669532478]
We propose a technique based on (anonymous) surveys in which participants report on the health status of their contacts.
This indirect reporting technique, known in the literature as network scale-up method, preserves the privacy of the participants and their contacts.
Results obtained by CoronaSurveys show the power and flexibility of the approach, suggesting that it could be an inexpensive and powerful tool for LMICs.
arXiv Detail & Related papers (2020-05-24T11:58:23Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.