The Trajectory of Romance Scams in the U.S

• URL: http://arxiv.org/abs/2405.03828v1
• Date: Mon, 6 May 2024 20:18:33 GMT
• Title: The Trajectory of Romance Scams in the U.S
• Authors: LD Herrera, John Hastings,
• Abstract summary: This study examines RS trends in the U.S. through a quantitative analysis of web searches, news articles, research publications, and government reports from 2004 to 2023. Results reveal increasing public interest and media coverage contrasted by a recent decrease in incidents reported to authorities. Findings suggest RS escalation despite declining official reports, which are likely obscured by low victim reporting rates.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Romance scams (RS) inflict financial and emotional damage by defrauding victims under the guise of meaningful relationships. This research study examines RS trends in the U.S. through a quantitative analysis of web searches, news articles, research publications, and government reports from 2004 to 2023. This is the first study to use multiple sources for RS trend analysis. Results reveal increasing public interest and media coverage contrasted by a recent decrease in incidents reported to authorities. The frequency of research dedicated to RS has steadily grown but focuses predominantly on documenting the problem rather than developing solutions. Overall, findings suggest RS escalation despite declining official reports, which are likely obscured by low victim reporting rates. This highlights the need for greater awareness to encourage reporting enabling accurate data-driven policy responses. Additionally, more research must focus on techniques to counter these crimes. With improved awareness and prevention, along with responses informed by more accurate data, the rising RS threat can perhaps be mitigated.

Related papers

• Illusions of Relevance: Using Content Injection Attacks to Deceive Retrievers, Rerankers, and LLM Judges [52.96987928118327]
  We find that embedding models for retrieval, rerankers, and large language model (LLM) relevance judges are vulnerable to content injection attacks. We identify two primary threats: (1) inserting unrelated or harmful content within passages that still appear deceptively "relevant", and (2) inserting entire queries or key query terms into passages to boost their perceived relevance. Our study systematically examines the factors that influence an attack's success, such as the placement of injected content and the balance between relevant and non-relevant material.
  arXiv  Detail & Related papers  (2025-01-30T18:02:15Z)
• Model Inversion Attacks: A Survey of Approaches and Countermeasures [59.986922963781]
  Recently, a new type of privacy attack, the model inversion attacks (MIAs), aims to extract sensitive features of private data for training. Despite the significance, there is a lack of systematic studies that provide a comprehensive overview and deeper insights into MIAs. This survey aims to summarize up-to-date MIA methods in both attacks and defenses.
  arXiv  Detail & Related papers  (2024-11-15T08:09:28Z)
• A Survey of Privacy-Preserving Model Explanations: Privacy Risks, Attacks, and Countermeasures [50.987594546912725]
  Despite a growing corpus of research in AI privacy and explainability, there is little attention on privacy-preserving model explanations. This article presents the first thorough survey about privacy attacks on model explanations and their countermeasures.
  arXiv  Detail & Related papers  (2024-03-31T12:44:48Z)
• On the Security Risks of Knowledge Graph Reasoning [71.64027889145261]
  We systematize the security threats to KGR according to the adversary's objectives, knowledge, and attack vectors. We present ROAR, a new class of attacks that instantiate a variety of such threats. We explore potential countermeasures against ROAR, including filtering of potentially poisoning knowledge and training with adversarially augmented queries.
  arXiv  Detail & Related papers  (2023-05-03T18:47:42Z)
• 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)
• Coronavirus statistics causes emotional bias: a social media text mining perspective [4.042350304426975]
  This paper proposes a deep learning model which classifies texts related to the pandemic from text data with place labels. Next, it conducts a sentiment analysis based on multi-task learning. Finally, it carries out a fixed-effect panel regression with outputs of the sentiment analysis.
  arXiv  Detail & Related papers  (2022-11-16T03:36:13Z)
• Trustworthy Recommender Systems [27.27498627500375]
  Recommender systems (RSs) aim to help users to effectively retrieve items of their interests from a large catalogue. Recent years have witnessed an increasing number of threats to RSs, coming from attacks, system and user generated noise, system bias. For end users, a trustworthy RS (TRS) should not only be accurate, but also transparent, unbiased and fair as well as robust to noise or attacks.
  arXiv  Detail & Related papers  (2022-08-10T08:28:46Z)
• Adherence to Misinformation on Social Media Through Socio-Cognitive and Group-Based Processes [79.79659145328856]
  We argue that when misinformation proliferates, this happens because the social media environment enables adherence to misinformation. We make the case that polarization and misinformation adherence are closely tied.
  arXiv  Detail & Related papers  (2022-06-30T12:34:24Z)
• Automatic Monitoring Social Dynamics During Big Incidences: A Case Study of COVID-19 in Bangladesh [0.26651200086513094]
  This study analyzed a large set of Bangladeshi newspaper data related to CO-19 pandemic. This analysis will help the government and other organizations to figure out the challenges that have arisen in society due to this pandemic.
  arXiv  Detail & Related papers  (2021-01-24T07:46:17Z)
• 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)
• Critical Impact of Social Networks Infodemic on Defeating Coronavirus COVID-19 Pandemic: Twitter-Based Study and Research Directions [1.6571886312953874]
  An estimated 2.95 billion people in 2019 used social media worldwide. The widespread of the Coronavirus COVID-19 resulted with a tsunami of social media. This paper presents a large-scale study based on data mined from Twitter.
  arXiv  Detail & Related papers  (2020-05-18T15:53:13Z)

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.