Guardado en:
| Autores principales: | , , |
|---|---|
| Formato: | Recurso digital |
| Lenguaje: | |
| Publicado: |
Zenodo
2026
|
| Acceso en línea: | https://doi.org/10.5281/zenodo.20338959 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866902007348985856 |
|---|---|
| author | Dr Raj Kumar Viklpa Rimmy |
| author_facet | Dr Raj Kumar Viklpa Rimmy |
| contents | <p>The influence of online consumer reviews on purchasing behavior, market visibility, and perceived credibility of digital platforms is significant. Consumers' evaluation of product quality, service reliability, and seller trustworthiness is heavily influenced by review ratings and textual opinions, as demonstrated by empirical studies. Small changes in aggregated ratings can have a significant impact on sales volume, search rankings, and long-term brand reputation. Online reviews have become high-value informational assets due to this dependency, which makes review systems attractive targets for manipulation. Economic and social consequences have been significant due to the increasing prevalence of fake and deceptive reviews. Unfair competitive advantages are gained by businesses who engage in review manipulation, while honest sellers suffer revenue loss despite offering comparable or superior quality. Consumers who are exposed to deceptive reviews face an increased risk of poor purchasing decisions, wasted expenditure, and reduced confidence in online marketplaces. Repeated exposure to fraudulent content at a large scale can erode platform credibility, weaken user engagement, and undermine the integrity of digital ecosystems constructed on user-generated content. In modern review environments, manual moderation mechanisms, such as user reporting and expert inspection, are not effective. The processing of millions of reviews daily by large platforms makes human-driven verification costly, inconsistent, and slow. To maintain trust, fairness, and scalability, automated fake review detection has become a crucial requirement. Despite the challenges, designing effective detection systems remains a challenge. To resemble genuine user opinions, fake reviews are often crafted in a way that is linguistically fluent, sentimentally plausible, and strategically crafted. To evade detection, deceptive reviews take advantage of subjectivity, context dependence, and social norms, unlike traditional spam.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_20338959 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Methods, Challenges, And Future Directions For Detecting Fake Reviews Dr Raj Kumar Viklpa Rimmy <p>The influence of online consumer reviews on purchasing behavior, market visibility, and perceived credibility of digital platforms is significant. Consumers' evaluation of product quality, service reliability, and seller trustworthiness is heavily influenced by review ratings and textual opinions, as demonstrated by empirical studies. Small changes in aggregated ratings can have a significant impact on sales volume, search rankings, and long-term brand reputation. Online reviews have become high-value informational assets due to this dependency, which makes review systems attractive targets for manipulation. Economic and social consequences have been significant due to the increasing prevalence of fake and deceptive reviews. Unfair competitive advantages are gained by businesses who engage in review manipulation, while honest sellers suffer revenue loss despite offering comparable or superior quality. Consumers who are exposed to deceptive reviews face an increased risk of poor purchasing decisions, wasted expenditure, and reduced confidence in online marketplaces. Repeated exposure to fraudulent content at a large scale can erode platform credibility, weaken user engagement, and undermine the integrity of digital ecosystems constructed on user-generated content. In modern review environments, manual moderation mechanisms, such as user reporting and expert inspection, are not effective. The processing of millions of reviews daily by large platforms makes human-driven verification costly, inconsistent, and slow. To maintain trust, fairness, and scalability, automated fake review detection has become a crucial requirement. Despite the challenges, designing effective detection systems remains a challenge. To resemble genuine user opinions, fake reviews are often crafted in a way that is linguistically fluent, sentimentally plausible, and strategically crafted. To evade detection, deceptive reviews take advantage of subjectivity, context dependence, and social norms, unlike traditional spam.</p> |
| title | Methods, Challenges, And Future Directions For Detecting Fake Reviews |
| url | https://doi.org/10.5281/zenodo.20338959 |