Poster: Leveraging Data Analysis and Machine Learning to Authenticate Yelp Reviews through User Metadata Patterns
Poster: Leveraging Data Analysis and Machine Learning to Authenticate Yelp Reviews through User Metadata Patterns
复制标题
海报：利用数据分析和机器学习通过用户元数据模式验证 Yelp 评论
DOI：
10.1145/3565287.3617983
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Jayarathna, Sampath
中科院分区：
文献类型：
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作者：
Richards, Johnovon;Dabhi, Saumya;Poursardar, Faryaneh;Jayarathna, Sampath
由于技术革命，在线评论对消费者决策变得非常有价值，大约93%的消费者依靠评论来做出购买决定。然而，虚假在线评论的兴起引发了人们对其可信度的担忧，因为据估计，所有互联网评论中有4%是欺诈性的，这对全球在线购物造成了1520亿美元的影响。这种普遍现象不仅误导了消费者，也给那些努力维护自己声誉的企业带来了挑战。著名的在线评论平台Yelp在塑造消费者观念和购买选择方面发挥着至关重要的作用。然而，Yelp上存在的虚假评论引发了人们对诚信的担忧。本研究探讨了使用数据分析技术和机器学习算法来确定Yelp评论的真实性，有助于提高在线评论系统的可信度，实现明智的决策，并促进企业之间的公平竞争。
Due to the technological revolution, online reviews have become extremely valuable to consumer decision making, with approximately 93% of all consumers relying on reviews for purchasing decisions. However, the rise of fake online reviews has raised concerns about their trustworthiness, as it is estimated that 4% of all internet reviews are estimated to be fraudulent, impacting worldwide online purchases by $152 billion. This prevalence not only misleads consumers but also challenges businesses striving to maintain their reputation. Yelp, a prominent platform hosting online reviews, plays a crucial role in shaping consumer perceptions and purchasing choices. However, the presence of fake reviews on Yelp has raised integrity concerns. This research explores the use of data analysis techniques and machine learning algorithms to determine the authenticity of Yelp reviews, contributing to the enhancement of the credibility of online review systems, enabling informed decisions, and promoting fair competition among businesses.
DOI：
10.1109/bigdata.2017.8257963
发表时间：
2017-12
期刊：
2017 IEEE International Conference on Big Data (Big Data)
影响因子：
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作者：
X. Wu;Yuxiao Dong;Jun Tao;Chao Huang;N. Chawla
通讯作者：
X. Wu;Yuxiao Dong;Jun Tao;Chao Huang;N. Chawla