Using Supervised Learning to Classify Authentic and Fake Online Reviews

被引：15
|
作者：
Banerjee, Snehasish [1 ]
Chua, Alton Y. K. [1 ]
Kim, Jung-Jae [2 ]
机构：
[1] Nanyang Technol Univ, Wee Kim Wee Sch Commun & Informat, Singapore, Singapore
[2] Nanyang Technol Univ, Sch Comp Engn, Singapore, Singapore
来源：
ACM IMCOM 2015, PROCEEDINGS | 2015年
关键词：
Internet shopping; authentic online reviews; fake online reviews; linguistic clues; supervised learning; PREDICTING DECEPTION; WORDS;
D O I：
10.1145/2701126.2701130
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
Before making a purchase, users are increasingly inclined to browse online reviews that are posted to share post-purchase experiences of products and services. However, not all reviews are necessarily authentic. Some entries could be fake yet written to appear authentic. Conceivably, authentic and fake reviews are not easy to differentiate. Hence, this paper uses supervised learning algorithms to analyze the extent to which authentic and fake reviews could be distinguished based on four linguistic clues, namely, understandability, level of details, writing style, and cognition indicators. The model performance was compared with two baselines. The results were generally promising.
引用
收藏
页数：7
相关论文
共 50 条
  • [1] Classification of Authentic and Fake Online Reviews with Supervised Machine Learning Techniques
    Kurtcan, Betul Durkaya
    Kaya, Tolga
    [J]. PROCEEDINGS OF THE SIXTEENTH INTERNATIONAL CONFERENCE ON MANAGEMENT SCIENCE AND ENGINEERING MANAGEMENT - VOL 1, 2022, 144 : 309 - 319
  • [2] A Supervised Machine Learning Approach to Detect Fake Online Reviews
    Hassan, Rakibul
    Islam, Md Rabiul
    [J]. 2020 23RD INTERNATIONAL CONFERENCE ON COMPUTER AND INFORMATION TECHNOLOGY (ICCIT 2020), 2020,
  • [3] Fake Reviews Detection using Supervised Machine Learning
    Elmogy, Ahmed M.
    Tariq, Usman
    Ibrahim, Atef
    Mohammed, Ammar
    [J]. INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2021, 12 (01) : 601 - 606
  • [4] Data Analytics for the Identification of Fake Reviews Using Supervised Learning
    Alsubari, Saleh Nagi
    Deshmukh, Sachin N.
    Alqarni, Ahmed Abdullah
    Alsharif, Nizar
    Aldhyani, Theyazn H. H.
    Alsaade, Fawaz Waselallah
    Khalaf, Osamah I.
    [J]. CMC-COMPUTERS MATERIALS & CONTINUA, 2022, 70 (02): : 3189 - 3204
  • [5] Online "helpful" Lies: An Empirical Study of Helpfulness in Fake and Authentic Online Reviews
    Chua, Alton Y. K.
    Chen, Xiaoyu
    [J]. INFORMATION FOR A BETTER WORLD: SHAPING THE GLOBAL FUTURE, PT I, 2022, 13192 : 91 - 99
  • [6] A Methodological Template to Construct Ground Truth of Authentic and Fake Online Reviews
    Banerjee, Snehasish
    [J]. 2018 IEEE 5TH INTERNATIONAL CONFERENCE ON DATA SCIENCE AND ADVANCED ANALYTICS (DSAA), 2018, : 641 - 648
  • [7] Detecting fake reviews with supervised machine learning algorithms
    Lee, Minwoo
    Song, Young Ho
    Li, Lin
    Lee, Kyung Young
    Yang, Sung-Byung
    [J]. SERVICE INDUSTRIES JOURNAL, 2022, 42 (13-14): : 1101 - 1121
  • [8] Detection of Fake Reviews on Online Products Using Machine Learning Algorithms
    Krishnan, H. Muthu
    Preetha, J.
    Shona, S. P.
    Sivakami, A.
    [J]. INNOVATIONS IN BIO-INSPIRED COMPUTING AND APPLICATIONS, IBICA 2021, 2022, 419 : 314 - 319
  • [9] Exaggeration in fake vs. authentic online reviews for luxury and budget hotels
    Banerjee, Snehasish
    [J]. INTERNATIONAL JOURNAL OF INFORMATION MANAGEMENT, 2022, 62
  • [10] Sentiment Analysis and Fake Amazon Reviews Classification Using SVM Supervised Machine Learning Model
    Tabany, Myasar
    Gueffal, Meriem
    [J]. JOURNAL OF ADVANCES IN INFORMATION TECHNOLOGY, 2024, 15 (01) : 49 - 58
12345