Mitigating Human and Computer Opinion Fraud via Contrastive Learning

• URL: http://arxiv.org/abs/2301.03025v1
• Date: Sun, 8 Jan 2023 12:02:28 GMT
• Title: Mitigating Human and Computer Opinion Fraud via Contrastive Learning
• Authors: Yuliya Tukmacheva, Ivan Oseledets, Evgeny Frolov
• Abstract summary: We introduce the novel approach towards fake text reviews detection in collaborative filtering recommender systems. The existing algorithms concentrate on detecting the fake reviews, generated by language models and ignore the texts, written by dishonest users. We propose the contrastive learning-based architecture, which utilizes the user demographic characteristics, along with the text reviews, as the additional evidence against fakes.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce the novel approach towards fake text reviews detection in collaborative filtering recommender systems. The existing algorithms concentrate on detecting the fake reviews, generated by language models and ignore the texts, written by dishonest users, mostly for monetary gains. We propose the contrastive learning-based architecture, which utilizes the user demographic characteristics, along with the text reviews, as the additional evidence against fakes. This way, we are able to account for two different types of fake reviews spamming and make the recommendation system more robust to biased reviews.

Related papers

• Enhanced Review Detection and Recognition: A Platform-Agnostic Approach with Application to Online Commerce [0.46040036610482665]
  We present a machine learning methodology for review detection and extraction. We demonstrate that it generalises for use across websites that were not contained in the training data. This method promises to drive applications for automatic detection and evaluation of reviews, regardless of their source.
  arXiv  Detail & Related papers  (2024-05-09T00:32:22Z)
• Verifying the Robustness of Automatic Credibility Assessment [50.55687778699995]
  We show that meaning-preserving changes in input text can mislead the models. We also introduce BODEGA: a benchmark for testing both victim models and attack methods on misinformation detection tasks. Our experimental results show that modern large language models are often more vulnerable to attacks than previous, smaller solutions.
  arXiv  Detail & Related papers  (2023-03-14T16:11:47Z)
• Combat AI With AI: Counteract Machine-Generated Fake Restaurant Reviews on Social Media [77.34726150561087]
  We propose to leverage the high-quality elite Yelp reviews to generate fake reviews from the OpenAI GPT review creator. We apply the model to predict non-elite reviews and identify the patterns across several dimensions. We show that social media platforms are continuously challenged by machine-generated fake reviews.
  arXiv  Detail & Related papers  (2023-02-10T19:40:10Z)
• Social Fraud Detection Review: Methods, Challenges and Analysis [42.30892608083864]
  Social reviews have dominated the web and become a plausible source of product information. Business make use of social information to spread fake information using a single user, groups of users, or a bot trained to generate fraudulent content. Many studies proposed approaches based on user behaviors and review text to address the challenges of fraud detection.
  arXiv  Detail & Related papers  (2021-11-10T11:25:20Z)
• Hierarchical Bi-Directional Self-Attention Networks for Paper Review Rating Recommendation [81.55533657694016]
  We propose a Hierarchical bi-directional self-attention Network framework (HabNet) for paper review rating prediction and recommendation. Specifically, we leverage the hierarchical structure of the paper reviews with three levels of encoders: sentence encoder (level one), intra-review encoder (level two) and inter-review encoder (level three) We are able to identify useful predictors to make the final acceptance decision, as well as to help discover the inconsistency between numerical review ratings and text sentiment conveyed by reviewers.
  arXiv  Detail & Related papers  (2020-11-02T08:07:50Z)
• Fact or Factitious? Contextualized Opinion Spam Detection [9.415901312074336]
  We apply a number machine learning approaches found to be effective, and introduce our own approach by fine-tuning state of the art contextualised embeddings. The results we obtain show the potential of contextualised embeddings for fake review detection, and lay the groundwork for future research in this area.
  arXiv  Detail & Related papers  (2020-10-29T00:59:06Z)
• Fake Reviews Detection through Analysis of Linguistic Features [1.609940380983903]
  This paper explores a natural language processing approach to identify fake reviews. We study 15 linguistic features for distinguishing fake and trustworthy online reviews. We were able to discriminate fake from real reviews with high accuracy using these linguistic features.
  arXiv  Detail & Related papers  (2020-10-08T21:16:30Z)
• Partial Bandit and Semi-Bandit: Making the Most Out of Scarce Users' Feedback [62.997667081978825]
  We present a novel approach for considering user feedback and evaluate it using three distinct strategies. Despite a limited number of feedbacks returned by users (as low as 20% of the total), our approach obtains similar results to those of state of the art approaches.
  arXiv  Detail & Related papers  (2020-09-16T07:32:51Z)
• A Robust Opinion Spam Detection Method Against Malicious Attackers in Social Media [0.0]
  It is a way a smart spammer can deceive the system in a manner in which he can continue generating spams without the fear of being detected and blocked by the system. A robust graph-based spam detection method is proposed.
  arXiv  Detail & Related papers  (2020-08-19T19:54:44Z)
• A Unified Dual-view Model for Review Summarization and Sentiment Classification with Inconsistency Loss [51.448615489097236]
  Acquiring accurate summarization and sentiment from user reviews is an essential component of modern e-commerce platforms. We propose a novel dual-view model that jointly improves the performance of these two tasks. Experiment results on four real-world datasets from different domains demonstrate the effectiveness of our model.
  arXiv  Detail & Related papers  (2020-06-02T13:34:11Z)
• Unsupervised Opinion Summarization with Noising and Denoising [85.49169453434554]
  We create a synthetic dataset from a corpus of user reviews by sampling a review, pretending it is a summary, and generating noisy versions thereof. At test time, the model accepts genuine reviews and generates a summary containing salient opinions, treating those that do not reach consensus as noise.
  arXiv  Detail & Related papers  (2020-04-21T16:54:57Z)

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.