FactCatch: Incremental Pay-as-You-Go Fact Checking with Minimal User Effort

Nguyên, Thành Tâm; Weidlich, Matthias; Yin, Hongzhi; Zheng, Bolong; Nguyen, Quang Huy; Nguyen, Quoc Viet Hung

doi:10.1145/3397271.3401408

Cited by 12 publications

(5 citation statements)

References 12 publications

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“…To fill the gap, we design a framework that SME marketers can use for detecting and understanding fake reviews. To facilitate online review data analytics, the framework achieves cost-effectiveness by enabling the pay-as-you-go analytic schema (Hung et al, 2019;Nguyen et al, 2020) and can be handled by non-data specialists. Using a case study, we answer the research questions relating to the differences between fake and organic reviews and investigate their differences in multi-perspectives.…”

Section: Discussionmentioning

confidence: 99%

Towards a Review-Analytics-as-a-Service (RAaaS) Framework for SMEs: A Case Study on Review Fraud Detection and Understanding

Chau

Nguyen

Tran³

et al. 2023

Australasian Marketing Journal

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With the advancement of internet technology, customers increasingly rely on online reviews as a valuable source of information. The study aims to develop a marketing data analytics framework to manage online reviews, especially fake reviews, which have become a significant issue undermining the creditability of online review systems. As small and medium-sized enterprises often lack the capabilities to automatically derive customer insights from online reviews, this study proposes a cost-effective, extensible Review-Analytics-as-a-Service (RAaaS) framework that can be operated by non-data specialists to facilitate online review data analytics. We demonstrate the framework’s application by using two datasets with more than 400,000 online reviews from Yelp to simulate live platforms and demonstrate an analytic flow of review fraud detection and understanding. The findings reveal insights into the influence of fake reviews on product ranking and exposure rate. Moreover, it was found that there was a higher concentration of sadness and anger in fake reviews (vs. organic reviews). In addition, fake reviews tend to be shorter, more extreme (with the use of strong adverbs), and have different patterns of topic distribution. This study has important implications for different stakeholder groups including, but not limited to, SMEs, review platforms and customers.

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Section: Discussionmentioning

confidence: 99%

Towards a Review-Analytics-as-a-Service (RAaaS) Framework for SMEs: A Case Study on Review Fraud Detection and Understanding

Chau

Nguyen

Tran³

et al. 2023

Australasian Marketing Journal

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“…Also, we would want to address the cold start or less active users issues, which is a common challenge in LBSN recommendation and social network mining [2], [62], [63]. We also want to incorporate user feedback mechanisms to improve the system performance [64]- [67]. Tailor the approach to streaming data is also an interesting extension [68], [69].…”

Section: Discussionmentioning

confidence: 99%

Learning Holistic Interactions in LBSNs with High-order, Dynamic, and Multi-role Contexts

Huynh

Tong

Nguyên

et al. 2022

IEEE Trans. Knowl. Data Eng.

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Location-based social networks (LBSNs) have emerged over the past few years. Their exponential network effects depend on the fact that each user can share her daily digital footprints with different communities, in different places, and at different times (for example in the form of check-in activities). Unlike other types of social networks, activities in an LBSN can potentially be performed by several users in a collaborative way. Existing studies of representation learning for LBSNs often consider them as regular graphs and ignore these high-order, dynamic, and multi-role contexts, since their holistic interactions are quite difficult to capture. In this paper, we propose a model in which these holistic interactions can be learned and transferred into node embeddings derived from a hypergraph representation and a persona decomposition process. More specifically, the model learns from friendship edges, check-in hyperedges, and node personas at the same time, and devises multiple presentations for each user that reflects their multiple roles in a social context. The embedding learning process also exploits useful patterns such as user colocation and sequential effects through a carefully designed point-of-interest splitting step. Extensive experiments on real and synthetic datasets show that our model outperforms alternative state-of-the-art embedding methods on friendship and location prediction tasks by an average margin of 45.7% and 29.46%, respectively. We also demonstrate the robustness of our model against adversarial conditions such as structural noise, attribute noise, and hyperparameter sensitivity.

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“…A follow-up study provides an interactive HITL tool for fact-checking (Nguyen et al, 2018a). Nguyen, Weidlich, Yin, Zheng, Nguyen and Nguyen (2020) propose a HITL system to minimise user effort and cost. Users validate algorithmic predictions but do so at a minimal cost by only validating the most-beneficial predictions for improving the system.…”

Section: Human-in-the-loop Approachesmentioning

confidence: 99%

The State of Human-centered NLP Technology for Fact-checking

Das,

Liu,

Kovatchev

et al. 2023

Preprint

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Misinformation threatens modern society by promoting distrust in science, changing narratives in public health, heightening social polarization, and disrupting democratic elections and financial markets, among a myriad of other societal harms. To address this, a growing cadre of professional fact-checkers and journalists provide high-quality investigations into purported facts. However, these largely manual efforts have struggled to match the enormous scale of the problem. In response, a growing body of Natural Language Processing (NLP) technologies have been proposed for more scalable fact-checking. Despite tremendous growth in such research, however, practical adoption of NLP technologies for fact-checking still remains in its infancy today.In this work, we review the capabilities and limitations of the current NLP technologies for fact-checking. Our particular focus is to further chart the design space for how these technologies can be harnessed and refined in order to better meet the needs of human fact-checkers. To do so, we review key aspects of NLP-based fact-checking: task formulation, dataset construction, modeling, and human-centered strategies, such as explainable models and human-in-the-loop approaches. Next, we review the efficacy of applying NLP-based fact-checking tools to assist human fact-checkers. We recommend that future research include collaboration with fact-checker stakeholders early on in NLP research, as well as incorporation of human-centered design practices in model development, in order to further guide technology development for human use and practical adoption. Finally, we advocate for more research on benchmark development supporting extrinsic evaluation of human-centered fact-checking technologies.

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FactCatch: Incremental Pay-as-You-Go Fact Checking with Minimal User Effort

Cited by 12 publications

References 12 publications

Towards a Review-Analytics-as-a-Service (RAaaS) Framework for SMEs: A Case Study on Review Fraud Detection and Understanding

Towards a Review-Analytics-as-a-Service (RAaaS) Framework for SMEs: A Case Study on Review Fraud Detection and Understanding

Learning Holistic Interactions in LBSNs with High-order, Dynamic, and Multi-role Contexts

The State of Human-centered NLP Technology for Fact-checking

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