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Vera: Prediction Techniques for Reducing Harmful Misinformation in Consumer Health Search

Published: 11 July 2021 Publication History

Abstract

The COVID-19 pandemic has brought about a proliferation of harmful news articles online, with sources lacking credibility and misrepresenting scientific facts. Misinformation has real consequences for consumer health search, i.e., users searching for health information. In the context of multi-stage ranking architectures, there has been little work exploring whether they prioritize correct and credible information over misinformation. We find that, indeed, training models on standard relevance ranking datasets like MS MARCO passage---which have been curated to contain mostly credible information---yields models that might also promote harmful misinformation. To rectify this, we propose a label prediction technique that can separate helpful from harmful content. Our design leverages pretrained sequence-to-sequence transformer models for both relevance ranking and label prediction. Evaluated at the TREC 2020 Health Misinformation Track, our techniques represent the top-ranked system: Our best submitted run was 19.2 points higher than the second-best run based on the primary metric, a 68% relative improvement. Additional post-hoc experiments show that we can boost effectiveness by another 3.5 points.

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Presentation Video for "Vera: Prediction Techniques for Reducing Harmful Misinformation in Consumer Health Search"

References

[1]
Mustafa Abualsaud, Christina Lioma, Maria Maistro, Mark D. Smucker, and Guido Zuccon. 2019. Overview of the TREC 2019 Decision Track. In Proceedings of the Twenty-Eigth Text REtrieval Conference (TREC 2019).
[2]
Zeynep Akkalyoncu Yilmaz, Charles L. A. Clarke, and Jimmy Lin. 2020. A Lightweight Environment for Learning Experimental IR Research Practices. In Proceedings of the 43rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2020). 2113--2116.
[3]
Nima Asadi and Jimmy Lin. 2013. Effectiveness/Efficiency Tradeoffs for Candidate Generation in Multi-Stage Retrieval Architectures. In Proceedings of the 36th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2013). Dublin, Ireland, 997--1000.
[4]
Charles L.A. Clarke, Maria Maistro, and Mark D. Smucker. 2020 a. Overview of the TREC 2020 Health Misinformation Track (Notebook). In Proceedings of the Twenty-Ninth Text REtrieval Conference (TREC 2020).
[5]
Charles L.A. Clarke, Alexandra Vtyurina, and Mark D. Smucker. 2020 c. Offline Evaluation without Gain. In Proceedings of the 2020 ACM SIGIR International Conference on Theory of Information Retrieval (ICTIR '20). 185--192.
[6]
Charles L. A. Clarke, Mark D. Smucker, and Alexandra Vtyurina. 2020 b. Offline Evaluation by Maximum Similarity to an Ideal Ranking. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management (CIKM '20). 225--234.
[7]
Zhuyun Dai and Jamie Callan. 2019. Deeper Text Understanding for IR with Contextual Neural Language Modeling. In Proceedings of the 42nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2019). Paris, France, 985--988.
[8]
Andreas Hanselowski, Christian Stab, Claudia Schulz, Zile Li, and Iryna Gurevych. 2019. A Richly Annotated Corpus for Different Tasks in Automated Fact-Checking. In Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Hong Kong, China, 493--503.
[9]
Nayeon Lee, Yejin Bang, Andrea Madotto, and Pascale Fung. 2020. Misinformation has High Perplexity. arXiv:2006.04666 (2020).
[10]
Jimmy Lin, Xueguang Ma, Sheng-Chieh Lin, Jheng-Hong Yang, Ronak Pradeep, and Rodrigo Nogueira. 2021. Pyserini: A Python Toolkit for Reproducible Information Retrieval Research with Sparse and Dense Representations. In Proceedings of the 44th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2021).
[11]
Jimmy Lin, Rodrigo Nogueira, and Andrew Yates. 2020. Pretrained Transformers for Text Ranking: BERT and Beyond. arXiv:2010.06467 (2020).
[12]
Sean MacAvaney, Arman Cohan, and Nazli Goharian. 2020. SLEDGE: A Simple Yet Effective Zero-Shot Baseline for Coronavirus Scientific Knowledge Search. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). 4171--4179.
[13]
Irina Matveeva, Chris Burges, Timo Burkard, Andy Laucius, and Leon Wong. 2006. High Accuracy Retrieval with Multiple Nested Ranker. In Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2006). Seattle, Washington, 437--444.
[14]
Rodrigo Nogueira, Zhiying Jiang, Ronak Pradeep, and Jimmy Lin. 2020. Document Ranking with a Pretrained Sequence-to-Sequence Model. In Findings of EMNLP.
[15]
Ronak Pradeep, Xueguang Ma, Rodrigo Nogueira, and Jimmy Lin. 2021 a. Scientific Claim Verification with VerT5erini. In Proceedings of the 12th International Workshop on Health Text Mining and Information Analysis. 94--103.
[16]
Ronak Pradeep, Rodrigo Nogueira, and Jimmy Lin. 2021 b. The Expando-Mono-Duo Design Pattern for Text Ranking with Pretrained Sequence-to-Sequence Models. arXiv:2101.05667 (2021).
[17]
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2020. Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer. Journal of Machine Learning Research, Vol. 21, 140 (2020), 1--67.
[18]
Stephen E. Robertson, Steve Walker, Susan Jones, Micheline Hancock-Beaulieu, and Mike Gatford. 1994. Okapi at TREC-3. In Proceedings of the 3rd Text REtrieval Conference (TREC-3). 109--126.
[19]
James Thorne, Andreas Vlachos, Christos Christodoulopoulos, and Arpit Mittal. 2018. FEVER: A Large-scale Dataset for Fact Extraction and VERification. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). New Orleans, Louisiana, 809--819.
[20]
David Wadden, Shanchuan Lin, Kyle Lo, Lucy Lu Wang, Madeleine van Zuylen, Arman Cohan, and Hannaneh Hajishirzi. 2020. Fact or Fiction: Verifying Scientific Claims. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). 7534--7550.
[21]
Lidan Wang, Jimmy Lin, and Donald Metzler. 2011. A Cascade Ranking Model for Efficient Ranked Retrieval. In Proceedings of the 34th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2011). Beijing, China, 105--114.
[22]
Lucy Lu Wang, Kyle Lo, Yoganand Chandrasekhar, Russell Reas, Jiangjiang Yang, Doug Burdick, Darrin Eide, Kathryn Funk, Yannis Katsis, Rodney Kinney, Yunyao Li, Ziyang Liu, William Merrill, Paul Mooney, Dewey Murdick, Devvret Rishi, Jerry Sheehan, Zhihong Shen, Brandon Stilson, Alex Wade, Kuansan Wang, Nancy Xin Ru Wang, Chris Wilhelm, Boya Xie, Douglas Raymond, Daniel S. Weld, Oren Etzioni, and Sebastian Kohlmeier. 2020. CORD-19: The COVID-19 Open Research Dataset. arxiv: 2004.10706 [cs.DL]
[23]
Adina Williams, Nikita Nangia, and Samuel Bowman. 2018. A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). New Orleans, Louisiana, 1112--1122.
[24]
Peilin Yang, Hui Fang, and Jimmy Lin. 2017. Anserini: Enabling the Use of Lucene for Information Retrieval Research. In Proceedings of the 40th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2017). Tokyo, Japan, 1253--1256.
[25]
Peilin Yang, Hui Fang, and Jimmy Lin. 2018. Anserini: Reproducible Ranking Baselines Using Lucene. Journal of Data and Information Quality, Vol. 10, 4 (2018), Article 16.
[26]
Xinyu Zhang, Andrew Yates, and Jimmy Lin. 2021. Comparing Score Aggregation Approaches for Document Retrieval with Pretrained Transformers. In Proceedings of the 43rd European Conference on Information Retrieval (ECIR 2021), Part II. 150--163.

Cited By

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  • (2024)Online Health Search Via Multidimensional Information Quality Assessment Based on Deep Language Models: Algorithm Development and ValidationJMIR AI10.2196/426303(e42630)Online publication date: 2-May-2024
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  • (2024)A Flexible Big Data System for Credibility-Based Filtering of Social Media Information According to ExpertiseInternational Journal of Computational Intelligence Systems10.1007/s44196-024-00483-y17:1Online publication date: 15-Apr-2024
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    cover image ACM Conferences
    SIGIR '21: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval
    July 2021
    2998 pages
    ISBN:9781450380379
    DOI:10.1145/3404835
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Published: 11 July 2021

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    1. multi-stage ranking
    2. sequence-to-sequence models

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    Cited By

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    • (2024)Online Health Search Via Multidimensional Information Quality Assessment Based on Deep Language Models: Algorithm Development and ValidationJMIR AI10.2196/426303(e42630)Online publication date: 2-May-2024
    • (2024)Everything We Hear: Towards Tackling Misinformation in PodcastsProceedings of the 26th International Conference on Multimodal Interaction10.1145/3678957.3678959(596-601)Online publication date: 4-Nov-2024
    • (2024)A Flexible Big Data System for Credibility-Based Filtering of Social Media Information According to ExpertiseInternational Journal of Computational Intelligence Systems10.1007/s44196-024-00483-y17:1Online publication date: 15-Apr-2024
    • (2023)Robust Benchmark for Propagandist Text Detection and Mining High-Quality DataMathematics10.3390/math1112266811:12(2668)Online publication date: 12-Jun-2023
    • (2023)Read it Twice: Towards Faithfully Interpretable Fact Verification by Revisiting EvidenceProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3592049(2319-2323)Online publication date: 19-Jul-2023
    • (2022)Crowdsourced Fact-Checking at TwitterProceedings of the 31st ACM International Conference on Information & Knowledge Management10.1145/3511808.3557279(1736-1746)Online publication date: 17-Oct-2022
    • (2022)Neural Query Synthesis and Domain-Specific Ranking Templates for Multi-Stage Clinical Trial MatchingProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3531853(2325-2330)Online publication date: 6-Jul-2022
    • (2022)Learning Trustworthy Web Sources to Derive Correct Answers and Reduce Health Misinformation in SearchProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3531812(2099-2104)Online publication date: 6-Jul-2022
    • (2022)An Unsupervised Approach to Genuine Health Information Retrieval Based on Scientific EvidenceWeb Information Systems Engineering – WISE 202210.1007/978-3-031-20891-1_10(119-135)Online publication date: 31-Oct-2022
    • (2022)Squeezing Water from a Stone: A Bag of Tricks for Further Improving Cross-Encoder Effectiveness for RerankingAdvances in Information Retrieval10.1007/978-3-030-99736-6_44(655-670)Online publication date: 10-Apr-2022
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