Reconstructing Diffusion Model for Virality Detection in News Spread Networks

Jain, Kritika; Garg, Ankit; Jain, Somya

doi:10.4018/978-1-7998-7291-7.ch006

Cited by 2 publications

(1 citation statement)

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“…Furthermore, some tasks can be formulated independent of the fact-checking pipeline and utilized later to improve individual fact-checking sub-tasks. For example, predicting the virality of social media content (Jain, Garg and Jain, 2021) can help improve claim detection and claim checkworthiness. Similarly, network analysis on fake news propagation (Shao, Ciampaglia, Flammini and Menczer, 2016) can help in analyzing provenance.…”

Section: Related Tasksmentioning

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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Section: Related Tasksmentioning

confidence: 99%