  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.
