Exploring Bias in Data and Models for Misinformation Detection from Text

Research output: Contribution to conferenceAbstractAcademic

Abstract

With the proliferation of misinformation on the web, automatic misinformation detection methods are becoming an increasingly important subject of study. Large language models have produced the best results among content-based methods, which rely on the text of the article rather than the metadata or network features. However, finetuning such a model requires significant training data, which has led to the automatic creation of large-scale misinformation detection datasets. In these datasets, articles are not labelled directly. Rather, each news site is labelled for reliability by an established fact-checking organisation and every article is subsequently assigned the corresponding label based on the reliability score of the news source in question. A recent paper has explored the biases present in one such dataset, NELA-GT-2018, and shown that the models are at least partly learning the stylistic and other features of different news sources rather than the features of unreliable news. We confirm a part of their findings. Apart from studying the characteristics and potential biases of the datasets, we also find it important to examine in what way the model architecture influences the results. We therefore explore which text features or combinations of features are learned by models based on contextual word embeddings as opposed to basic bag-of-words models. To elucidate this, we perform extensive error analysis aided by the SHAP post-hoc explanation technique on a debiased portion of the dataset. We validate the explanation technique on our inherently interpretable baseline model.
Original languageEnglish
Publication statusPublished - 7 Apr 2022
EventICT.OPEN 2022 - RAI Amsterdam, Amsterdam, Netherlands
Duration: 6 Apr 20227 Apr 2022
https://www.ictopen.nl/

Conference

ConferenceICT.OPEN 2022
Abbreviated titleICT.OPEN2022
Country/TerritoryNetherlands
CityAmsterdam
Period6/04/227/04/22
Internet address

Fingerprint

Dive into the research topics of 'Exploring Bias in Data and Models for Misinformation Detection from Text'. Together they form a unique fingerprint.

Cite this