DPOD: Domain-Specific Prompt Tuning for Multimodal Fake News Detection

The spread of fake news using out-of-context images has become widespread and is a relevant problem in this era of information overload. Such out-of-context fake news may arise across different domains like politics, sports, entertainment, etc. In practical scenarios, an inherent problem of imbalance exists among news articles from such widely varying domains, resulting in a few domains with abundant data, while the rest containing very limited data. Under such circumstances, it is imperative to develop methods which can work in such varying amounts of data setting. In this work, we explore whether out-of-domain data can help to improve out-of-context misinformation detection (termed here as multi-modal fake news detection) of a desired domain, to address this challenging problem. Towards this goal, we propose a novel framework termed DPOD (Domain-specific Prompt-tuning using Out-of-Domain data). First, to compute generalizable features, we modify the Vision-Language Model, CLIP to extract features that helps to align the representations of the images and corresponding text captions of both the in-domain and out-of-domain data in a label-aware manner. Further, we propose a domain-specific prompt learning technique which leverages the training samples of all the available domains based on the extent they can be useful to the desired domain. Extensive experiments on a large-scale benchmark dataset, namely NewsCLIPpings demonstrate that the proposed framework achieves state of-the-art performance, significantly surpassing the existing approaches for this challenging task. Code will be released on acceptance.

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