Is Less Really More? Fake News Detection with Limited Information

The threat that online fake news and misinformation pose to democracy, justice, public confidence, and especially to vulnerable populations, has led to a sharp increase in the need for fake news detection and intervention. Whether multi-modal or pure text-based, most fake news detection methods depend on textual analysis of entire articles. However, these fake news detection methods come with certain limitations. For instance, fake news detection methods that rely on full text can be computationally inefficient, demand large amounts of training data to achieve competitive accuracy, and may lack robustness across different datasets. This is because fake news datasets have strong variations in terms of the level and types of information they provide; where some can include large paragraphs of text with images and metadata, others can be a few short sentences. Perhaps if one could only use minimal information to detect fake news, fake news detection methods could become more robust and resilient to the lack of information. We aim to overcome these limitations by detecting fake news using systematically selected, limited information that is both effective and capable of delivering robust, promising performance. We propose a framework called SLIM Systematically-selected Limited Information) for fake news detection. In SLIM, we quantify the amount of information by introducing information-theoretic measures. SLIM leverages limited information to achieve performance in fake news detection comparable to that of state-of-the-art obtained using the full text. Furthermore, by combining various types of limited information, SLIM can perform even better while significantly reducing the quantity of information required for training compared to state-of-the-art language model-based fake news detection techniques.

PiRL: Participant-Invariant Representation Learning for Healthcare Using Maximum Mean Discrepancy and Triplet Loss

Due to individual heterogeneity, person-specific models are usually achieving better performance than generic (one-size-fits-all) models in data-driven health applications. However, generic models are usually preferable in real-world applications, due to the difficulties of developing person-specific models, such as new-user-adaptation issues and system complexities. To improve the performance of generic models, we propose a Participant-invariant Representation Learning (PiRL) framework, which utilizes maximum mean discrepancy (MMD) loss and domain-adversarial training to encourage the model to learn participant-invariant representations. Further, to avoid trivial solutions in the learned representations, a triplet loss based constraint is used for the model to learn the label-distinguishable embeddings. The proposed framework is evaluated on two public datasets (CLAS and Apnea-ECG), and significant performance improvements are achieved compared to the baseline models.

PiRL: Participant-Invariant Representation Learning for Healthcare

Due to individual heterogeneity, performance gaps are observed between generic (one-size-fits-all) models and person-specific models in data-driven health applications. However, in real-world applications, generic models are usually more favorable due to new-user-adaptation issues and system complexities, etc. To improve the performance of the generic model, we propose a representation learning framework that learns participant-invariant representations, named PiRL. The proposed framework utilizes maximum mean discrepancy (MMD) loss and domain-adversarial training to encourage the model to learn participant-invariant representations. Further, a triplet loss, which constrains the model for inter-class alignment of the representations, is utilized to optimize the learned representations for downstream health applications. We evaluated our frameworks on two public datasets related to physical and mental health, for detecting sleep apnea and stress, respectively. As preliminary results, we found the proposed approach shows around a 5% increase in accuracy compared to the baseline.