The rise of misinformation online and offline reveals the erosion of long-standing institutional bulwarks against its propagation in the digitized era. Concerns over the problem are global and the impact is long-lasting. The past few decades have witnessed the critical role of misinformation detection in enhancing public trust and social stability. However, it remains a challenging problem for the Natural Language Processing community. This paper discusses the main issues of misinformation and its detection with a comprehensive review on representative works in terms of detection methods, feature representations, evaluation metrics and reference datasets. Advantages and disadvantages of the key techniques are also addressed with focuses on content-based analysis and predicative modeling. Alternative solutions to anti-misinformation imply a trend of hybrid multi-modal representation, multi-source data and multi-facet inference, e.g., leveraging the language complexity. In spite of decades' efforts, the dynamic and evolving nature of misrepresented information across different domains, languages, cultures and time spans determines the openness and uncertainty of this restless adventure in the future.

Metaphor as a cognitive mechanism in human's conceptual system manifests itself an effective way for language communication. Although being intuitively sensible for human, metaphor detection is still a challenging task due to the subtle ontological differences between metaphorical and non-metaphorical expressions. This work proposes a modality enriched deep learning model for tackling this unsolved issue. It provides a new perspective for understanding metaphor as a modality shift, as in 'sweet voice'. It also attempts to enhance metaphor detection by combining deep learning with effective linguistic insight. Extending the work at Wan et al. (2020), we concatenate word sensorimotor scores (Lynott et al., 2019) with word vectors as the input of attention-based Bi-LSTM using a benchmark dataset-the VUA corpus. The experimental results show great F1 improvement (above 0.5%) of the proposed model over other methods in record, demonstrating the usefulness of leveraging modality norms for metaphor detection.

Torque-free ring spinning is a new spinning technology that has produced yarns with low twist and balanced torque. In this study, a commercially torque-free ring-spun yarn, namely Estex yarn, with three types of cotton fibre, i.e. Pima, upland and organic cotton, were used. Cotton fabric samples were knitted with Estex yarns and conventional ring-spun yarns. The fabric samples were then dyed with two reactive dyes, Remazol Black B and Remazol Brilliant Blue R Spec., and the fabric dyeability was measured in terms of reflectance and colour yield. Finally, the results were analysed using the statistical software package SPSS and the results revealed that fabric samples manufactured by Estex yarns could achieve a better colour yield than conventional ring-spun fabric samples. In addition, the Pima cotton gave the best colour yield, followed by upland cotton and organic cotton.

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This paper reports a linguistically-enriched method of detecting token-level metaphors for the second shared task on Metaphor Detection. We participate in all four phases of competition with both datasets, i.e. Verbs and All-POS on the VUA and the TOFEL datasets. We use the modality exclusivity and embodiment norms for constructing a conceptual representation of the nodes and the context. Our system obtains an F-score of 0.652 for the VUA Verbs track, which is 5% higher than the strong baselines. The experimental results across models and datasets indicate the salient contribution of using modality exclusivity and modality shift information for predicting metaphoricity.