Zero-Shot Rumor Detection with Propagation Structure via Prompt Learning

The spread of rumors along with breaking events seriously hinders the truth in the era of social media. Previous studies reveal that due to the lack of annotated resources, rumors presented in minority languages are hard to be detected. Furthermore, the unforeseen breaking events not involved in yesterday's news exacerbate the scarcity of data resources. In this work, we propose a novel zero-shot framework based on prompt learning to detect rumors falling in different domains or presented in different languages. More specifically, we firstly represent rumor circulated on social media as diverse propagation threads, then design a hierarchical prompt encoding mechanism to learn language-agnostic contextual representations for both prompts and rumor data. To further enhance domain adaptation, we model the domain-invariant structural features from the propagation threads, to incorporate structural position representations of influential community response. In addition, a new virtual response augmentation method is used to improve model training. Extensive experiments conducted on three real-world datasets demonstrate that our proposed model achieves much better performance than state-of-the-art methods and exhibits a superior capacity for detecting rumors at early stages.Comment: AAAI 202

An early guidance system for a general knowledge-based aiding framework using probabilistic interventions

International audienceA common decision problem repeats a lot of time with the same kind of alternatives and the same set of criteria, but with a different decision case in each occurrence. The objective of early guidance in this kind of problem is to facilitate the selection of a subset of satisfactory alternatives for each new decision case, without asking the user any knowledge of the problem. This article proposes an early guidance system based on a model of knowledge of the common decision problem. It first presents the construction of a Bayesian network for a common decision problem to embed the knowledge in the aiding framework. Second, the concept of intervention proposed by Pearl is extended to prob-abilistic interventions for a single variable and for a set of variables. Finally the early guidance procedure is presented on the basis of the Bayesian network and using a proba-bilistic intervention to set a decision case even though it is partially observed.Un problème de décision courant se répète de nom-breuses fois, avec le même type d'alternatives et le même ensemble de critères, mais avec une situation de décision différente à chaque occurence du problème. Dans ce type de problème, le conseil en amont vise à faciliter la sélec-tion d'un sous ensemble d'alternatives satisfaisantes pour le cas de décision considéré, sans demander à l'utilisateur d'avoir des connaissances sur le problème. Cet article propose un système de conseil en amont basé sur un modèle de connaissances du problème de décision courant. Pour commencer, l'article présente la construction d'un réseau bayésien pour embarquer la connaissance dans le système. Ensuite, le concept d'intervention dans un réseau bayésien proposé par Pearl est étendu aux interventions probabilistes pour des variables simples et des ensembles de variables. Enfin, la procédure de conseil en amont pour un problème de décision courant est présentée, sur la base du modèle de connaissance et en utilisant les interventions probabilistes pour fixer l'écosystème de la personne, même lorsque le cas de décision n'est que partiellement observé

How to model mutually exclusive events based on independent causal pathways in Bayesian network models

This is supported by ERC project ERC-2013-AdG339182-BAYES_KNOWLEDGE

From the Unexpected to the Unbelievable: Thetics, Miratives and Exclamatives in Conceptual Space

This study investigates the relationship between three linguistic functions: thetics, miratives and exclamatives. Thetics are an information structure configuration that conveys that the information is new to the addressee. The thetic subtypes selected for this study are the following: existentials (e.g. There are apples in the kitchen); presentatives (e.g. Heres your book); weather statements (e.g. It rains); physical sensation statements (e.g. My HEAD hurts) and hot news (e.g. MIchael JACKson died). Thetics do not perform a predication but present the state of affairs as a whole. Crosslinguistically, they tend to use morphosyntactic strategies that distinguish them from prototypical predications. Similar morphosyntactic strategies can also be found in miratives and exclamatives. Miratives are defined as grammatical markers that convey that the information is suprising for the speaker, whereas exclamatives are defined as a sentence type that conveys surprise with respect to a scalar extent that has surpassed the current expectations (e.g. How beautiful you are!). I hypothesize that the structural similarities between these functions are motivated by semantic resemblance. The structural features of these functions are compared in a sample of 76 languages, from which 360 constructions were extracted. Multidimensional scaling was used in order to construct a spatial representation of the degree of similarity/dissimilarity of the constructions. The resulting spatial map shows a dimension motivated by a semantic distinction between event-central and entity-central statements. It also shows a second dimension motivated by the following distinctions: 1) an existential domain, 2) a presentational domain, 3) a mirative domain, and 4) an exclamative domain. Several case studies illustrating the relationships between the functions are presented. It is also demonstrated that miratives can establish a distinction between unexpected and misexpected events. As for exclamatives, it is shown that they are related to linguistic hedges that convey the degree of membership of an item into a category. Several neurobiological and psychological correlates are proposed: thetics correspond to two types of awareness, whereas miratives and exclamatives are related to different stages of a cognitive-evolutionary model of surprise

Evolving Clustering Algorithms And Their Application For Condition Monitoring, Diagnostics, & Prognostics

Applications of Condition-Based Maintenance (CBM) technology requires effective yet generic data driven methods capable of carrying out diagnostics and prognostics tasks without detailed domain knowledge and human intervention. Improved system availability, operational safety, and enhanced logistics and supply chain performance could be achieved, with the widespread deployment of CBM, at a lower cost level. This dissertation focuses on the development of a Mutual Information based Recursive Gustafson-Kessel-Like (MIRGKL) clustering algorithm which operates recursively to identify underlying model structure and parameters from stream type data. Inspired by the Evolving Gustafson-Kessel-like Clustering (eGKL) algorithm, we applied the notion of mutual information to the well-known Mahalanobis distance as the governing similarity measure throughout. This is also a special case of the Kullback-Leibler (KL) Divergence where between-cluster shape information (governed by the determinant and trace of the covariance matrix) is omitted and is only applicable in the case of normally distributed data. In the cluster assignment and consolidation process, we proposed the use of the Chi-square statistic with the provision of having different probability thresholds. Due to the symmetry and boundedness property brought in by the mutual information formulation, we have shown with real-world data that the algorithm’s performance becomes less sensitive to the same range of probability thresholds which makes system tuning a simpler task in practice. As a result, improvement demonstrated by the proposed algorithm has implications in improving generic data driven methods for diagnostics, prognostics, generic function approximations and knowledge extractions for stream type of data. The work in this dissertation demonstrates MIRGKL’s effectiveness in clustering and knowledge representation and shows promising results in diagnostics and prognostics applications