Fake news detection: a survey of evaluation datasets

Fake news detection has gained increasing importance among the research community due to the widespread diffusion of fake news through media platforms. Many dataset have been released in the last few years, aiming to assess the performance of fake news detection methods. In this survey, we systematically review twenty-seven popular datasets for fake news detection by providing insights into the characteristics of each dataset and comparative analysis among them. A fake news detection datasets characterization composed of eleven characteristics extracted from the surveyed datasets is provided, along with a set of requirements for comparing and building new datasets. Due to the ongoing interest in this research topic, the results of the analysis are valuable to many researchers to guide the selection or definition of suitable datasets for evaluating their fake news detection methods

Dealing with multimodal languages ambiguities : a classification and solution method

Starting from discussing the problem of ambiguity and its pervasiveness on communication processes, this thesis dissertation faces problems of classifying and solving ambiguities for multimodal languages. This thesis gives an overview of the works proposed in literature about ambiguities in natural language and visual languages and discusses some existing proposals on multimodal ambiguities. An original classification of multimodal ambiguities has been defined using a linguistic perspective, introducing the notions of multimodal grammar, multimodal sentence and multimodal language. An overview of methods that the literature proposes for avoiding and detecting ambiguities has been done. These methods are grouped into: prevention of ambiguities, a-posterior resolution and approximation resolution methods. The analysis of these methods has underlined the suitability of Hidden Markov Models (HMMs) for disambiguation processes. However, due to the complexity of ambiguities for multimodal interaction, this thesis uses the Hierarchical Hidden Markov Models to manage the semantic and syntactic classes of ambiguities for multimodal sentences; this choice permits to operate at different levels going from the terminal elements to the multimodal sentence. The proposed methods for classifying and solving multimodal ambiguities have been used to design and implement two software modules. The experimental results of these modules have underlined a good level of accuracy during the classification and solution processes of multimodal ambiguities

Solving Ambiguities for Sketch-Based Interaction in Mobile Environments

The diffusion of mobile devices and the development of their services and applications are connected with the possibility to communicate anytime and anywhere according to a natural approach, which combines different modalities (speech, sketch, etc.). A natural communication approach, such as sketch-based interaction, frequently produces ambiguities. Ambiguities can arise in sketch recognition process by the gap between the user’s intention and the system interpretation. This paper presents a classification of meaningful ambiguities in sketchbased interaction and discusses methods to solve them taking into account of the spatial and temporal information that characterise the drawing process. The proposed solution methods use both sketch-based approaches and/or integrated approaches with other modalities. They are classified in: prevention, aposteriori and approximation methods

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Ambiguities in Sketch-Based Interfaces

Sketch-based interaction is an intuitive, simple communication method. However, it has several critical aspects, due to difficulties during the interpretation step by the computer side. These mainly derive from the semantic gap between the user's communicative intention and how he/she is able to convey it. The interpretation must also consider the deletion and over-tracing actions. This introduces further ambiguities that make it difficult to interpret the user's communicative intention. To deal with these problems, this paper provides a classification of ambiguities for sketch-based interfaces, based on experimental observations from a group of users. Ambiguities are caused by inaccuracy, approximation of the represented reality, user's indecision and deletion or retracing of parts of the sketch. This paper presents some useful situations to give an overview on ambiguities by considering information strictly related to the user's behaviour, considering spatial and temporal information in the drawing process and the user's intention. © 2007 IEEE