1,685 research outputs found

    Deep Neural Attention for Misinformation and Deception Detection

    Get PDF
    PhD thesis in Information technologyAt present the influence of social media on society is so much that without it life seems to have no meaning for many. This kind of over-reliance on social media gives an opportunity to the anarchic elements to take undue advantage. Online misinformation and deception are vivid examples of such phenomenon. The misinformation or fake news spreads faster and wider than the true news [32]. The need of the hour is to identify and curb the spread of misinformation and misleading content automatically at the earliest. Several machine learning models have been proposed by the researchers to detect and prevent misinformation and deceptive content. However, these prior works suffer from some limitations: First, they either use feature engineering heavy methods or use intricate deep neural architectures, which are not so transparent in terms of their internal working and decision making. Second, they do not incorporate and learn the available auxiliary and latent cues and patterns, which can be very useful in forming the adequate context for the misinformation. Third, Most of the former methods perform poorly in early detection accuracy measures because of their reliance on features that are usually absent at the initial stage of news or social media posts on social networks. In this dissertation, we propose suitable deep neural attention based solutions to overcome these limitations. For instance, we propose a claim verification model, which learns embddings for the latent aspects such as author and subject of the claim and domain of the external evidence document. This enables the model to learn important additional context other than the textual content. In addition, we also propose an algorithm to extract evidential snippets out of external evidence documents, which serves as explanation of the model’s decisions. Next, we improve this model by using improved claim driven attention mechanism and also generate a topically diverse and non-redundant multi-document fact-checking summary for the claims, which helps to further interpret the model’s decision making. Subsequently, we introduce a novel method to learn influence and affinity relationships among the social media users present on the propagation paths of the news items. By modeling the complex influence relationship among the users, in addition to textual content, we learn the significant patterns pertaining to the diffusion of the news item on social network. The evaluation shows that the proposed model outperforms the other related methods in early detection performance with significant gains. Next, we propose a synthetic headline generation based headline incongruence detection model. Which uses a word-to-word mutual attention based deep semantic matching between original and synthetic news headline to detect incongruence. Further, we investigate and define a new task of incongruence detection in presence of important cardinal values in headline. For this new task, we propose a part-of-speech pattern driven attention based method, which learns requisite context for cardinal values

    QUT Research Graduates Yearbook, 2019

    Get PDF

    Deep Learning para BigData

    Get PDF
    We live in a world where data is becoming increasingly valuable and increasingly abundant in volume. Every company produces data, be it from sales, sensors, and various other sources. Since the dawn of the smartphone, virtually every person in the world is connected to the internet and contributes to data generation. Social networks are big contributors to this Big Data boom. How do we extract insight from such a rich data environment? Is Deep Learning capable of circumventing Big Data’s challenges? This is what we intend to understand. To reach a conclusion, Social Network data is used as a case study for predicting sentiment changes in the Stock Market. The objective of this dissertation is to develop a computational study and analyse its performance. The outputs will contribute to understand Deep Learning’s usage with Big Data and how it acts in Sentiment analysis.Vivemos num mundo onde dados são cada vez mais valiosos e abundantes. Todas as empresas produzem dados, sejam eles provenientes de valores de vendas, parâmetros de sensores bem como de outras diversas fontes. Desde que os smartphones se tornaram pessoais, o mundo tornou-se mais conectado, já que virtualmente todas as pessoas passaram a ter a internet na ponta dos dedos. Esta explosão tecnológica foi acompanhada por uma explosão de dados. As redes sociais têm um grande contributo para a quantidade de dados produzida. Mas como se analisam estes dados? Será que Deep Learning poderá dar a volta aos desafios que Big Data traz inerentemente? É isso se pretende perceber. Para chegar a uma conclusão, foi utilizado um caso de estudo de redes sociais para previsão de alterações nas ações de mercados financeiros relacionadas com as opiniões dos utilizadores destas. O objetivo desta dissertação é o desenvolvimento de um estudo computacional e a análise da sua performance. Os resultados contribuirão para entender o uso de Deep Learning com Big Data, com especial foco em análise de sentimento. The objective of this dissertation is to develop a computational study and analyse its performance. The outputs will contribute to understand Deep Learning’s usage with Big Data and how it acts in Sentiment analysis

    Environmental Messages in Boating Magazines: A Content Analysis of Recreational Power Boating Articles

    Get PDF
    This paper presents the results of a study analyzing the editorial content of consumer power boating magazines for messages of environmental responsibility. It presents evidence of a cause and effect relationship between recreational power boating and damage to the marine environment, and cites arguments that power boating in its currently popular form, involving high speeds and heavy fuel consumption, is inherently unfriendly to the environment. The paper describes a chain of influence through which boat manufacturers, in their role as advertisers, influence the editorial content of consumer boating magazines through the tactics of marketing public relations. A hypothesis is developed which states that publishers\u27 commercial interests behoove them to publish editorial material generally supportive of their advertisers, hence promoting consumer behaviors that damage the environment through profligacy of fuel consumption leading to excessive engine emissions. Three magazines are selected for analysis, based on their high levels of influence and visibility within the consumer boating market: Boating, Motor Boating & Sailing, and Power & Motoryacht. These magazines maintain the largest circulation figures of all North American consumer power boating magazines, with a combine monthly circulation totaling more than one half million issues and a substantial pass-along readership. The hypotheses is tested using methods of content analysis. Separate tests are employed to analyze five different types of editorial content: textual content of boat test feature articles; photographic content of boat test feature articles; topical content of how to articles on two specific subjects (boat handling, and maintenance); topical content and slant of opinion columns; and topical content of feature articles explicitly addressing environmental issues. Test results fail to support the hypothesis, suggesting that special-interest consumer publishing in the recreational power boating niche maintains a degree of editorial independence from advertisers, and that environmental topics are treated in a reasonably balanced manner

    Visualizing Evaluative Language in Relation to Constructing Identity in English Editorials and Op-Eds

    Get PDF
    This thesis is concerned with the problem of managing complexity in Systemic Functional Linguistic (SFL) analyses of language, particularly at the discourse semantics level. To deal with this complexity, the thesis develops AppAnn, a suite of linguistic visualization techniques that are specifically designed to provide both synoptic and dynamic views on discourse semantic patterns in text and corpus. Moreover, AppAnn visualizations are illustrated in a series of explorations of identity in a corpus of editorials and op-eds about the bin Laden killing. The findings suggest that the intriguing intricacies of discourse semantic meanings can be successfully discerned and more readily understood through linguistic visualization. The findings also provide insightful implications for discourse analysis by contributing to our understanding of a number of underdeveloped concepts of SFL, including coupling, commitment, instantiation, affiliation and individuation

    A Multidisciplinary Design and Evaluation Framework for Explainable AI Systems

    Get PDF
    Nowadays, algorithms analyze user data and affect the decision-making process for millions of people on matters like employment, insurance and loan rates, and even criminal justice. However, these algorithms that serve critical roles in many industries have their own biases that can result in discrimination and unfair decision-making. Explainable Artificial Intelligence (XAI) systems can be a solution to predictable and accountable AI by explaining AI decision-making processes for end users and therefore increase user awareness and prevent bias and discrimination. The broad spectrum of research on XAI, including designing interpretable models, explainable user interfaces, and human-subject studies of XAI systems are sought in different disciplines such as machine learning, human-computer interactions (HCI), and visual analytics. The mismatch in objectives for the scholars to define, design, and evaluate the concept of XAI may slow down the overall advances of end-to-end XAI systems. My research aims to converge knowledge behind design and evaluation of XAI systems between multiple disciplines to further support key benefits of algorithmic transparency and interpretability. To this end, I propose a comprehensive design and evaluation framework for XAI systems with step-by-step guidelines to pair different design goals with their evaluation methods for iterative system design cycles in multidisciplinary teams. This dissertation presents a comprehensive XAI design and evaluation framework to provide guidance for different design goals and evaluation approaches in XAI systems. After a thorough review of XAI research in the fields of machine learning, visualization, and HCI, I present a categorization of XAI design goals and evaluation methods and show a mapping between design goals for different XAI user groups and their evaluation methods. From my findings, I present a design and evaluation framework for XAI systems (Objective 1) to address the relation between different system design needs. The framework provides recommendations for different goals and ready-to-use tables of evaluation methods for XAI systems. The importance of this framework is in providing guidance for researchers on different aspects of XAI system design in multidisciplinary team efforts. Then, I demonstrate and validate the proposed framework (Objective 2) through one end-to-end XAI system case study and two examples by analysis of previous XAI systems in terms of our framework. I present two contributions to my XAI design and evaluation framework to improve evaluation methods for XAI system
    • …
    corecore