1,299 research outputs found

    Detecting deceptive reviews using argumentation

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    The unstoppable rise of social networks and the web is facing a serious challenge: identifying the truthfulness of online opinions and reviews. In this paper we use Argumentation Frameworks (AFs) extracted from reviews and explore whether the use of these AFs can improve the performance of machine learning techniques in detecting deceptive behaviour, resulting from users lying in order to mislead readers. The AFs represent how arguments from reviews relate to arguments from other reviews as well as to arguments about the goodness of the items being reviewed

    Unleashing the Potential of Argument Mining for IS Research: A Systematic Review and Research Agenda

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    Argument mining (AM) represents the unique use of natural language processing (NLP) techniques to extract arguments from unstructured data automatically. Despite expanding on commonly used NLP techniques, such as sentiment analysis, AM has hardly been applied in information systems (IS) research yet. Consequentially, knowledge about the potentials for the usage of AM on IS use cases appears to be still limited. First, we introduce AM and its current usage in fields beyond IS. To address this research gap, we conducted a systematic literature review on IS literature to identify IS use cases that can potentially be extended with AM. We develop eleven text-based IS research topics that provide structure and context to the use cases and their AM potentials. Finally, we formulate a novel research agenda to guide both researchers and practitioners to design, compare and evaluate the use of AM for text-based applications and research streams in IS

    Mining bipolar argumentation frameworks from natural language text

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    We describe a methodology for mining topic-dependent Bipolar Argumentation Frameworks (BAFs) from natural language text. Our focus is on identifying attack and support argumentative re- lations between texts about the same topic, treating these texts as arguments when they are argumentatively related to other texts. We illustrate our methodology on a dataset of hotel reviews and outline some possible applications using the BAFs resulting from our methodology

    Deception

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    A platform for crowdsourcing corpora for argumentative

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    One problem that Argument Mining (AM) is facing is the difficultyof obtaining suitable annotated corpora. We propose a web-basedplatform, BookSafari, that allows crowdsourcing of annotated cor-pora forrelation-based AMfrom users providing reviews for booksand exchanging opinions about these reviews to facilitate argumen-tative dialogue. The annotations amount to pairwise argumentativerelations ofattackandsupportbetween opinions and between opin-ions and reviews. As a result of the annotations, reviews and opinionsform structured debates which can be understood as bipolar argu-mentation frameworks. The platform also empowers annotationsof the same pairs by multiple annotators and can support differentmeasures of inter-annotator agreement and corpora selection

    Review Manipulation: Literature Review, and Future Research Agenda

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    Background: The phenomenon of review manipulation and fake reviews has gained Information Systems (IS) scholars’ attention during recent years. Scholarly research in this domain has delved into the causes and consequences of review manipulation. However, we find that the findings are diverse, and the studies do not portray a systematic approach. This study synthesizes the findings from a multidisciplinary perspective and presents an integrated framework to understand the mechanism of review manipulation. Method: The study reviews 88 relevant articles on review manipulation spanning a decade and a half. We adopted an iterative coding approach to synthesizing the literature on concepts and categorized them independently into potential themes. Results: We present an integrated framework that shows the linkages between the different themes, namely, the prevalence of manipulation, impact of manipulation, conditions and choice for manipulation decision, characteristics of fake reviews, models for detecting spam reviews, and strategies to deal with manipulation. We also present the characteristics of review manipulation and cover both operational and conceptual issues associated with the research on this topic. Conclusions: Insights from the study will guide future research on review manipulation and fake reviews. The study presents a holistic view of the phenomenon of review manipulation. It informs various online platforms to address fake reviews towards building a healthy and sustainable environment

    Explainable NLP for Human-AI Collaboration

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    With more data and computing resources available these days, we have seen many novel Natural Language Processing (NLP) models breaking one performance record after another. Some of them even outperform human performance in some specific tasks. Meanwhile, many researchers have revealed weaknesses and irrationality of such models, e.g., having biases against some sub-populations, producing inconsistent predictions, and failing to work effectively in the wild due to overfitting. Therefore, in real applications, especially in high-stakes domains, humans cannot rely carelessly on predictions of NLP models, but they need to work closely with the models to ensure that every final decision made is accurate and benevolent. In this thesis, we devise and utilize explainable NLP techniques to support human-AI collaboration using text classification as a target task. Overall, our contributions can be divided into three main parts. First, we study how useful explanations are for humans according to three different purposes: revealing model behavior, justifying model predictions, and helping humans investigate uncertain predictions. Second, we propose a framework that enables humans to debug simple deep text classifiers informed by model explanations. Third, leveraging on computational argumentation, we develop a novel local explanation method for pattern-based logistic regression models that align better with human judgements and effectively assist humans to perform an unfamiliar task in real-time. Altogether, our contributions are paving the way towards the synergy of profound knowledge of human users and the tireless power of AI machines.Open Acces

    Combating Fake News: A Gravity Well Simulation to Model Echo Chamber Formation In Social Media

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    Fake news has become a serious concern as distributing misinformation has become easier and more impactful. A solution is critically required. One solution is to ban fake news, but that approach could create more problems than it solves, and would also be problematic from the beginning, as it must first be identified to be banned. We initially propose a method to automatically recognize suspected fake news, and to provide news consumers with more information as to its veracity. We suggest that fake news is comprised of two components: premises and misleading content. Fake news can be condensed down to a collection of premises, which may or may not be true, and to various forms of misleading material, including biased arguments and language, misdirection, and manipulation. Misleading content can then be exposed. While valuable, this framework’s utility may be limited by artificial intelligence, which can be used to alter fake news strategies at a rate exceeding the ability to update the framework. Therefore, we propose a model for identifying echo chambers, which are widely reported to be havens for fake news producers and consumers. We simulate a social media interest group as a gravity well, through which we identify the online groups postured to become echo chambers, and thus a source for fake news consumption and replication. This echo chamber model is supported by three pillars related to the social media group: technology employed, topic explored, and confirmation bias of group members. The model is validated by modeling and analyzing 19 subreddits on the Reddit social media platform. Contributions include a working definition for fake news, a framework for recognizing fake news, a generic model for social media echo chambers including three pillars central to echo chamber formation, and a gravity well simulation for social media groups, implemented for 19 subreddits
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