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Register a new userCan You Distinguish Truthful from Fake Reviews? User Analysis and Assistance Tool for Fake Review Detection
يمكنك التمييز الصادق من مراجعات وهمية؟أداة تحليل المستخدم والمساعدة لكشف مراجعة وهمية
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مراجعات العملاء مفيدة في توفير تجربة غير مباشرة من المنتج.غالبا ما يستخدم الناس الاستعراضات التي كتبها عملاء آخرون كمبدأ توجيهي قبل شراء منتج.هذا السلوك يدل على صحة الاستعراضات في منصات التجارة الإلكترونية.ومع ذلك، أصبحت مراجعات وهمية بشكل متزايد متاعب لكل من المستهلكين وأصحاب المنتجات.لمعالجة هذه المشكلة، نقترح عليك فقط تحتاج الذهب (يونغ)، وهي أداة تعدين معلومات أساسية للكشف عن مراجعات وهمية وتعزيز تقدير المستخدم.تظهر النتائج التجريبية لدينا الأداء البشري الفقراء على اكتشاف مراجعة وهمية، وقدرة المستخدم المحسنة بشكل كبير بالنظر إلى أدواتنا، والحاجة النهائية للحصول على اعتماد المستخدم على الأداة.
Customer reviews are useful in providing an indirect, secondhand experience of a product. People often use reviews written by other customers as a guideline prior to purchasing a product. Such behavior signifies the authenticity of reviews in e-commerce platforms. However, fake reviews are increasingly becoming a hassle for both consumers and product owners. To address this issue, we propose You Only Need Gold (YONG), an essential information mining tool for detecting fake reviews and augmenting user discretion. Our experimental results show the poor human performance on fake review detection, substantially improved user capability given our tool, and the ultimate need for user reliance on the tool.
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The exponential growth of the internet and social media in the past decade gave way to the increase in dissemination of false or misleading information. Since the 2016 US presidential election, the term fake news'' became increasingly popular and thi
s phenomenon has received more attention. In the past years several fact-checking agencies were created, but due to the great number of daily posts on social media, manual checking is insufficient. Currently, there is a pressing need for automatic fake news detection tools, either to assist manual fact-checkers or to operate as standalone tools. There are several projects underway on this topic, but most of them focus on English. This research-in-progress paper discusses the employment of deep learning methods, and the development of a tool, for detecting false news in Portuguese. As a first step we shall compare well-established architectures that were tested in other languages and analyse their performance on our Portuguese data. Based on the preliminary results of these classifiers, we shall choose a deep learning model or combine several deep learning models which hold promise to enhance the performance of our fake news detection system.
While COVID-19 vaccines are finally becoming widely available, a second pandemic that revolves around the circulation of anti-vaxxer fake news'' may hinder efforts to recover from the first one. With this in mind, we performed an extensive analysis o
f Arabic and English tweets about COVID-19 vaccines, with focus on messages originating from Qatar. We found that Arabic tweets contain a lot of false information and rumors, while English tweets are mostly factual. However, English tweets are much more propagandistic than Arabic ones. In terms of propaganda techniques, about half of the Arabic tweets express doubt, and 1/5 use loaded language, while English tweets are abundant in loaded language, exaggeration, fear, name-calling, doubt, and flag-waving. Finally, in terms of framing, Arabic tweets adopt a health and safety perspective, while in English economic concerns dominate.
The last several years have seen a massive increase in the quantity and influence of disinformation being spread online. Various approaches have been developed to target the process at different stages from identifying sources to tracking distributio
n in social media to providing follow up debunks to people who have encountered the disinformation. One common conclusion in each of these approaches is that disinformation is too nuanced and subjective a topic for fully automated solutions to work but the quantity of data to process and cross-reference is too high for humans to handle unassisted. Ultimately, the problem calls for a hybrid approach of human experts with technological assistance. In this paper we will demonstrate the application of certain state-of-the-art NLP techniques in assisting expert debunkers and fact checkers as well as the role of these NLP algorithms within a more holistic approach to analyzing and countering the spread of disinformation. We will present a multilingual corpus of disinformation and debunks which contains text, concept tags, images and videos as well as various methods for searching and leveraging the content.
As the world continues to fight the COVID-19 pandemic, it is simultaneously fighting an infodemic' -- a flood of disinformation and spread of conspiracy theories leading to health threats and the division of society. To combat this infodemic, there i
s an urgent need for benchmark datasets that can help researchers develop and evaluate models geared towards automatic detection of disinformation. While there are increasing efforts to create adequate, open-source benchmark datasets for English, comparable resources are virtually unavailable for German, leaving research for the German language lagging significantly behind. In this paper, we introduce the new benchmark dataset FANG-COVID consisting of 28,056 real and 13,186 fake German news articles related to the COVID-19 pandemic as well as data on their propagation on Twitter. Furthermore, we propose an explainable textual- and social context-based model for fake news detection, compare its performance to black-box'' models and perform feature ablation to assess the relative importance of human-interpretable features in distinguishing fake news from authentic news.
The flexibility of the inference process in Variational Autoencoders (VAEs) has recently led to revising traditional probabilistic topic models giving rise to Neural Topic Models (NTM). Although these approaches have achieved significant results, sur
prisingly very little work has been done on how to disentangle the latent topics. Existing topic models when applied to reviews may extract topics associated with writers' subjective opinions mixed with those related to factual descriptions such as plot summaries in movie and book reviews. It is thus desirable to automatically separate opinion topics from plot/neutral ones enabling a better interpretability. In this paper, we propose a neural topic model combined with adversarial training to disentangle opinion topics from plot and neutral ones. We conduct an extensive experimental assessment introducing a new collection of movie and book reviews paired with their plots, namely MOBO dataset, showing an improved coherence and variety of topics, a consistent disentanglement rate, and sentiment classification performance superior to other supervised topic models.
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