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الكشف عن الموقف في مقالات الأخبار الألمانية
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يشكل الاستخدام الواسع للإنترنت والنشر السريع للمعلومات التحدي المتمثل في تحديد صحة محتواه. اكتشف الكشف عن الموقف، الذي تعد مهمة التنبؤ بموقف نص فيما يتعلق بهدف محدد (سؤال المطالبة أو النقاش)، لتحديد صحة المعلومات في مهام مثل تصنيف الشائعات والكشف عن الأخبار المزيفة. في حين أن معظم الأعمال ومجموعات البيانات المتاحة للكشف عن الموقف يعالج النصوص القصيرة مقتطفات مستخرجة من الحوارات النصية، أو منصات وسائل التواصل الاجتماعي، أو عناوين الأخبار مع التركيز القوي على اللغة الإنجليزية، فهناك نقص في الموارد المستهدفة للنصوص الطويلة بلغات أخرى. مساهمتنا في هذه الورقة هي ذات شقين. أولا، نقدم مجموعة بيانات ألمانية من أسئلة النقاش والمقالات الإخبارية التي يتم تفاحها يدويا للكشف عن الموقف والعاطفة. ثانيا، نستفيد من مجموعة البيانات لمعالجة المهمة الخاضعة للإشراف على تصنيف موقف مقال إخباري فيما يتعلق بمسألة النقاش وتوفير نماذج خط الأساس كمرجع للعمل في المستقبل بشأن اكتشاف الموقف في المقالات الإخبارية الألمانية.
The widespread use of the Internet and the rapid dissemination of information poses the challenge of identifying the veracity of its content. Stance detection, which is the task of predicting the position of a text in regard to a specific target (e.g. claim or debate question), has been used to determine the veracity of information in tasks such as rumor classification and fake news detection. While most of the work and available datasets for stance detection address short texts snippets extracted from textual dialogues, social media platforms, or news headlines with a strong focus on the English language, there is a lack of resources targeting long texts in other languages. Our contribution in this paper is twofold. First, we present a German dataset of debate questions and news articles that is manually annotated for stance and emotion detection. Second, we leverage the dataset to tackle the supervised task of classifying the stance of a news article with regards to a debate question and provide baseline models as a reference for future work on stance detection in German news articles.
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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 goal of stance detection is to identify whether the author of a text is in favor of, neutral or against a specific target. Despite substantial progress on this task, one of the remaining challenges is the scarcity of annotations. Data augmentatio
n is commonly used to address annotation scarcity by generating more training samples. However, the augmented sentences that are generated by existing methods are either less diversified or inconsistent with the given target and stance label. In this paper, we formulate the data augmentation of stance detection as a conditional masked language modeling task and augment the dataset by predicting the masked word conditioned on both its context and the auxiliary sentence that contains target and label information. Moreover, we propose another simple yet effective method that generates target-aware sentence by replacing a target mention with the other. Experimental results show that our proposed methods significantly outperforms previous augmentation methods on 11 targets.
Statements that are intentionally misstated (or manipulated) are of considerable interest to researchers, government, security, and financial systems. According to deception literature, there are reliable cues for detecting deception and the belief t
hat liars give off cues that may indicate their deception is near-universal. Therefore, given that deceiving actions require advanced cognitive development that honesty simply does not require, as well as people's cognitive mechanisms have promising guidance for deception detection, in this Ph.D. ongoing research, we propose to examine discourse structure patterns in multilingual deceptive news corpora using the Rhetorical Structure Theory framework. Considering that our work is the first to exploit multilingual discourse-aware strategies for fake news detection, the research community currently lacks multilingual deceptive annotated corpora. Accordingly, this paper describes the current progress in this thesis, including (i) the construction of the first multilingual deceptive corpus, which was annotated by specialists according to the Rhetorical Structure Theory framework, and (ii) the introduction of two new proposed rhetorical relations: INTERJECTION and IMPERATIVE, which we assume to be relevant for the fake news detection task.
Cross-target generalization constitutes an important issue for news Stance Detection (SD). In this short paper, we investigate adversarial cross-genre SD, where knowledge from annotated user-generated data is leveraged to improve news SD on targets u
nseen during training. We implement a BERT-based adversarial network and show experimental performance improvements over a set of strong baselines. Given the abundance of user-generated data, which are considerably less expensive to retrieve and annotate than news articles, this constitutes a promising research direction.
Detecting stance on Twitter is especially challenging because of the short length of each tweet, the continuous coinage of new terminology and hashtags, and the deviation of sentence structure from standard prose. Fine-tuned language models using lar
ge-scale in-domain data have been shown to be the new state-of-the-art for many NLP tasks, including stance detection. In this paper, we propose a novel BERT-based fine-tuning method that enhances the masked language model for stance detection. Instead of random token masking, we propose using a weighted log-odds-ratio to identify words with high stance distinguishability and then model an attention mechanism that focuses on these words. We show that our proposed approach outperforms the state of the art for stance detection on Twitter data about the 2020 US Presidential election.
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