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Register a new userDetecting Frames in News Headlines and Lead Images in U.S. Gun Violence Coverage
الكشف عن الإطارات في عناوين الأخبار والصور الرصاص في تغطية أعمال عنف بندقية الولايات المتحدة
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هيكل وسائل الإعلام الإخبارية الإبلاغ عن الأحداث أو القضايا باستخدام وجهات نظر معينة. عند وصف الحادث الذي ينطوي على أعمال عنف بندقية، على سبيل المثال، قد يركز بعض الصحفيين على تنظيم الصحة العقلية أو البندقية، بينما يجوز للآخرين التأكيد على مناقشة حقوق البندقية. تسمى هذه المنظورات إطارات "في بحث الاتصالات. نحن ندرس، لأول مرة، قيمة الجمع بين الصور الرائدة ومعلوماتها السياقية مع النص لتحديد إطار مقال أخبار معين. نلاحظ أن استخدام أوضاع متعددة من المعلومات (المقالات والميزات المشتقة من الصور) يحسن التنبؤ بإطارات الأخبار حول أي طريقة واحدة من المعلومات عندما تكون الصور ذات صلة بإطارات العناوين الرئيسية. نلاحظ أيضا أن أهمية صورة الإطار مرتبطة سهولة نقل الإطارات عبر الصور، والتي نسميها. بالإضافة إلى ذلك، نطلق سراح بيانات تأطير الأخبار متعددة الوسائط المتعددة متعلقة بعنف بندقية في الولايات المتحدة، المنسوحة والمشروحة من قبل باحثين الاتصالات. ستتيح DataSet للباحثين مواصلة فحص استخدام طرائق معلومات متعددة لدراسة تأطير الوسائط.
News media structure their reporting of events or issues using certain perspectives. When describing an incident involving gun violence, for example, some journalists may focus on mental health or gun regulation, while others may emphasize the discussion of gun rights. Such perspectives are called frames'' in communication research.We study, for the first time, the value of combining lead images and their contextual information with text to identify the frame of a given news article. We observe that using multiple modes of information(article- and image-derived features) improves prediction of news frames over any single mode of information when the images are relevant to the frames of the headlines.We also observe that frame image relevance is related to the ease of conveying frames via images, which we call frame concreteness. Additionally, we release the first multimodal news framing dataset related to gun violence in the U.S., curated and annotated by communication researchers. The dataset will allow researchers to further examine the use of multiple information modalities for studying media framing.
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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.
We describe SemEval-2021 task 6 on Detection of Persuasion Techniques in Texts and Images: the data, the annotation guidelines, the evaluation setup, the results, and the participating systems. The task focused on memes and had three subtasks: (i) de
tecting the techniques in the text, (ii) detecting the text spans where the techniques are used, and (iii) detecting techniques in the entire meme, i.e., both in the text and in the image. It was a popular task, attracting 71 registrations, and 22 teams that eventually made an official submission on the test set. The evaluation results for the third subtask confirmed the importance of both modalities, the text and the image. Moreover, some teams reported benefits when not just combining the two modalities, e.g., by using early or late fusion, but rather modeling the interaction between them in a joint model.
Memes are one of the most popular types of content used to spread information online. They can influence a large number of people through rhetorical and psychological techniques. The task, Detection of Persuasion Techniques in Texts and Images, is to
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The following system description presents our approach to the detection of persuasion techniques in texts and images. The given task has been framed as a multi-label classification problem with the different techniques serving as class labels. The mu
lti-label classification problem is one in which a list of target variables such as our class labels is associated with every input chunk and assumes that a document can simultaneously and independently be assigned to multiple labels or classes. In order to assign class labels to the given memes, we opted for RoBERTa (A Robustly Optimized BERT Pretraining Approach) as a neural network architecture for token and sequence classification. Starting off with a pre-trained model for language representation we fine-tuned this model on the given classification task with the provided annotated data in supervised training steps. To incorporate image features in the multi-modal setting, we rely on the pre-trained VGG-16 model architecture.
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