See the MediaEval 2021 webpage for information on how to register and participate.

Task Description

News articles use both text and images to communicate their message. The overall goal of this task is to better understand the relationship between the textual and visual (images) content of news articles, and the impact of these elements on readers’ interest in the news.

Within this task participants are expected to discover and develop patterns/models to describe the relation between:

To do this, the participants will be provided a sizable real-world dataset of news items, each consisting of textual features (headline and snippet) as well the link to download the accompanying image.

The task requires extracting features from visual images and textual descriptions. Participants must analyze the features’ correlation concerning the context, noise, and the topic domain.

The NewsImages task includes two subtasks: Image-Text Re-Matching and News Click Prediction. The participants can choose to participate in either or both subtasks.

Participants are encouraged to make their code public with their submission.

Subtask 1: Image-Text Re-Matching: News articles often contain images that accompany the text. The connection between the images and the text is more complex than often realized. Aspects such as readers’ attention, difference between authentic imagery and stock photos, and placement on the website play important roles. We encourage participants to consider the explainability of their models. In this subtask, by using the news articles and accompanying images in the provided dataset, participants should predict which image was published with a given news article. We also ask participants to report their insights into characteristics that connect the text of news articles and the images. We expect that these insights contribute to the understanding of the image-text relationship in news articles.

Subtask 2: News Click Prediction: News websites present recommendations to users suggesting what to read next. These are often displayed as the article title accompanied by an image. In this task, participants investigate whether recommendations that are frequently clicked by users can be predicted using the textual content of the article and/or the accompanying image. Publishers tend to focus on click-related scores to determine the value of recommendations.

Motivation and background

Online news articles are multimodal: the textual content of an article is often accompanied by an image. The image is important for illustrating the content of the text, but also attracting readers’ attention. Research in multimedia and recommender systems generally assumes a simple relationship between images and text occurring together. For example, in image captioning [6], the caption is often assumed to describe the literally depicted content of the image. In contrast, when images accompany news articles, the relationship becomes less clear [8]. The goal of this task is to investigate these intricacies in more depth, in order to understand the implications that it may have for the areas of journalism and recommender systems.

The task is formulated into two straightforward subtasks that participants can address using text-based and/or image features. However, the ultimate objective of this task is to gain additional insight. Specifically, we are curious about the connection between the textual content of articles and the images that accompany them and also about the connection between the image and title shown by a recommender system to users and the tendency of users to click on the recommended article. We are especially interested in aspects of images that go beyond the conventional set of concepts studied by concept detection. We are also interested in aspects of images that go beyond the literally depicted content. Such aspects include color, style, and framing.

Target group

This task targets researchers who are interested in the connection between images and text and images and user behavior. This includes people working in the areas of computer vision, recommender systems, cross-modal information retrieval, as well as in the area of news analysis.


The data set is a large collection of news articles from a German publisher that publishes news article recommendations on its website. Each article consists of a headline and a text snippet (first 256 characters) plus the link to download the accompanying image. The data is split into a training set (ground truth provided) and a test set. Participants must crawl the images themselves as we lack the necessary copyright to provide them directly. To strictly ensure fair comparison, the final test set will include the test set articles for which all participants could successfully access the images.

Evaluation methodology

Subtask 1: Image-Text Re-Matching: For each news article in the test set, participants return the top five images that they predict to have accompanied that article. The ground truth (the correct news article-image-connection) is defined by the image that was published in the news article on the web portal. We encourage participants to additionally provide confidence scores such that we can learn more about the robustness of their methods. Success is measured with Precision@5. This means, that for each news item, 5 images should be suggested. If the correct images is in the suggested set, the predction is seen as correct. Since only one image per news item is correct, the metric could also be seen as Recall@5. Additionally, we promote the idea of explainability and ask the participants to look into the inner workings of their methods. What does the model tell? For which instances has the method failed and why?

Subtask 2: News Click Prediction: Given a set of images, participants predict the topmost news articles that are likely to be clicked when they are recommended. The number of top images will be specified. Success is measured by precision. More concretely, participants score each image which induces a ranking. We will determine the precision at a suited cut off point. Again, we encourage participants to examine their models and try to explain what they have picked up.

Analysis and Insight: For both tasks, the ultimate goal is to understand news and news consumption behavior. We will also judge participants in terms of the quality of the insight that they achieve about the relationship between text and images and in the relationship between images and news consumption behavior.

[1] Corsini, Francesco, and Martha A. Larson. CLEF NewsREEL 2016: image based recommendation. (2016).

[2] Das, A. S., Datar, M., Garg, A., & Rajaram, S. (2007, May). Google news personalization: scalable online collaborative filtering. In Proceedings of the 16th international conference on World Wide Web (pp. 271-280).

[3] Garcin, F., Faltings, B., Donatsch, O., Alazzawi, A., Bruttin, C., & Huber, A. (2014, October). Offline and online evaluation of news recommender systems at In Proceedings of the 8th ACM Conference on Recommender systems (pp. 169-176).

[4] Ge, M., & Persia, F. (2017). A survey of multimedia recommender systems: Challenges and opportunities. International Journal of Semantic Computing, 11(03), 411-428.

[5] Hopfgartner, F., Balog, K., Lommatzsch, A., Kelly, L., Kille, B., Schuth, A., & Larson, M. (2019). Continuous evaluation of large-scale information access systems: a case for living labs. In Information Retrieval Evaluation in a Changing World (pp. 511-543). Springer, Cham.

[6] Hossain, M. Z., Sohel, F., Shiratuddin, M. F., & Laga, H. (2019). A comprehensive survey of deep learning for image captioning. ACM Computing Surveys (CSUR), 51(6), 1-36.

[7] Lommatzsch, A., Kille, B., Hopfgartner, F., Larson, M., Brodt, T., Seiler, J., & Özgöbek, Ö. (2017, September). CLEF 2017 NewsREEL overview: A stream-based recommender task for evaluation and education. In International Conference of the Cross-Language Evaluation Forum for European Languages (pp. 239-254). Springer, Cham.

[8] Oostdijk, N., van Halteren, H., Bașar, E., & Larson, M. (2020, May). The Connection between the Text and Images of News Articles: New Insights for Multimedia Analysis. In Proceedings of The 12th Language Resources and Evaluation Conference (pp. 4343-4351).

Task organizers

Task Schedule (Updated)