  The consumption of news has changed significantly as the Web has become the
most influential medium for information. To analyze and contextualize the large
amount of news published every day, the geographic focus of an article is an
important aspect in order to enable content-based news retrieval. There are
methods and datasets for geolocation estimation from text or photos, but they
are typically considered as separate tasks. However, the photo might lack
geographical cues and text can include multiple locations, making it
challenging to recognize the focus location using a single modality. In this
paper, a novel dataset called Multimodal Focus Location of News
(MM-Locate-News) is introduced. We evaluate state-of-the-art methods on the new
benchmark dataset and suggest novel models to predict the focus location of
news using both textual and image content. The experimental results show that
the multimodal model outperforms unimodal models.
