English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

There has been an explosion of multimodal content generated on social medianetworks in the last few years, which has necessitated a deeper understandingof social media content and user behavior. We present a novelcontent-independent content-user-reaction model for social multimedia contentanalysis. Compared to prior works that generally tackle semantic contentunderstanding and user behavior modeling in isolation, we propose a generalizedsolution to these problems within a unified framework. We embed users, imagesand text drawn from open social media in a common multimodal geometric space,using a novel loss function designed to cope with distant and disparatemodalities, and thereby enable seamless three-way retrieval. Our model not onlyoutperforms unimodal embedding based methods on cross-modal retrieval tasks butalso shows improvements stemming from jointly solving the two tasks on Twitterdata. We also show that the user embeddings learned within our joint multimodalembedding model are better at predicting user interests compared to thoselearned with unimodal content on Instagram data. Our framework thus goes beyondthe prior practice of using explicit leader-follower link information toestablish affiliations by extracting implicit content-centric affiliations fromisolated users. We provide qualitative results to show that the user clustersemerging from learned embeddings have consistent semantics and the ability ofour model to discover fine-grained semantics from noisy and unstructured data.Our work reveals that social multimodal content is inherently multimodal andpossesses a consistent structure because in social networks meaning is createdthrough interactions between users and content.

Deep Unified Multimodal Embeddings for Understanding both Content and Users in Social Media Networks