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

The overwhelming amount and rate of information update in online social mediais making it increasingly difficult for users to allocate their attention totheir topics of interest, thus there is a strong need for prioritizing newsfeeds. The attractiveness of a post to a user depends on many complexcontextual and temporal features of the post. For instance, the contents of thepost, the responsiveness of a third user, and the age of the post may all haveimpact. So far, these static and dynamic features has not been incorporated ina unified framework to tackle the post prioritization problem. In this paper,we propose a novel approach for prioritizing posts based on a feature modulatedmulti-dimensional point process. Our model is able to simultaneously capturetextual and sentiment features, and temporal features such as self-excitation,mutual-excitation and bursty nature of social interaction. As an evaluation, wealso curated a real-world conversational benchmark dataset crawled fromFacebook. In our experiments, we demonstrate that our algorithm is able toachieve the-state-of-the-art performance in terms of analyzing, predicting, andprioritizing events. In terms of interpretability of our method, we observethat features indicating individual user profile and linguistic characteristicsof the events work best for prediction and prioritization of new events.

A Continuous-time Mutually-Exciting Point Process Framework for Prioritizing Events in Social Media