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

Automatic response forecasting for news media plays a crucial role inenabling content producers to efficiently predict the impact of news releasesand prevent unexpected negative outcomes such as social conflict and moralinjury. To effectively forecast responses, it is essential to develop measuresthat leverage the social dynamics and contextual information surroundingindividuals, especially in cases where explicit profiles or historical actionsof the users are limited (referred to as lurkers). As shown in a previousstudy, 97% of all tweets are produced by only the most active 25% of users.However, existing approaches have limited exploration of how to best processand utilize these important features. To address this gap, we propose a novelframework, named SocialSense, that leverages a large language model to induce abelief-centered graph on top of an existent social network, along withgraph-based propagation to capture social dynamics. We hypothesize that theinduced graph that bridges the gap between distant users who share similarbeliefs allows the model to effectively capture the response patterns. Ourmethod surpasses existing state-of-the-art in experimental evaluations for bothzero-shot and supervised settings, demonstrating its effectiveness in responseforecasting. Moreover, the analysis reveals the framework's capability toeffectively handle unseen user and lurker scenarios, further highlighting itsrobustness and practical applicability.

Decoding the Silent Majority: Inducing Belief Augmented Social Graph with Large Language Model for Response Forecasting