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

Rumour stance classification, the task that determines if each tweet in acollection discussing a rumour is supporting, denying, questioning or simplycommenting on the rumour, has been attracting substantial interest. Here weintroduce a novel approach that makes use of the sequence of transitionsobserved in tree-structured conversation threads in Twitter. The conversationthreads are formed by harvesting users' replies to one another, which resultsin a nested tree-like structure. Previous work addressing the stanceclassification task has treated each tweet as a separate unit. Here we analysetweets by virtue of their position in a sequence and test two sequentialclassifiers, Linear-Chain CRF and Tree CRF, each of which makes differentassumptions about the conversational structure. We experiment with eightTwitter datasets, collected during breaking news, and show that exploiting thesequential structure of Twitter conversations achieves significant improvementsover the non-sequential methods. Our work is the first to model Twitterconversations as a tree structure in this manner, introducing a novel way oftackling NLP tasks on Twitter conversations.

Stance Classification in Rumours as a Sequential Task Exploiting the Tree Structure of Social Media Conversations