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

Existing rumor detection strategies typically provide detection labels whileignoring their explanation. Nonetheless, providing pieces of evidence toexplain why a suspicious tweet is rumor is essential. As such, a novel model,LOSIRD, was proposed in this paper. First, LOSIRD mines appropriate evidencesentences and classifies them by automatically checking the veracity of therelationship of the given claim and its evidence from about 5 million Wikipediadocuments. LOSIRD then automatically constructs two heterogeneous graph objectsto simulate the propagation layout of the tweets and code the relationship ofevidence. Finally, a graphSAGE processing component is used in LOSIRD toprovide the label and evidence. To the best of our knowledge, we are the firstone who combines objective facts and subjective views to verify rumor. Theexperimental results on two real-world Twitter datasets showed that our modelexhibited the best performance in the early rumor detection task and its rumordetection performance outperformed other baseline and state-of-the-art models.Moreover, we confirmed that both objective information and subjectiveinformation are fundamental clues for rumor detection.

Meet The Truth: Leverage Objective Facts and Subjective Views for Interpretable Rumor Detection