Attention Based Neural Architecture For Rumor Detection With Author Context Awareness

Keywords

Attention; Context; Deep Neural Network; Rumor Detection; Social Network

Abstract

The prevalence of social media has made information sharing possible across the globe. The downside, unfortunately, is the wide spread of misinformation. Methods applied in most previous rumor classifiers give an equal weight, or attention, to words in the microblog, and do not take the context beyond microblog contents into account; therefore, the accuracy becomes plateaued. In this research, we propose an ensemble neural architecture to detect rumor on Twitter. The architecture incorporates word attention and context from the author to enhance the classification performance. In particular, the word-level attention mechanism enables the architecture to put more emphasis on important words when constructing the text representation. To derive further context, microblog posts composed by individual authors are exploited since they can reflect style and characteristics in spreading information, which are significant cues to help classify whether the shared content is rumor or legitimate news. The experiment on the real-world Twitter dataset collected from two well-known rumor tracking websites demonstrates promising results.

Publication Date

9-1-2018

Publication Title

2018 13th International Conference on Digital Information Management, ICDIM 2018

Number of Pages

82-87

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/ICDIM.2018.8847052

Socpus ID

85073503720 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85073503720

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