Jointly embedding the local and global relations of heterogeneous graph for rumor detection
TABLE V: The ablation study results on the Weibo, Twitter15, and Twitter16 data set.
['Models', 'Weibo', 'Twitter15', 'Twitter16']
In order to determine the relative importance of every module of the GLAN, we perform a series of ablation studies over the different parts of the model. The ablation studies are conducted as following orders: w/o LRE: Removing local relation encoding module and only using global structure information for rumor classification. w/o GRE: Removing global relation encoding module and only remaining local structure encoding module for rumor classification. Only Text: Only using CNN to extract text features from source tweet for rumor classification. Next, we evaluate the influence of the global relation encoding module. Intuitively speaking, the GRE module will make the distance between two users with identical class labels close to each other, thus leading to the rumor and non-rumor group high cohesion and low coupling, and thereby it can improve the performance.