Neural Language Model Based Training Data Augmentation for Weakly Supervised Early Rumor Detection
TABLE V: Number of rumor and non-rumor source tweets and replies in the augmented data.
['[EMPTY]', '[BOLD] Augmented data  [BOLD] Rumor', '[BOLD] Augmented data  [BOLD] Rumor', '[BOLD] Augmented data  [BOLD] Non-rumor', '[BOLD] Augmented data  [BOLD] Non-rumor', '[BOLD] After balancing and merging  [BOLD] Rumor', '[BOLD] After balancing and merging  [BOLD] Rumor', '[BOLD] After balancing and merging  [BOLD] Non-rumor', '[BOLD] After balancing and merging  [BOLD] Non-rumor']
Then, the augmented tweets for the PHEME5 events are merged with the original PHEME5. The number of conversational threads in original PHEME5 is provided in parentheses for comparison. Overall, the number of source tweets for rumors and non-rumors increased by 187% and 185%, respectively. There are 52% and 110% increases in the number of replies for rumor sources and that for non-rumor sources, respectively. The standard deviation of imbalance ratios of non-rumor sources to rumor source improved from 1.24% to 0.35%, respectively. In particular, significant class imbalances in two largest events–“fergusonunrest” and “charliehebdo”–have become moderate as a result of data augmentation.