Stance Classification in Rumours as a Sequential Task Exploiting the Tree Structure of Social Media Conversations
Table 4: Micro- and Macro-F1 performance results broken down by event, along with a comparison with the results obtained by Lukasik et al. lukasik2016hawkes’s state-of-the-art approach based on Hawkes Processes, where available.
['Event', 'Tree CRF Micro-F1', 'Tree CRF Macro-F1', 'HP  Micro-F1', 'HP  Macro-F1']
For comparison with the state-of-the-art stance classification approach by Lukasik et al. \shortcitelukasik2016hawkes Note that Lukasik et al. \shortcitelukasik2016hawkes only tested their approach on four of the events, and therefore performance scores for the rest of the events are not shown. As can be observed from the four events for which we have comparable results, the Hawkes Process performs better in terms of micro-F1, and therefore accurately classifying more instances. However, the Tree CRF performs substantially better in terms of macro-F1, which shows Tree CRF’s ability to better estimate the distribution of labels in what is a highly imbalanced task and hence favouring the use of conversational structure in the classification process. We deem this a strong factor in this case as even a simple majority classifier achieves high micro-F1 scores, and the challenge lies in boosting macro-F1 scores to better balance the classification.