Two-path Deep Semi-supervised Learning for Timely Fake News Detection
TABLE II: Number of tweets and class distribution in the PHEME dataset.
['[BOLD] Events', '[BOLD] Tweets', '[BOLD] Fake', '[BOLD] True']
We employ PHEME [zubiaga2016analysing] to verify the effectiveness of the proposed framework on social media data, where it collects 330 twitter threads. Tweets in PHEME are labeled as true or false in terms of thread structures and follow-follower relationships. PHEME dataset is related to nine events whereas this paper only focuses on the five main events, namely, Germanwings-crash (GC), Charlie Hebdo (CH), Sydney siege (SS), Ferguson (FE), and Ottawa shooting (OS). It has different levels of annotations such as thread level and tweet level. We only adopt the annotations on the tweet level and thus classify the tweets as fake or true. It is observed that the class distribution is different among these events. For example, three events including CH, SS, and FE have more true news while the event GC and OS have more fake news. Furthermore, class distributions for events such as CH, SS, FE are significantly imbalanced, which will be a challenge to the detection task (a binary classification task).