Title: Mining Significant Microblogs for Misinformation Identification: An Attention-based Approach,
Abstract: With the rapid growth of social media, massive misinformation is also
spreading widely on social media, such as microblog, and bring negative effects
to human life. Nowadays, automatic misinformation identification has drawn
attention from academic and industrial communities. For an event on social
media usually consists of multiple microblogs, current methods are mainly based
on global statistical features. However, information on social media is full of
noisy and outliers, which should be alleviated. Moreover, most of microblogs
about an event have little contribution to the identification of
misinformation, where useful information can be easily overwhelmed by useless
information. Thus, it is important to mine significant microblogs for a
reliable misinformation identification method. In this paper, we propose an
Attention-based approach for Identification of Misinformation (AIM). Based on
the attention mechanism, AIM can select microblogs with largest attention
values for misinformation identification. The attention mechanism in AIM
contains two parts: content attention and dynamic attention. Content attention
is calculated based textual features of each microblog. Dynamic attention is
related to the time interval between the posting time of a microblog and the
beginning of the event. To evaluate AIM, we conduct a series of experiments on
the Weibo dataset and the Twitter dataset, and the experimental results show
that the proposed AIM model outperforms the state-of-the-art methods.