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Top1. Introduction
There has been a major growth in the use of multimedia data on the Internet in the past decade, especially the microblogging platforms. Microblogs, such as Twitter, Tumblr, Weibo, and Facebook, allow users to exchange small contents including images, short videos, as well as comments. Some previous research efforts were paid on these kinds of multimedia data (Yan et al., 2016; Chen, Hsieh, Yan, & Chen, 2015; Meng et al., 2014; Yan et al., 2014; Chen, Zhu, Lin, & Shyu, 2013; Lin, Chen, Shyu, & Chen, 2011; Zhu, Lin, Shyu, & Chen, 2011; Lin & Shyu, 2010; Chen, Rubin, Shyu, & Zhang, 2006; Chen, Shyu, & Kashyap, 2000). Twitter is one of the most widely used microblog platforms nowadays. As with traditional blogging, Twitter users ranging from regular users to politicians, celebrities, and company representatives post and interact with messages ranging from simple to the thematic post (known as “tweets”). As a result, it is possible to collect tweets of users from different social and interested groups for commercial or academic proposes. In 2016, the battle on Twitter was an integral part of a prearranged effort to disturb the past U.S. presidential election. To visualize the overall picture, Figure 1(a) presents the daily tweet counts between Hillary Clinton and Donald Trump for the election time period in 2016; while the popularity measure between the two candidates is shown in Figure 1(b) by mapping the retweet and favorite counts of the two candidates.
Figure 1.
Daily tweets versus retweet and favorite counts
Generally speaking, stance detection can be formulated in different ways. In the scope of this paper, it is defined as automatically determining whether a tweet or a Twitter user tends to endorse the candidate of Democratic (Clinton) or Republican (Trump) Party. For instance, considering the candidate-tweet pair in Figure 2, the tweet from “Katy Perry” was in favor of Clinton obviously. Stance detection could be viewed as a subtask of opinion mining and it stands next to the sentiment analysis; while one significant difference is that the models determine whether a piece of a text is positive, negative, or neutral in sentiment analysis (Krejzl, Hourová, & Steinberger, 2017). However, our work is to determine a tweet or a Twitter user’s favorability towards either candidate even when the candidate’s name is not explicitly mentioned in the text. It is notable that the lack of evidence for ‘favor’ or ‘against’ does not necessarily imply that a tweet or a Twitter user is neutral towards the target, and it just means that we cannot deduce stance sometimes (Sobhani, Mohammad, & Kiritchenko, 2016).
Figure 2.
Katy Perry shows her support for Clinton to the public on Twitter