"All I know about politics is what I read in Twitter": Weakly Supervised Models for Extracting Politicians' Stances From Twitter
نویسندگان
چکیده
During the 2016 United States presidential election, politicians have increasingly used Twitter to express their beliefs, stances on current political issues, and reactions concerning national and international events. Given the limited length of tweets and the scrutiny politicians face for what they choose or neglect to say, they must craft and time their tweets carefully. The content and delivery of these tweets is therefore highly indicative of a politician’s stances. We present a weakly supervised method for extracting how issues are framed and temporal activity patterns on Twitter for popular politicians and issues of the 2016 election. These behavioral components are combined into a global model which collectively infers the most likely stance and agreement patterns among politicians, with respective accuracies of 86.44% and 84.6% on average.
منابع مشابه
Identifying Stance by Analyzing Political Discourse on Twitter
Politicians often use Twitter to express their beliefs, stances on current political issues, and reactions concerning national and international events. Since politicians are scrutinized for what they choose or neglect to say, they craft their statements carefully. Thus despite the limited length of tweets, their content is highly indicative of a politician’s stances. We present a weakly superv...
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