Understanding and Identifying Rhetorical Questions in Social Media

Abstract

Social media provides a platform for seeking information from a large user base. Information seeking in social media, however, occurs simultaneously with users expressing their viewpoints by making statements. Rhetorical questions have the form of a question but serve the function of a statement and are an important tool employed by users to express their viewpoints. Therefore, rhetorical questions might mislead platforms assisting information seeking in social media. It becomes difficult to identify rhetorical questions as they are not syntactically different from other questions. In this article, we develop a framework to identify rhetorical questions by modeling some motivations of the users to post them. We focus on two motivations of the users drawing from linguistic theories to implicitly convey a message and to modify the strength of a statement previously made. We develop a quantitative framework from these motivations to identify rhetorical questions in social media. We evaluate the framework using two datasets of questions posted on a social media platform Twitter and demonstrate its effectiveness in identifying rhetorical questions. This is the first framework, to the best of our knowledge, to model the possible motivations for posting rhetorical questions to identify them on social media platforms.

Exploring the Role of Context to Distinguish Rhetorical and Information-Seeking Questions

“…However, their work did not explore the usefulness of prior context in dis-tinguishing rhetorical questions from informationseeking questions. Ranganath et al (2016) modeled the contextual overlap between a question and the most recent status message (MRSM) of the same user in Twitter, with the hypothesis that a rhetorical question shares context with its MRSM more than a random question with its MRSM. Bhattasali et al (2015) found that n-gram features from utterances immediately preceding and following a question could help identify rhetorical questions.…”

“…First our evaluation dataset contains humanassigned gold labels and a rich mix of both RQ and IQ. In contrast, Ranganath et al (2016) automatically assigned their dataset with labels according to some heuristic rules, which may be noisy, and Bhattasali et al (2015) used the Switchboard Dialog Act Corpus (Godfrey et al, 1992), where only 5% of questions are rhetorical. Second, neither of these works took preceding context and topic information into account.…”

Asking too much? The rhetorical role of questions in political discourse

“…Sachdeva and Kumaraguru (2017) studied another rhetorical aspect by examining linguistic attributes distinguishing serviceable requests addressed to police on social media from general conversation. Previous research has also been directed at identifying rhetorical questions (Bhattasali et al, 2015) and understanding the motivations of their "askers" (Ranganath et al, 2016). Using the relationship between questions and answers, our work examines the rhetorical and social aspect of questions without predefining a pragmatic dimension and without relying on labeled data.…”

Automatic classification of product reviews into interrogative and noninterrogative: Generating real time answer

“…Besides covering classifications of reviews from different dimensions, there is still need of classification of reviews with respect to interrogatives and non-interrogatives. In this regard, question conveying and not conveying Information (Zhao and Mei, 2013), identifying Answer Seeking questions from Arabic tweets (Hasanain et al, 2014), extraction of subjective/objective and questions and Rhetorical Questions (Hasanain et al, 2014;Liu and Jansen, 2015;Ranganath et al, 2016;Liu and Jansen, 2016), investigation of questions asked by Arab journalists (Hasanain et al, 2016), and detection of user intent behind asking the question (Kharche and Mante, 2017) have been studied. However, they all used the data readily available in the form of interrogatives.…”