The storytelling narrative is the key to conveying an author's opinion and argument about a specific topic to intended readers. A good narrative not only conveys the underlying message but also leads readers to a better conceptual understanding of the discussed topic. Stories play a vital role in understanding through their chronological style of reporting. Similarly, to gain readers' attention from beginning to end, news agencies generally adopt an inverted pyramid structure where a story starts with stating the most important material. The facts of news are encapsulated in five basic questions What, When and Why which are fundamental for any news readers' understanding. Distributions of the categorical facts of the news correlate to the answers to What, When, Where and Who questions and the answer to Why is correlated to the authors' argumentation and evidence. In this paper, we presented a theory of mapping 5Ws and Aristotle's Rhetoric into the format of Joseph Campbell's The Hero's Journey as a structural story template to assist in automatic understanding of the structure of news and evaluated the approach via cognitive reading and writing user experiment tasks.
Authors preserve their rhetoric, creativity and knowledge in stories. News writing falls under
the genre of storytelling [
Ethos, Logos and Pathos
[
Considering the speed and amount of new information along with the rise of fake news, fact-checking the news content and its rhetoric has become a big challenge during news analysis. To automatically predict the veracity of claims in news, researchers have been using techniques based on natural language processing, machine learning, knowledge representation and databases [7]. The authors of fake news aim to excite the sentiments of the readers towards the intended goal [8]. Therefore, to determine the polarity and strength of sentiments expressed in fake news, various knowledge-based, context-based and content-based sentiment analysis approaches are used to detect fake news [8]. Some news sentiment analysis systems assign scores indicating positive or negative opinions to each topic in the corpus using statistical analysis on sentiment cues [9]. Some systems use deep learning methodologies of recurrent (a) Story words mapping based on the word categories (b) Sample story plan visualization of a sample news neural networks with long short-term memory units for forecasting from news archives [10]. News, articles and books convey authors's rhetoric of the author about the topics discussed in the document. In this paper, we employed story templates based on terms extraction to understand the rhetoric of the author and validated it using user experiments. This can be used in rhetoric-based fact-checking in future studies.
News, articles and books are a rich communication medium that preserves authors' rhetoric through Ethos, Logos and Pathos which triggers readers' cognitive thinking for learning. The facts of a document are encapsulated in six basic questions Who, Where, What, When, Why and How. In Figure 1 (b), we proposed a theoretical distribution of rhetorical questions along the sequential structure of news using The Hero's Journey template. Following this template diagram, we visualized the automatically extracted authors' plan in a clockwise circular pattern using the D3 circular bar plot [11] as shown in Figure 2 (b). The concept of known and unknown is also inspired by this template. This is a factual distribution encapsulating the answer to categorical facts of the news What, When, Where and Who. The answer to Why and How is correlated to the authors' argumentation and evidence. We also mapped Aristotle's Rhetoric into this format for the news as shown in Figure 2 (a). To establish Ethos, at the beginning of the document, from title to introduction, the author explains the topic/What, the problem area/Where and the main characters/Who related to the story. To establish Logos in the body of the document, the authors attempt to explain Why and How. For enriching/educating the reader, in the conclusion the author attempts to establish Pathos by stating the current situation of the event and by addressing future issues. Information about When of news is related to its date attribute. 0-2 where 0 = wrongly understood … 2 = well understood 0-2 where 0 = wrongly understood … 2 = well understood 0-2 where 0 = wrongly understood … 2 = well understood 0-1 where 0 = wrongly understood, 1 = understood 0-2 where 0 = wrongly understood … 2 = well understood 0-1 where 0 = false, 1 = true 1-5 where 0 = poor … 5 = well written
We used an archive of pharmaceutical news from a website for analysis. The information extraction philosophy from a news document for this demo is based on our skimming technique. Word is the atomic unit of processing. This technique processes all sentences from top to bottom for extracting story words. We split news into M (5) blocks along document length and focus on multiple block appearances of selected words. Story words are extracted from the news based on four categories.
• Wtopic: Most frequent N words that appear in all blocks. • Wforward: Words that have the highest forward position weight. If a word appears earlier (based on sentence position) in the document it gets a higher weight. • Wmiddle: Words that appear in more than M / 2 blocks. • Wbackward: Words that have the highest backward position weight. If a word appears later(based on sentence position) in the document it gets a higher weight.
The theoretical mapping is evaluated via an online participation-based controlled experiment. We scored understanding factors of readers' cognition based on comprehension tasks using a homogeneous group of 32 participants. We followed the within-group experiment design. Each participant was given four comprehension tasks. The order of the tasks was generated using Latine Square Design. Each participant responded to these questions: ”When did the incident take place?”, ”Who are the main character(s)/role player(s) of the story?”, ”Where did the story take place?”, ”Why is the story important?”, ”Write a summary of the story in a few sentences” and ”Ease of comprehension”. We invited 3 academic reviewers to blindly score the comprehension tasks. Apart from ease of the task and task time scores, the rest of the questions were scored by the reviewers based on model answers and following the scales in Table 1. We performed a paired t-test on the average scores of text-based comprehension tasks against the average scores of visualization-based comprehension tasks. The result is reported in Table 2. According to Table 2 we have achieved 95% confidence in the reported result on all criteria apart from What, When and Why. The result demonstrates that the current state of representation is providing close cognition scores. This reveals the fact that within the context of visualization-as-summarization, the mapping ofers a benefit due to the more compact representation.
An automatic understanding of authors’ rhetoric can be extremely useful for comprehensive tasks like abstract summarization or strategic story plan visualization during the learning and teaching process. Topic models [13] help us to analyze the news based on What the news is about. Name entity recognition [14] systems and classifiers [ 15] can help us to analyze news based on Where and Who. News timeline helps us to analyze the When. Story plan templates like Aristotle's Rhetoric, The Hero's Journey and 5Ws can aid extraction of the main story words for analyzing news by Why and How along with Who, Where, When, What. The evaluation of our theoretical mapping demonstrated a close human understanding of the compressed representation when compared to the whole text task results. [5] Y. Cao, R. Klamma, M. Jarke, The Hero’s Journey - Template-Based Storytelling for Ubiquitous Multimedia Management, Journal of Multimedia 6 (2011) 156–169. doi:10.4304/ jmm.6.2.156-169. [6] A. McKane, News writing, SAGE, London, 2006. [7] Z. Guo, M. Schlichtkrull, A. Vlachos, A Survey on Automated Fact-Checking, Transactions of the Association for Computational Linguistics 10 (2022) 178–206. doi:10.1162/tacl_a_ 00454. [8] M. A. Alonso, D. Vilares, C. Gómez-Rodríguez, J. Vilares, Sentiment analysis for fake news detection, Electronics 10 (2021) 1348. [9] N. Godbole, M. Srinivasaiah, S. Skiena, Large-scale sentiment analysis for news and blogs.,
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