Changing views: Persuasion modeling and argument extraction from online discussions

Abstract

Persuasion and argumentation are possibly among the most complex examples of the interplay between multiple human subjects. With the advent of the Internet, online forums provide wide platforms for people to share their opinions and reasonings around various diverse topics. In this work, we attempt to model persuasive interaction between users on Reddit, a popular online discussion forum. We propose a deep LSTM model to classify whether a conversation leads to a successful persuasion or not, and use this model to predict whether a certain chain of arguments can lead to persuasion. While learning persuasion dynamics, our model tends to identify argument facets implicitly, using an attention mechanism. We also propose a semi-supervised approach to extract argumentative components from discussion threads. Both these models provide useful insight into how people engage in argumentation on online discussion forums.

Persuasion Action Strategies in Da'wah Discourse on Social Media in the Global Communication Era

“…Persuasion with the use of argumentative content can increase the acceptance of the message conveyed. This is following what Dutta et al, (2020) have stated that persuasion has a close relationship with the ability of argumentation to convince others to change according to the persuasion message conveyed.…”

“…Persuasion is a person's attempt to influence others in terms of understanding, attitudes, and actions. In this connection, persuasion is also closely related to the ability of argumentation to convince others to change according to the persuasion message conveyed (Dutta, Das, & Chakraborty, 2020). Therefore, persuasion has a close relationship between speakers, writers, speech partners, or readers (Fanani, Setiawan, Purwati, Maisarah, & Qoyyimah, 2020).…”

Which Message? Which Channel? Which Customer? - Exploring Response Rates in Multi-Channel Marketing Using Short-Form Advertising

“…However, other research indicates that both emotional (G. Li, Lin, Yu, & Guo, 2020) and rational messaging (i.e., message based on reason, logic, or facts without emotional embellishment) have an impactful role, with the emotional component appearing preeminent (Nicolini, Cassia, & Bellotto, 2017). Several factors are found to be relevant to the effects of persuasive messages (Dutta, Das, & Chakraborty, 2020). Individual differences exist in reactivity to emotion-inducing messages.…”

Exploiting Open Data to analyze discussion and controversy in online citizen participation

“…First, Natural Language Processing and Opinion Mining techniques may be applied to analyze the citizens comments on proposals. Among others, opinion lexicons (Hubert et al, 2018), controversy vocabularies (Mejova et al, 2014;Roitman et al, 2016), language models (Jang et al, 2016) and word embeddings (Rethmeier et al, 2018), and argument extraction methods and tools (Shum et al, 2008;Lytos et al, 2019;Dutta et al, 2019) could be used to extract statements and claims in favor and against each proposal, and hence, achieving a better understanding of the most important and urgent citizen needs, as well as the underlying discussions and controversies. Moreover, applying machine learning to automatically classify and predict the relevance and levels of discussion and controversy (Jang et al, 2016;Rad and Barbosa, 2012) of citizen proposals represents a research line whose results would be of special interest for government decision and policy making.…”