J SYST SCI SYST ENG Vol. 30, No. 4, August 2021, pp. 450–464 ISSN: 1004-3756 (paper), 1861-9576 (online) DOI: https://doi.org/10.1007/s11518-021-5497-1 CN 11-2983/N Argumentative Conversational Agents for Online Discussions Rafik Hadfi,a Jawad Haqbeen,b Sofia Sahab,a Takayuki Itoa aDepartment of Social Informatics, Kyoto University, Yoshidahonmachi, Sakyo Ward, Kyoto, Japan 606-8501 rafik.hadfi@i.kyoto-u.ac.jp (), sahab.sofi[email protected], [email protected] bNagoya Institute of Technology, Gokiso-cho, Showa-ku, Nagoya, Aichi, Japan 466-8555 [email protected] Abstract. Artificial Intelligence is revolutionising our communication practices and the ways in which we interact with each other. This revolution does not only impact how we communicate, but it affects the nature of the partners with whom we communicate. Online discussion platforms now allow humans to communicate with artificial agents in the form of socialbots. Such agents have the potential to moderate online discussions and even manipulate and alter public opinions. In this paper, we propose to study this phenomenon using a constructed large-scale agent platform. At the heart of the platform lies an artificial agent that can moderate online discussions using argumentative messages. We investigate the influence of the agent on the evolution of an online debate involving human participants. The agent will dynamically react to their messages by moderating, supporting, or attacking their stances. We conducted two experiments to evaluate the platform while looking at the effects of the conversational agent. The first experiment is a large-scale discussion with 1076 citizens from Afghanistan discussing urban policy-making in the city of Kabul. The goal of the experiment was to increase the citizen involvement in implementing Sustainable Development Goals. The second experiment is a small-scale debate between a group of 16 students about globalisation and taxation in Myanmar. In the first experiment, we found that the agent improved the responsiveness of the participants and increased the number of identified ideas and issues. In the second experiment, we found that the agent polarised the debate by reinforcing the initial stances of the participant. Keywords: Artificial intelligence, conversational agents, natural language processing, online discussion, computational social science 1. Introduction widespread use of such chatbots, their devel- opment raises important ethical questions like The field of Artificial Intelligence (AI) is grad- their deliberative nature, moral accountability, ually changing the ways in which humans in- and the extent of their polarising capabilities. teract with each other. Most importantly, it Such issues need to be addressed at this early is changing the nature of the partners with stage, and the design choices made by the de- whom we communicate. It is currently pos- velopers of such agents need to be carefully sible to communicate with artificial agents in scrutinised. the forms of virtual assistants, socialbots, or chatbots. Recent studies have shown that online plat- With the increasing sophistication of Natu- forms are vulnerable to deceptive automated ral Language Generation (NLG) techniques, it activities. For instance, Stella et al. (2018) is not inconceivable for humans to forget that showed that socialbots deliberately targeted they are actually interacting with a machine, central election hubs with inflammatory con- in what is known as the “pretended intimacy” tent during the 2017 Catalan independence (Weizenbaum 1966). While it is believed that it referendum. Similarly, Ferrara (2017) inves- will take many years until we start seeing the tigates the disinformation campaign that has 450 © Systems Engineering Society of China and Springer-Verlag GmbH Germany 2021 Hadfi et al.: Argumentative Conversational Agents for Online Discussions 451 been coordinated by means of socialbots dis- versational agents and their effects in online guising themselves as legitimate human users discussions. In section 3, we employ a method- during the 2017 French presidential election. ology based on an artificial agent and a social Shao et al. (2018) analysed 14 million messages experiment. In section 4, we present the re- spreading 400 thousand articles on Twitter and sults of the experiment. Finally, we summarise found evidence that socialbots played a dis- our major findings and provide future research proportionate role in spreading articles from directions. low-credibility sources. In an attempt to char- acterise such bots and their impact on social 2. Related Work media, Varol et al. (2017) presented a frame- Discussion platforms are considered as the work for the detection of socialbots on Twitter. next-generation democratic platforms for cit- In this light, we propose to investigate how izen deliberation. Such platforms could inte- an elaborate conversational agent could gradu- grate ideas, opinions, and could lead to en- ally influence the evolution of an online debate. hanced consensuses (Malone 2018, Malone and We particularly look at the persuasive conse- Klein 2007). For instance, the Collagree plat- quences associated with an agent that uses ar- form was employed for opinion gathering and gumentative cues to influence the opinions of city planning in Japan (Ito et al. 2019 2014 human debaters. The debaters start by tak- 2015). The CoLab platform was used to har- ing a stance on a predefined theme and then nesses the collective intelligence of thousands try to elaborate and defend their positions by of people worldwide to address global climate posting their ideas and arguments on an on- change (Malone and Klein 2007). The Deliber- line forum. The agent will adaptively reply to atorium is another platform where people sub- their messages by mediating the discussion or mit ideas by following an argumentation map by supporting or attacking the users that agree that frames the ideas within a given discussion (or disagree) with the main stance. This work structure (Iandoli et al. 2007). has two main contributions. Such platforms are also being used to em- 1. A platform centred around an intelligent power citizens and help implementing sustain- conversational agent that uses Natural able goals (Savaget et al. 2019). For instance, Language Processing, Natural Language the D-Agree platform was employed to col- Generation, and argumentative reason- lect opinions and the implementation of Sus- ing to interact with humans in online dis- tainable Development Goals in Afghanistan cussions. (Haqbeen et al. 2020a). Another work has re- 2. A study on the effect of polarised and cently used the same platform to fight COVID- non-polarised conversational agents in 19 by collecting and analysing vast amounts online discussions between human par- of social data to increase public awareness and ticipants. for public health policy-making (Haqbeen et al. The results suggest that the agent could in- 2020b). crease the responsiveness of the participants In practice, sophisticated discussion plat- and their ability to identify ideas and issues. forms combine algorithmic methods and ma- Second, when the debaters have prior knowl- chine learning techniques to harness the intel- edge of the issue, their stances do not change ligence of the crowd. In our work, we par- under the effect of a bipolarised agent. ticularly focus on the use of artificial intelli- The paper is structured as follows. In sec- gent agents for their ability to adapts to human tion 2, we cover the literature relative to con- behaviour and to the problems at hand. In 452 Hadfi et al.: Argumentative Conversational Agents for Online Discussions this case, a conversational agent is defined as with other artificial agents. Any knowledge a computer program that is designed to inter- that is required for such decisions may be act with users using natural language in ways missing, incoherent or conflicting. Formal that mimic human conversation. Most of the argumentation is a viable approach for han- existing chatbots utilise algorithms to gener- dling conflicting opinions and beliefs. It is the ate adequate responses. The earlier versions process by which arguments are constructed, of conversational agents merely created an il- compared, and evaluated in order to establish lusion of intelligence by employing much sim- whether any of them are justifiable. Argumen- pler pattern matching and rule-based models tation is inherent to human reasoning and to in their interaction with users. However, with our native ability to decide collectively about the emergence of new technologies, more in- a problem. It is therefore important to con- telligent systems have emerged with learning ceive of an autonomous conversational agent methodologies and knowledge-based models. that can exploit argumentation theories in or- Conversational agents are generally classified der to reason about complex problems. Ar- into categories based on the knowledge do- gumentation has been applied in various do- main, the mode of interaction, and the design mains and its applications range from deci- aspects. Overall, we distinguish task oriented sion making to negotiation (Dung 1995, Fox agents and non-task oriented agents (Chen and Parsons 1997, Amgoud and Parsons 2000). et al. 2017, Yan et al. 2017). Task-oriented In general, an argumentation process consists agents are designed for a particular task and in the construction of the arguments, the def- are set up to have short conversations, usu- inition of the interactions between the argu- ally within a closed domain such as online ments, the evaluation of each argument, the shopping, customer support, or medical ex- selection of the acceptable arguments, and the pertise. When the task is not specific, a non- conclusion. An interesting type of interaction task oriented agent can simulate a conversa- between arguments is the case where an argu- tion with a person for entertainment purposes ment can defeat or support another argument. in open domains (Hussain et al. 2019). Many These two independent types of information approaches could be employed when building suggest a notion of bipolarity illustrated by the task-oriented conversational agents.