Diogo Nazareno Almeida de Melo
ABSTRACT
With the COVID-19 pandemic, governments have used applications to help citizens to be informed about the disease. Some applications use chatbots, which despite the advances, still represent challenges for researchers and designers in the domain of human-computer interaction (HCI). Therefore, this paper aims to evaluate three Brazilian government chatbots focused on COVID-19, considering two perspectives: communication (which involves communicability and language) and personality. For this, we used the Semiotic Inspection Method (SIM) to evaluate the chatbots' communicability and best practice guidelines for evaluating the chatbots' language and personality. As a result, we show that chatbots make little use of communicability strategies. In addition, they do not convey personality traits. We also discussed the relationship between personality and the designer's self-expression, making room for potential future work in the area. The research contributes to the extent that the study of COVID-19 chatbots in the current pandemic scenario is important and presents a methodology for assessing the language and personality of chatbots.
- Mauricio Agudelo, Eduardo Chomali, Jesus Suniaga, Georgina Nunez, Valeria Jordan, Fernando Rojas, Jorge Negrete, Jorge Bravo, Paula Bertolini, Raúl Katz, and others. 2020. Las oportunidades de la digitalización en América Latina frente al COVID-19. (2020).Google Scholar
- Anderson N. Alves. 2019. Autoexpressão do designer do Nubank: um estudo de caso no aplicativo. Trabalho de Conclusão de Curso. Universidade Federal do Ceará (UFC), Quixadá, Brazil. In Portuguese.Google Scholar
- Aggeliki Androutsopoulou, Nikos Karacapilidis, Euripidis Loukis, and Yannis Charalabidis. 2019. Transforming the communication between citizens and government through AI-guided chatbots. Government Information Quarterly 36, 2 (2019), 358--367.Google ScholarCross Ref
- Naomi Aoki. 2020. An experimental study of public trust in AI chatbots in the public sector. Government Information Quarterly 37, 4 (2020), 101490.Google Scholar
- Gopi Battineni, Nalini Chintalapudi, and Francesco Amenta. 2020. AI Chatbot design during an epidemic like the novel coronavirus. In Healthcare, Vol. 8. Multidisciplinary Digital Publishing Institute, 154.Google Scholar
- Petter Bae Brandtzaeg and Asbjørn Følstad. 2018. Chatbots: changing user needs and motivations. Interactions 25, 5 (2018), 38--43.Google ScholarDigital Library
- Zoraida Callejas, Ramón López-Cózar, Nieves Ábalos, and David Griol. 2011. Affective conversational agents: the role of personality and emotion in spoken interactions. In Conversational agents and natural language interaction: Techniques and effective practices. IGI Global, 203--222.Google Scholar
- Heloisa Candello and Claudio Pinhanez. 2016. Designing Conversational Interfaces. Simpósio Brasileiro sobre Fatores Humanos em Sistemas Computacionais-IHC (2016).Google Scholar
- Ana Paula Chaves and Marco Aurelio Gerosa. 2020. How Should My Chatbot Interact? A Survey on Social Characteristics in Human-Chatbot Interaction Design. International Journal of Human-Computer Interaction (2020), 1--30.Google ScholarCross Ref
- Ministério da Saúde. 2020. Coronavírus: o que você precisa saber e como prevenir o contágio. https://coronavirus.saude.gov.br. (2020).Google Scholar
- Luciana da Silveira Espindola and Milene Selbach Silveira. 2017. Self-expression and discourse continuity in a multilevel EUD environment: The case of moodle. Journal of Visual Languages & Computing 40 (2017), 36--50.Google ScholarDigital Library
- Kate Daley, Ines Hungerbuehler, Kate Cavanagh, Heloísa Garcia Claro, Paul Alan Swinton, and Michael Kapps. 2020. Preliminary evaluation of the engagement and effectiveness of a mental health chatbot. Frontiers in digital health 2 (2020), 41.Google Scholar
- Antonella De Angeli, Graham I Johnson, and Lynne Coventry. 2001. The unfriendly user: exploring social reactions to chatterbots. In Proceedings of the international conference on affective human factors design, london. Citeseer, 467--474.Google Scholar
- Centro de Operações de Emergência em Saúde Pública. 2020. Plano de contingência do Estado de São Paulo para infecção humana pelo novo coronavírus-2019-nCoV. (2020).Google Scholar
- Secretaria de Saúde do Estado de Sergipe. 2020. Plano de contingência estadual para infecção humana pelo novo coronavírus 2019-nCoV. (2020).Google Scholar
- Secretaria de Saúde do Estado do Ceará. 2020. Plano estadual de contingência para resposta às emergências em saúde pública: novo coronavírus (2019-nCoV). (2020).Google Scholar
- Clarisse Sieckenius De Souza. 2005. The semiotic engineering of human-computer interaction. MIT press.Google Scholar
- Clarisse Sieckenius De Souza, Carla Faria Leitão, Raquel Oliveira Prates, and Elton José Da Silva. 2006. The semiotic inspection method. In Proceedings of VII Brazilian symposium on Human factors in computing systems. 148--157.Google ScholarDigital Library
- Clarisse S De Souza, Raquel O Prates, and Simone DJ Barbosa. 1999. A method for evaluating software communicability. Monografias em Ciência da Computação. Departamento de Informática. PUC-RioInf 1200, 1 (1999), 11--99.Google Scholar
- Kohji Dohsaka, Ryota Asai, Ryuichiro Higashinaka, Yasuhiro Minami, and Eisaku Maeda. 2014. Effects of conversational agents on activation of communication in thought-evoking multi-party dialogues. IEICE TRANSACTIONS on Information and Systems 97, 8 (2014), 2147--2156.Google ScholarCross Ref
- Tovi Grossman, George Fitzmaurice, and Ramtin Attar.2009. A survey of software learnability: metrics, methodologies and guidelines. In Proceedings of the sigchi conference on human factors in computing systems. 649--658.Google Scholar
- Sviatlana Hoehn and Kerstin Bongard. 2020. Heuristic Evaluation of COVID-19 Chatbots. Proceedings of CONVERSATIONS 2020 (2020).Google Scholar
- Lia Maulia Indrayani, Rosaria Mita Amalia, and Fauzia Zahira Munirul Hakim. 2020. Emotive Expressions on Social Chatbot. Jurnal Sosioteknologi 18, 3 (2020), 509--516.Google ScholarCross Ref
- Mohit Jain, Pratyush Kumar, Ramachandra Kota, and Shwetak N Patel. 2018. Evaluating and informing the design of chatbots. In Proceedings of the 2018 Designing Interactive Systems Conference. 895--906.Google ScholarDigital Library
- Yanran Li, Hui Su, Xiaoyu Shen, Wenjie Li, Ziqiang Cao, and Shuzi Niu. 2017. Dailydialog: A manually labelled multi-turn dialogue dataset. arXiv preprint arXiv:1710.03957 (2017).Google Scholar
- Helena Lindgren, Esteban Guerrero, and Rebecka Janols. 2017. Personalised persuasive coaching to increase older adults' physical and social activities: a motivational model. In International Conference on Practical Applications of Agents and Multi-Agent Systems. Springer, 170--182.Google ScholarCross Ref
- Andreas Lommatzsch. 2018. A next generation chatbot-framework for the public administration. In International Conference on Innovations for Community Services. Springer, 127--141.Google ScholarCross Ref
- Theodora A Maniou and Andreas Veglis. 2020. Employing a chatbot for news dissemination during crisis: Design, implementation and evaluation. Future Internet 12, 7 (2020), 109.Google ScholarCross Ref
- Robert R McCrae and Paul T Costa Jr. 1997. Personality trait structure as a human universal. American psychologist 52, 5 (1997), 509.Google Scholar
- Michael McTear, Zoraida Callejas, and David Griol. 2016. Affective conversational interfaces. In The Conversational Interface. Springer, 329--357.Google Scholar
- Adam S Miner, Liliana Laranjo, and A Baki Kocaballi. 2020. Chatbots in the fight against the COVID-19 pandemic. NPJ digital medicine 3, 1 (2020), 1--4.Google Scholar
- Ingrid T.Monteiro. 2015. Autoexpressão e engenharia semiótica do usuário-designer. Ph.D. Thesis. Pontifícia Universidade Católica do Rio de Janeiro (PUC), Rio de Janeiro, Brazil. In Portuguese.Google Scholar
- Luana Müller, Milene Selbach Silveira, and Clarisse Sieckenius de Souza. 2015. Mine, Yours, Ours: Examples Reuse and the Self-Expression of Programming Students. In Proceedings of the 14th Brazilian Symposium on Human Factors in Computing Systems (IHC '15). Association for Computing Machinery, New York, NY, USA, Article 30, 10 pages. DOI:https://doi.org/10.1145/3148456.3148486Google ScholarDigital Library
- Nicolas Munsch, Alistair Martin, Stefanie Gruarin, Jama Nateqi, Isselmou Abdarahmane, Rafael Weingartner-Ortner, and Bernhard Knapp. 2020. A benchmark of online COVID-19 symptom checkers. medRxiv (2020).Google Scholar
- Raquel O Prates, Clarisse S De Souza, and Simone DJ Barbosa. 2000. Methods and tools: a method for evaluating the communicability of user interfaces. interactions 7, 1 (2000), 31--38.Google Scholar
- Peter Salovey and John D Mayer. 1990. Emotional intelligence. Imagination, cognition and personality 9, 3 (1990), 185--211.Google Scholar
- Anna-Maria Seeger and Armin Heinzl. 2018. Human versus machine: Contingency factors of anthropomorphism as a trust-inducing design strategy for conversational agents. In Information systems and neuroscience. Springer, 129--139.Google Scholar
- Tuva Lunde Smestad and Frode Volden. 2018. Chatbot personalities matters. In International Conference on Internet Science. Springer, 170--181.Google Scholar
- Pol Pérez Sust, Oscar Solans, Joan Carles Fajardo, Manuel Medina Peralta, Pepi Rodenas, Jordi Gabaldà, Luis Garcia Eroles, Adrià Comella, César Velasco Muñoz, Josuè Sallent Ribes, and others. 2020. Turning the crisis into an opportunity: digital health strategies deployed during the COVID-19 outbreak. JMIR public health and surveillance 6, 2 (2020), e19106.Google Scholar
- Navid Tavanapour, Mathis Poser, and E. Bittner. 2019. Supporting the Idea Generation Process in Citizen Participation - toward an Interactive System with a Conversational Agent as Facilitator. In ECIS.Google Scholar
- James A Thorson and FC Powell. 1993. Sense of humor and dimensions of personality. Journal of clinical Psychology 49, 6 (1993), 799--809.Google ScholarCross Ref
- Francisco AM Valério, Tatiane G Guimarães, Raquel O Prates, and Heloisa Candello. 2017. Here's What I Can Do: Chatbots' Strategies to Convey Their Features to Users. In Proceedings of the xvi brazilian symposium on human factors in computing systems. 1--10.Google ScholarDigital Library
- WHO. 2020. Coronavirus. https://www.who.int/health-topics/coronavirus. (2020). Accessado: 05-05-2020.Google Scholar
- WHO. 2020. Coronavirus disease 2019 (COVID-19): situation report 51. https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200311-sitrep-51-covid-19.pdf. (2020). Accessado: 05-05-2020.Google Scholar
- Marvin Zuckerman, D Michael Kuhlman, Jeffrey Joireman, Paul Teta, and Michael Kraft. 1993. A comparison of three structural models for personality: the big three, the big five, and the alternative five. Journal of personality and social psychology 65, 4 (1993), 757.Google ScholarCross Ref
Index Terms
- Communication and Personality: how COVID-19 government chatbots express themselves
Recommendations
Here's What I Can Do: Chatbots' Strategies to Convey Their Features to Users
IHC '17: Proceedings of the XVI Brazilian Symposium on Human Factors in Computing SystemsChatbots have been around since the 1960's, but recently they have risen in popularity especially due to new compatibility with social networks and messenger applications. Chatbots are different from traditional user interfaces, for they unveil ...
The Power of Personalization: A Systematic Review of Personality-Adaptive Chatbots
AbstractConversation serves as a crucial means for individuals to express their intentions, feelings, attitudes, and personalities, making natural conversational interaction a vital technical competency for conversational agents. With the growing ...
User Perception of Text-Based Chatbot Personality
Chatbot Research and DesignAbstractThis work explores the effect of chatbot personality on user experience and investigates how users perceive agent personality when conveyed through text. Building on previous work in the field of human-computer interaction on designing chatbot ...
Comments