Chatbot

About: Chatbot is a research topic. Over the lifetime, 2415 publications have been published within this topic receiving 24372 citations. The topic is also known as: IM bot & AI chatbot.

Overcoming polarization with chatbot news? Investigating the impact of news content containing opposing views on agreement and credibility:

Abstract: Chatbots are a burgeoning opportunity for news media outlets to disseminate their content in a conversational way, and create an engaging experience around it. Since chatbots are social and interactive technologies, they might be effective tools to lower the threshold of engaging with news content containing opposing views. In an experiment, we test this idea by investigating whether people are more likely to accept a news article containing conflicting views when it is delivered by a chatbot, as compared with the same article on a news website. The results indicated that people agreed more to a counter-attitudinal news article when it was delivered by a news chatbot (compared with the website article). In addition, users also perceived this chatbot article as more credible. The underlying process for this effect was that people attributed human-like characteristics to the chatbot on an implicit level (i.e., perceived mindless anthropomorphism). These results are discussed in the light of their potential contribution to an informed public discourse and a decrease in polarization in our society.

Occupational Disease Risk Assessment System Using Artificial Intelligence System and Chatbot

Abstract: This research aimed to study and develop a chatbot to assess the risk from occupational diseases among informal workers and to provide knowledge on disease prevention. There is a problem of accessing health services among informal workers or independent professional groups who do not have health benefit, lack professional healthcare, and lack knowledge about risk factors arising from their work. In addition, visiting a doctor in person takes time and informal workers may lose benefit from the work they do when doing so. Therefore, the researcher applied artificial intelligence to seek answers according to word groups based on word weight that classified symptoms, disease risk, and disease prevention guidelines for each occupation from diagnostic manuals. The system was developed to be compatible with the LINE application that is easily accessible. The evaluation of the system accuracy by experts had a total value of 86%. The evaluation of the overall system quality in 3 aspects had an average value of 4.24. It can be concluded that the developed system can be used in actual practice.

A turn to language: How interactional sociolinguistics informs the redesign of prompt:response chatbot turns

Abstract: This paper discusses how a microlevel linguistic analysis, using interactional sociolinguistics as an umbrella framework and drawing on analytical concepts from politeness theory and conversation analysis, can be used to advise chatbot designers on the interactional features contributing to problematic human user engagement as part of a consultancy project. Existing research using a microlevel linguistic analysis has analysed human user: bot interactions using natural language. This research has identified a central role for language which promotes sociability between the machine and users in the alignment of their goals and practices. However, there is no research currently which discusses how a microlevel linguistic analysis can help identify how the discursive construction of alignment and affiliation within prompt: response chatbots supports social presence and trust. This paper addresses this gap through an analysis of a database of prompt: response chatbot interactions which identified problematic sequences involving misalignment and disaffiliation, undermining human users’ trust and sense of social presence within the interaction. It also reports on how the consultancy project suggested changes to the programming of the chatbot which have potential to lead to improved user engagement and satisfaction.

Using a Telegram chatbot as cost-effective software infrastructure for ambulatory assessment studies with iOS and Android devices

Abstract: In this work, we present an innovative and cost-effective approach to run ambulatory assessment (AA) studies on participants' smartphones via Telegram Messenger. Our approach works both for Android and iOS devices. The population of potential participants in a given country or region consists of all individuals who (a) are in possession of a smartphone, (b) are willing to install Telegram Messenger, and (c) live in an environment providing constant connection to the Internet. In our new approach to AA, participants are asked to subscribe to a Telegram chatbot that provides them with links to brief surveys at specified points in time in their everyday lives via short notifications. We developed a user-friendly Python script that allows for the flexible editing of the chatbot's settings, e.g., the number of surveys per day. All common survey software designed for mobile devices can be used to present surveys to participants. This means that data collection takes place exclusively via the selected survey software, not via Telegram. With our approach, AA studies can be carried out among iOS and Android users cost-effectively and reliably while data security is ensured. Initial data from a pilot study show that studies of this kind are feasible, and the procedure is accepted by participants. Our Python script is licensed under General Public License (GPLv3) and therefore freely available and editable: https://github.com/Raze97/Telegram-Survey-Bot.