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Real conversations with artificial intelligence: A comparison between human-human conversations and human-chatbot conversations

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Abstract

This study analyzed how communication changes when people communicate with an intelligent agent as opposed to with another human. We compared 100 instant messaging conversations to 100 exchanges with the popular chatbot Cleverbot along seven dimensions: words per message, words per conversation, messages per conversation, word uniqueness, and use of profanity, shorthand, and emoticons. A MANOVA indicated that people communicated with the chatbot for longer durations (but with shorter messages) than they did with another human. Additionally, human-chatbot communication lacked much of the richness of vocabulary found in conversations among people, and exhibited greater profanity. These results suggest that while human language skills transfer easily to human-chatbot communication, there are notable differences in the content and quality of such conversations.

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... Claro, también ha habido críticas sobre el concepto "caja negra" y sus capacidades para entender a los algoritmos, especialmente porque reproducen una idea de "opacidad", aun cuando hay diferentes alternativas para visibilizarlos y entenderlos. Además, diferente a su uso convencional para referirse a tecnologías estables ya "empaquetadas o cerradas", los algoritmos son fluidos, maleables y cambiantes(BUCHER, 2016;GILLESPIE, 2013;SEAVER, 2013).En el caso de los chatbots, aunque ha habido una creciente literatura en el área, la mayoría de investigaciones solo analizan los factores que influencian la interacción humano-chatbot y las características de la misma desde abordajes psicológicos y experimentales(ARAUJO, 2018;HILL;FORD;FARRERAS, 2015;HO;HANCOCK;MINER, 2018;PÜTTEN et al., 2010;TAYLOR et al., 2014;XUETAO;BOUCHET;SANSONNET, 2009). ...
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... There is also the enduring popularity of Eliza, which is still used online (Heller et al., 2005) decades after its inception. Both observations are as true for early chatbot interaction as it is today, with researchers highlighting the persistent communication with chatbots many individuals choose to undertake (Hill, Ford, & Farreras, 2015). ...
... While this evidence was hopeful, it failed to push chatbots into the clear category of broadly useful for language practice. In contrast, Hill et al. (2015) analysed 100 messaging conversations and found that humans carried on significantly longer messaging conversations with the chatbot than with other humans. ...
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... There have been many studies which have looked into the many different factors which affect the perception of humanness by users, such as reciprocity, expressions of emotion, etc. [20,32,26,35]. The absolute majority of those studies are concentrated in how content influences the machine-likeness of a conversational system. ...
... Brennan et al. [6] tested the effects of message style on dialog and on people's mental models of computer agents. Hill [20] compared human-human online conversations and human conversations with the chatbot Cleverbot. Researchers found that people adapt their communication styles accordingly to the other conversant, regardless they are a machine or human. ...
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... Studies have been conducted on how people react to agents and avatars, in an attempt to establish why users have a social reaction towards them regardless of the knowledge that they are conversing with a machine. The authors [11] and [12] investigated the nature of the changes in people's communication when they interact with intelligent agents, as compared to human-to-human communication. ...
... 78% of the students found the avatar's tone of voice either very appealing, quite appealing or moderately appealing. This was unexpected, as we were not entirely satisfied with the quality of the avatar's voice and previous research [11], [12], [13] has found that subjects respond to artificial voices in the same way as they do with real human voices. ...
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... ‫(شكؿ‬ 3 ‫ا‬ ‫العدهٓة‬ ‫الفرضٓة‬ ‫فض‬ ‫ر‬ ‫تـ‬ ‫فقد‬ ، ) ‫لٍا،‬ ‫البدٓمة‬ ‫الفرضٓة‬ ‫كقبكؿ‬ ‫ئٓسٓة‬ ‫لر‬ ( ‫الدٚلة‬ ‫هستكل‬ ‫عىد‬ ‫إحصائٓة‬ ‫دٚلة‬ ‫ذات‬ ‫ٓة‬ ‫تأثٓر‬ ‫عٛقة‬ ‫تكجد‬ ‫أىً‬ ‫أم‬ α ≤ 0.05 ‫ة‬ ‫(القدر‬ ‫خصائص‬ ‫هابٓف‬ ) ‫كرضا‬ ‫هستقمة‬ ‫ات‬ ‫كهتغٓر‬ ‫الكفاءة)‬ ‫الهحادثة؛‬ ‫عمِ‬ ‫ة‬ ‫القدر‬ ‫شخصٓة؛‬ ‫أك‬ ‫ٌكٓة‬ ‫بىاء‬ ‫العاطفة؛‬ ‫إظٍار‬ ‫عمِ‬ ‫كه‬ ‫التفاعمٓة‬ ‫الدردشة‬ ‫ىاهج‬ ‫بر‬ ‫عف‬ ‫العهٛء‬ ‫الىحك‬ ‫عمِ‬ ‫التأثٓر‬ ‫أٌهٓة‬ ‫حٓث‬ ‫هف‬ ‫اهؿ‬ ‫العك‬ ‫تٓب‬ ‫تر‬ ‫ككاف‬ ‫تابع‬ ‫تغٓر‬ ‫اظٍرت‬ ‫حٓث‬ ‫الهحادثة).‬ ‫عمِ‬ ‫ة‬ ‫القدر‬ ‫شخصٓة؛‬ ‫أك‬ ‫ٌكٓة‬ ‫بىاء‬ ‫الكفاءة،‬ ‫العاطفة؛‬ ‫إظٍار‬ ‫عمِ‬ ‫ة‬ ‫التالِ(القدر‬ ‫العاطفة‬ ‫إظٍار‬ ‫عمِ‬ ‫ة‬ ‫القدر‬ ‫لخصائص‬ ‫آجابِ‬ ‫تاثٓر‬ ‫كجكد‬ ‫عمِ‬ ‫أكدت‬ ‫التِ‬ ‫اسات‬ ‫الدر‬ ‫بعض‬ ‫ىتائج‬ ‫هع‬ ‫افؽ‬ ‫تك‬Radziwill & Benton, 2017) ‫شخصٓة‬ ‫أك‬ ‫ٌكٓة‬ ‫بىاء‬ ‫)؛‬(Baylor & Rosenberg-Kima, 2003;Kerly, 2007;Radziwill & Benton, 2017; Knight,2017; Staven, 2017) ( ‫الهحادثة‬ ‫عمِ‬ ‫ة‬ ‫القدر‬ ‫)؛‬Farreras et al., 2015;Hill et al., 2015; Knight,2017 ( ‫الكفاءة‬ ‫)؛‬ Schlicht, 2016;Taylor ,2016;Van Manen,2016; Wei, 2016 ‫التفاعمٓة.‬ ‫الدردشة‬ ‫اهج‬ ‫لبر‬ ‫الهستخدهٓف‬ ‫رضا‬ ‫عمِ‬ ) ‫اختبار‬ ‫ىتائج‬ ‫أظٍرت‬ ‫قد‬ Independent sample t test ‫ه‬ ‫عٓىتٓف‬ ‫هتكسطْ‬ ‫بٓف‬ ‫لمفرؽ‬ ‫لمىكع‬ ‫ستقمتٓف‬ ‫دٚلة‬ ‫هستكل‬ ‫عىد‬ ‫إحصائٓة‬ ‫دٚلة‬ ‫ذات‬ ‫فركقات‬ ‫تكجد‬ ‫بأىً‬ ‫(الذككر,ا٘ىاث)‬ 0.05 ‫الهبحكثٓف‬ ‫استجابة‬ ‫فْ‬ ( ‫الجدكؿ‬ ‫فْ‬ ‫هبٓىة‬ ‫الىتائج‬ ‫ك‬ ‫الىكع‬ ‫لهتغٓر‬ ‫تعزل‬ 3 ‫ٓبٓف‬ ‫الذم‬ ‫ك‬ ) ‫قٓهة‬ ‫أف‬ T ‫اسة‬ ‫الدر‬ ‫أبعاد‬ ‫لجهٓع‬ ‫بالىسبة‬ ‫سالبة‬ ‫الدٚلة‬ ‫هستكل‬ ‫قٓهة‬ ‫كذلؾ‬ P ‫هف‬ ‫أقؿ‬ 0.05 ‫اٖ‬ ‫لكافة‬ ‫لهتغٓر‬ ‫تعزل‬ ‫ٓة‬ ‫جكٌر‬ ‫فركؽ‬ ‫كجكد‬ ‫ٓعىْ‬ ‫هها‬ ‫أٓضا‬ ‫بعاد‬ ‫دٚلة‬ ‫هستكل‬ ‫عىد‬ ‫الجىس‬ 0.05 ‫عىد‬ ‫إحصائٓة‬ ‫دٚلة‬ ‫ذات‬ ‫فركؽ‬ ‫ٌىاؾ‬ ‫أف‬ ‫أم‬ ‫البدٓمة‬ ‫الفرضٓة‬ ‫قبكؿ‬ ‫كبالتالْ‬ ‫هستكل‬ 0.05 ‫الجىس‬ ‫لهتغٓر‬ ‫تعزل‬ ‫الهبحكثٓف‬ ‫استجابة‬ ‫فْ‬ , ‫ىتائج‬ ‫هع‬ ‫ٓتفؽ‬ ‫ها‬ ‫كٌك‬ ‫ا٘ىاث‬ ‫كلصالح‬ ( ‫السابقة‬ ‫اسات‬ ‫الدر‬ ...
... Contrary to our approach focused on platform comparison, Kuligowska (2015) focuses on the comparison of particular chatbot deployments. Hill et al. (2015) focus on an analysis of differences between human-chatbot conversation and humanhuman conversation. This study analyzed seven variables (words per conversation, messages per conversation, average number of words per message, etc.) from real human-human and humanchatbot conversations. ...
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ELIZA is a program operating within the MAC time-sharing system of MIT which makes certain kinds of natural language conversation between man and computer possible. Input sentences are analyzed on the basis of decomposition rules which are triggered by key words appearing in the input text. Responses are generated by reassembly rules associated with selected decomposition rules. The fundamental technical problems with which ELIZA is concerned are: (1) the identification of key words, (2) the discovery of minimal context, (3) the choice of appropriate transformations, (4) generation of responses in the absence of key words, and (5) the provision of an editing capability for ELIZA “scripts”. A discussion of some psychological issues relevant to the ELIZA approach as well as of future developments concludes the paper. © 1983, ACM. All rights reserved.
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