Hi, I'm Sarah and I’m going to present our paper
on User Perceptions of Extraversion in Chatbots after Repeated Use.
When we meet another person, we immediately and automatically form an impression of their
personality, which significantly influences our behaviour and expectations towards
that person.
As chatbots are perceived as social actors, they elicit similar personality judgements.
That is, users subconsciously assign chatbots a personality regardless of whether this was
intended by the designer or not.
Similar to human-human interaction, this personality assignment has been shown to influence attitude,
expectations, and behaviour, such as trust or consumer spending.
However, commercially available chatbots have so far taken a one-size-fits-all approach,
ignoring the potential benefits that deliberate personality manipulation and personalisation
may bring.
A reason for this could be that systematically synthesising chatbot personality is quite challenging.
The most predominant model to describe human personality is the so called Big Five model,
which describes personality on five broad dimensions.
In this project, we focused on the personality dimension Extraversion.
Human personality manifests itself in observable behaviour cues, for example choice of words
or linguistic style.
That is, extraverts and introverts talk differently.
To systematically generate different chatbot personalities, we transferred these human
behaviour cues to our chatbots.
Behaviour cues have been leveraged in prior work to imbue voice assistants and robots
with personality, however, little is known about whether verbal cues alone are sufficient
to evoke the intended personality traits.
We address this gap by examining three research questions:
First, can we create different levels of Extraversion in a chatbot by using verbal cues from human behaviour?
Second, which level of Extraversion do users prefer after repeated use of the chatbots?
And third, does user personality influence their preference for the chatbot personality?
In this talk, we will focus on the first two research questions.
To this end, we built three Telegram chatbots with different levels of Extraversion.
We scripted the chatbots’ responses and systematically varied the language based on
human behaviour cues as informed by psycholinguistic literature.
For example, the introverted chatbot uses more formal language and a broad vocabulary
with infrequent words.
In contrast, the extraverted chatbot is more talkative and uses fillers, emojis, as well
as positively valanced words.
We situated the three chatbots in a stress tracking and reflection application due to
the growing availability of chatbots for health-related support.
As adapting chatbots to users seems particularly meaningful in long term interaction, we asked
34 participants to interact with the three chatbots on their personal smartphones,
each over the course of four days.
On the first three days, the chatbot went through a questionnaire once a day to find out
whether participants had experienced any stressors.
On the fourth day, the chatbot sent a feedback report
and afterwards, we asked participants to evaluate the chatbot’s personality and the interaction
in an online questionnaire.
On the following day, participants then interacted with the next chatbot.
Before the study, we pre-screened participants to ensure that our samples comprises users
with low, average, and high levels of Extraversion After participants had interacted with all
three chatbots, they completed a final post-interaction questionnaire,
in which they ranked the three chatbots.
Let’s turn to our first research question: how did participants perceive the personality
of the three chatbots?
After four days of interacting with a chatbot, we asked participants to rate the chatbot’s
personality using a standardised human personality inventory.
Our findings show that participants perceived the extraverted and average chatbots as such.
However, human verbal cues were insufficient to infuse introversion.
A reason for this result could be that we kept several interaction variables constant
across the three chatbots.
For example, a ``truly introverted chatbot'' might not prompt the user to complete the
daily questionnaire but only reacts if the user initiates the conversation.
Before participants rated the chatbot on the personality inventory, we asked them to describe
the chatbot’s personality in a free-text field.
We systematically mapped these open personality descriptions to the Big Five personality traits.
The most salient personality traits of the introverted chatbot referred to its Conscientiousness
and Agreeableness. For example, it was characterised as professional, formal, and understanding.
The same dimensions also stood out for the average chatbot, which was mainly perceived
as being straight to the point.
In contrast, participants stressed the friendly, helpful, casual, and upbeat demeanour of the
extraverted chatbot, thereby describing it as high in Agreeableness and Extraversion.
Summing up, conversation designers should keep in mind that Agreeableness and Conscientiousness
were most salient for participants.
Outside of the Big Five personality dimensions, the average chatbot was often discerned as
being robotic and automated.
However, these differences in the perceived artificiality would not have been possible
to decipher by means of a human personality questionnaire alone. Because such a questionnaire
obviously doesn’t ask how human-like a person is.
Hence, our results suggest that human personality inventories might not be an adequate measurement
tool to assess chatbot personality.
We were also interested in which chatbot personalities our participants liked best.
On average, participants showed the greatest desire to interact again with the extraverted chatbot.
Participants’ final ranking of the three
chatbots corroborates this.
While the majority ranked the extraverted chatbot first,
the average chatbot was the least liked.
As reasons for this ranking, participants in particular singled out the friendliness
of the extraverted chatbot, thereby highlighting the importance of Agreeableness for user preferences.
This adds much-needed information as to which personality traits chatbot designers should consider.
On the other hand, our results also show that users have very individual preferences for chatbot
personality.
As I mentioned before, participants who favoured the extraverted chatbot emphasised its friendliness,
and also its informal language and emoji use.
These characteristics are precisely the reasons, however, why the other participants rejected it.
While our results show a subjective preference for the extraverted chatbot, participants
disclosed more words to the introverted one.
A reason for this could be that the introverted chatbot was perceived as being very professional
and understanding.
Summing up, our main findings were that verbal cues transferred from human behaviour
were insufficient to create an introverted chatbot.
Second, our results indicate that chatbot personality determines users’ subjective
preference and interaction behaviour, underlining the benefits of deliberately manipulating personality.
Furthermore, it became clear that one size
in chatbot personality does not fit all users: Instead, users have individual preferences for chatbot
personalities.
And finally, human personality inventories might be inadequate to assess chatbot personality.
Thank you very much for watching our video.
If you are interested in our research, please reach out to us any time. Thank you.