Charlotte O. Brand’s research while affiliated with The University of Sheffield and other places

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Publications (4)


Figure 1: A dialogue excerpt from our dataset about veganism between a participant (P) and a Wizard (W).
Figure 3: Ratings for chat experiences for the argubot. The y-axis corresponds to the proportion of the dialogues, the x-axis corresponds to chat experiences and the different colors refer to the ratings on the 7-point Likert scale, where 1=strongly disagree and 7=strongly agree.
The percentage of dialogues that have zero, positive or negative OUM scores in the three OUM categories.
Opening up Minds with Argumentative Dialogues
  • Preprint
  • File available

January 2023

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66 Reads

Youmna Farag

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Charlotte O. Brand

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Jacopo Amidei

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[...]

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Andreas Vlachos

Recent research on argumentative dialogues has focused on persuading people to take some action, changing their stance on the topic of discussion, or winning debates. In this work, we focus on argumentative dialogues that aim to open up (rather than change) people's minds to help them become more understanding to views that are unfamiliar or in opposition to their own convictions. To this end, we present a dataset of 183 argumentative dialogues about 3 controversial topics: veganism, Brexit and COVID-19 vaccination. The dialogues were collected using the Wizard of Oz approach, where wizards leverage a knowledge-base of arguments to converse with participants. Open-mindedness is measured before and after engaging in the dialogue using a questionnaire from the psychology literature, and success of the dialogue is measured as the change in the participant's stance towards those who hold opinions different to theirs. We evaluate two dialogue models: a Wikipedia-based and an argument-based model. We show that while both models perform closely in terms of opening up minds, the argument-based model is significantly better on other dialogue properties such as engagement and clarity.

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Density plot of raw vaccination attitudes before and after the experiment. 7 = strongly agree and 1 = strongly disagree with the five vaccination items: (i) vaccines are safe, (ii) vaccines are effective, (iii) they have not been rushed, (iv) those who make them can be trusted; and (v) they are necessary.
Violin plot of average vaccination attitudes before and after the experiment. 1 = negative attitudes, 7 = positive attitudes. The green lines show an increase in positive vaccination attitudes, and red lines show a decrease.
Using dialogues to increase positive attitudes towards COVID-19 vaccines in a vaccine-hesitant UK population

October 2022

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61 Reads

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7 Citations

Recently, Altay et al. (Altay et al. 2021. J. Exp.Psychol.: Appl. (doi:10.1037/xap0000400)) showed that 5 min of interaction with a chatbot led to increases in positive COVID-19 vaccination attitudes and intentions in a French population. Here we replicate this effect in a vaccine-hesitant, UK-based population. We attempt to isolate what made the chatbot condition effective by controlling the amount of information provided, the trustworthiness of the information and the level of interactivity. Like Altay et al., our experiment allowed participants to navigate a branching dialogue by choosing questions of interest about COVID-19 vaccines. Our control condition used the same questions and answers but removed participant choice by presenting the dialogues at random. Importantly, we also targeted those who were either against or neutral towards COVID-19 vaccinations to begin with, screening-out those with already positive attitudes. Replicating Altay et al., we found a similar size increase in positive attitudes towards vaccination, and in intention to get vaccinated. Unlike Altay et al., we found no difference between our two conditions: choosing the questions did not increase vaccine attitudes or intentions any more than our control condition. These results suggest that the attitudes of the vaccine hesitant are modifiable with exposure to in-depth, trustworthy and engaging dialogues.



Three example screenshots representing what participants saw at different stages of the experiment
The top screenshot is an example question from the language topic. Participants could either select one of the two blue buttons showing two possible answers (one correct, one incorrect), or select the red button labelled “Ask Someone Else” which allows participants to copy someone else within their group. The number ‘7’ at the bottom is a countdown timer that forces participants to answer within 15 seconds. The second image represents what a participant would see if they chose to “Ask Someone Else” in Round 2 of Condition C, where they could choose to either view Times Chosen Altogether (domain-general prestige) or Times Chosen On This Topic (domain-specific prestige). The bottom image represents what a participant would see if they chose ‘Times Chosen Altogether’ and (domain-general prestige), in which there were only two other players to choose from. Please note that for any given question, participants could have between one and nine other participants to choose from, depending on how many answered individually for that particular question. See Table 1 for the information combinations displayed in the other Conditions. See S1 File for screenshots for all Conditions.
Raw counts of the information chosen when participants chose to copy someone else’s answer in Round 2
Total possible copying instances for each condition in Round 2 were: Condition A = 3240, Condition B = 3437, Condition C = 3377, Condition D = 3416.
Model predictions for participants choosing the predicted information compared to the alternative information in Round 2 of the four conditions, on the probability scale
Information displayed when choosing to “Ask Someone Else” in Rounds 1 and 2 across conditions, with our predicted choice for Round 2 in bold
Correlation coefficients between Round 1 and Round 2 topic scores representing total variance of Round 2 topic score explained by Round 1 score of the same topic
Trusting the experts: The domain-specificity of prestige-biased social learning

August 2021

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61 Reads

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20 Citations

Prestige-biased social learning (henceforth “prestige-bias”) occurs when individuals predominantly choose to learn from a prestigious member of their group, i.e. someone who has gained attention, respect and admiration for their success in some domain. Prestige-bias is proposed as an adaptive social-learning strategy as it provides a short-cut to identifying successful group members, without having to assess each person’s success individually. Previous work has documented prestige-bias and verified that it is used adaptively. However, the domain-specificity and generality of prestige-bias has not yet been explicitly addressed experimentally. By domain-specific prestige-bias we mean that individuals choose to learn from a prestigious model only within the domain of expertise in which the model acquired their prestige. By domain-general prestige-bias we mean that individuals choose to learn from prestigious models in general, regardless of the domain in which their prestige was earned. To distinguish between domain specific and domain general prestige we ran an online experiment (n = 397) in which participants could copy each other to score points on a general-knowledge quiz with varying topics (domains). Prestige in our task was an emergent property of participants’ copying behaviour. We found participants overwhelmingly preferred domain-specific (same topic) prestige cues to domain-general (across topic) prestige cues. However, when only domain-general or cross-domain (different topic) cues were available, participants overwhelmingly favoured domain-general cues. Finally, when given the choice between cross-domain prestige cues and randomly generated Player IDs, participants favoured cross-domain prestige cues. These results suggest participants were sensitive to the source of prestige, and that they preferred domain-specific cues even though these cues were based on fewer samples (being calculated from one topic) than the domain-general cues (being calculated from all topics). We suggest that the extent to which people employ a domain-specific or domain-general prestige-bias may depend on their experience and understanding of the relationships between domains.

Citations (2)


... A total of 12 studies published from 2019 to 2023 were analyzed and summarized (Altay et al., 2022;Amith et al., 2019Amith et al., , 2020Brand & Stafford, 2022;EI Ayadi et al., 2022;Hong et al., 2021;Kobayashi et al., 2022Kobayashi et al., , 2023Lee et al., 2022;Luk et al., 2022;Wang et al., 2023;Weeks et al., 2023) (Appendix C). Regarding the time of the studies, two were conducted before 2020 (before the COVID-19 pandemic), and ten were conducted in 2020 or the following years (during the COVID-19 pandemic). ...

Reference:

Effectiveness of chatbots in increasing uptake, intention, and attitudes related to any type of vaccination: A systematic review and meta-analysis
Using dialogues to increase positive attitudes towards COVID-19 vaccines in a vaccine-hesitant UK population

... In simple terms, a prestigious person would be one that is listened to, has influence on others, and whose opinions are heavily weighted because they enjoy credit, estimation or high standing in general opinion (Brand & Mesoudi 2019). In the social learning process, individuals are hypothesized to copy behaviors of individuals who are highly respected and admired in their social group, i.e. to copy 'prestigious' individuals (Henrich & Gil-White 2001, Brand et al. 2021. The evaluation and determination of prestige, however, varies depending on the context and object towards which it is directed, for instance, by social position, occupation, age, education, wealth and so on (Mesoudi 2011, Brand et al. 2021, Burdett et al. 2016, Chellappoo 2021. ...

Trusting the experts: The domain-specificity of prestige-biased social learning