Hi, I’m Fu-Yin Cherng.
I’m going to share our work, Understanding Social Influence in Collective Product Ratings
Using Behavioral and Cognitive Metrics.
Viewing collective opinions provided by others and providing personal opinions are everyday
online interactions.
For users, we often use social information as collective intelligence to support decision
making.
Previous research also found that there is a strong correlation between sales figures
and reviews of products.
For service providers, they often use these user-generated ratings to understand what
customers want.
Moreover, they can generate personalized recommendations to users by modeling individual preferences
through collective ratings from a large group of users.
Although the usefulness of collective information, previous research found some social bias in
the collective ratings.
For example, people tend to lower their preference after viewing negative ratings from others,
even when they didn’t know the reasons behind the negative ratings.
Research in social science suggested that social conformity is one important reason
of this social bias.
Social conformity indicates that people align their own opinions, decisions, and behaviors
to those of others.
This figure illustrates the most classic experiment of social conformity.
This experiment found that participants conform to the wrong answers given by the others,
even the innocent participants knew the correct answer.
There are two motivations behind such phenomenon.
The first one is human’s natural desire of social approval, and the second is the
dependency on the crowd’s opinion for more information.
For example, we may change our opinions or behaviors to fit in or be liked by the group.
Or, we would tend to believe that a statement agreed by the majority is correct.
The consequence of social conformity can be substantial.
The influence of social conformity will still affect an individual’s decisions even when
there is no social interaction presented.
From the perspective of service providers, these potential social biases would lead to
incorrect decisions and potentially the issue of algorithmic fairness.
Although there are several previous related research, most of them observe the social
influence on users still through behavioral and self-report measures.
However, the effects of collective social information on users are not always explicit.
Moreover, different cognitive processes can sometimes lead to similar feedback.
For example, when users reported “I like it” after viewing collective information,
Knowing their emotion can help us distinguish whether users truly change their attitudes.
Or just comply without altering their opinion authentically.
So, knowing users’ cognitions and affective experience can help us understand the critical
factors associated with the decision of conforming.
In this study, we decide to investigate social conformity using the real-world materials
we collected from a popular e-commerce platform, Amazon.
We collected 90 products from the new arrival products in the smart home.
For the social information, we collected the rating distribution and the titles of the
most positive and critical reviews of each product.
There are two phases in the experiment.
Participants will rate their preference to the products we collected in the first phase.
In the second phase, participants will rate the same products in different order.
The products are distributed into three conditions with display of different types of social
information The purpose of this experiment design is to
compare if participants change their first phase preference in the second phase after seeing information
of others.
For the preference rating in the first phase, participants saw a product’s photo and title,
and then rate their preference in a 5-point scale.
In the second phase, the first condition is the no-display condition, in which we don’t
display any information of others.
So, in the no-display condition, the participants did the same task as they did in the first
phase.
In the next condition, the limited-display condition, the participants will see the rating
distribution of others’ preferences after viewing the product information.
Then, they will rate their preference.
Finally, in the full-display condition, the title of the most positive and the critical
reviews are displayed after showing the rating distribution, and then the participants rate
their preference.
Through the experiment design, we can observe the influence of different combinations and
displays of others’ opinions on participants’ preferences.
We want to know if participants make different preference ratings between the first phase
and the different conditions in the second phase.
Moreover, we were also curious if participants experienced different cognitive states in
different conditions.
In this experiment, we recruited 30 participants, and this photo displays our experimental setting.
In this work, we used the portable EEG headset with a VR display, which is easy to use and
able to measure the cognitive responses we want to get.
We also recorded participants’ reaction time of rating, and preference rating at the
first and the second phase.
The first result I want to introduce is the results of preference ratings
The value in the y-axis is the score we computed to represent the level of social conformity
based on the change of preference ratings between phase 1 and phase 2.
The higher score means that our participants make more changes in their rating toward others.
The result of the conformity score shows that participants have a higher score under the
full-display condition than the limited-display condition.
This indicates that participants tend to change their preference toward others’ preference
after seeing review and rating together.
Next, I want to share the results of the cognitive responses we measured.
We got these cognitive responses using different frequency bands of EEG signals.
Please refer to our paper for more detail on EEG signal processing.
The result I want to share is about emotional valence, which reflects levels of emotion
from positive to negative feelings.
We can see that participants have the most positive emotion in the full-display condition.
Previous research reported that people felt uncomfortable when seeing contrary opinions
from the majority and felt positive after they chose to go along with the majority opinions.
Since we also found that participants chose to conform to others more in the full-display
condition, we consider that this positive emotion we observed here is associated with
the social conformity effect.
For the final takeaways of this research, I want to share some design implications based
on our results.
Although displaying the ratings and reviews of others could induce social conformity,
showing collective social information is still a beneficial design feature in social computing
systems.
Instead of removing such information completely, we consider that it’s important to increase users' awareness
of undesirable social bias.
Given the significant effect of emotional valence on conforming decisions, we can help
users to be more aware of their emotional status when they are exposed to social information
by using mood tracker applications or affect sensing technology like smartwatches or even
commercial-grade EEG headsets.
With these assistances, users could identify the situations more easily where the collective
information is overwhelming rather than informative.
That’s all I’d like to share.
Thank you for listening.