Hi, my name is Jinkyung Park.
I am a postdoctoral scholar at Vanderbilt University.
Today, I will present our work on how background images impact online incivility.
This research was done while I was a Ph.D. candidate at Rutgers University with my faculty
advisor, Professor Vivek Singh.
Incivility is a subjective and multifaceted construct.
One of the definitions is the features of discussion that convey an unnecessarily disrespectful
tone toward the participants or the discussion topic.
Online incivility can negatively impact users’ cognitions, emotions, and behaviors.
So, our research tried to find a way to effectively reduce online incivility.
Design intervention utilizes design ideas to generate individuals’ behavioral change.
Particularly, we applied a "calm" design intervention where the interaction between interventions
and users occurs in the user's periphery because unobtrusive intervention is under-studied
in incivility literature.
According to deindividuation theory, online incivility is frequent in anonymous settings
because people tend to lower self-regulation, and hence they become more likely to engage
in aggressive behavior.
The social identity model of deindividuation theory suggests that in anonymous settings,
people behave at the level of social group identity, hence, they are more likely to offend
out-group members.
The broaden and build theory of positive emotions suggests that positive emotions can increase
self-regulation, hence, make people less aggressive.
On a social level, positive emotions can lower the boundaries between us vs them.
As such, positive emotions may be effective in reducing aggressive behavior such as online
incivility.
Empirically, positive images are used to generate positive emotions.
In this project, we wanted to test whether being exposed to positive images would be
effective in reducing online incivility.
The first research question is does exposure to positive images help reduce online incivility?
Since online incivility is a multifaceted construct, we wanted to understand the context
of online incivility by addressing the second research question: how do user comments in
positive, and other conditions differ in terms of the direction of incivility, sentiment,
and topics discussed?
We created three online discussion forums with a positive background image in color,
a positive background image in grayscale, and no background image at all.
The background images were embedded except for the center part where people can read
and write comments to be less obtrusive.
We chose a positive-color image from the standardized affective image set and then we gray-scaled
the positive image.
We tested the experiment settings with 40 crowd-sourced participants to make sure that
the background settings actually look positive.
There were three stages in the experiment.
Pre-survey asked people’s demography, personality traits, political ideology, and issue opinion.
After the pre-survey, people were randomly assigned to one of the three experimental
conditions.
For the main intervention, participants in all conditions read a blog post describing
the Texas governor’s idea to build a wall in the border area to curve illegal immigration,
and some example comments taken from the Reddit forum.
After reading the post, they left one comment on the post on day 1 and left two more comments
on other participants' comments on day 2 and day 3.
They used a unique ID assigned by the researcher so that they can interact anonymously.
Each condition was a separate website so participants could not see user comments in other conditions.
After the intervention, participants filled out the post-survey to assess how uncivil
participants' comments were.
There were three open-ended questions asking whether they recognized the background images
and whether the background images changed their emotions and behaviors.
120 people were recruited on a crowdsourcing platform and 105 of them completed all three
stages of the experiment.
34 were in the positive color and gray conditions, respectively, and 37 were in the control condition.
The median age range of the participants was 25-34, 52% were female, 71% were white, and
49% had a bachelor’s degree.
First, we compare the mean values of the perceived incivility score across the three groups.
The results show that -the perceived incivility scores were lower
in the positive-color and the positive gray conditions and higher in the control condition.
20 participants from the positive-color condition and 12 from the positive-gray condition recognized
the background image meaning the positive-gray background image was less likely to be recognized
by the participants than the positive-color background image.
Participants mentioned that the positive-color background image prevented people from acting
rude, and they remember that they were sharing thoughts with another person, not an enemy.
They also mentioned that the positive-gray background gave them some relaxation and a
serene feeling.
Next, we compared the mean number of uncivil expressions in each comment across the three
conditions.
The mean incivility scores in the positive-color and positive-gray conditions were lower than
those in the control condition.
The most common direction of incivility was other-directed in all conditions.
However, the comments in the positive-color and the positive-gray conditions were more
likely to be impersonal and less likely to target other participants than those in the
control condition.
Next, we did the sentiment analysis to compare positive and negative emotions manifested
in user comments in three conditions.
The comments in the control condition manifested more positive and negative tones compared
to the positive-gray and positive-color conditions.
Finally, using the LDA topic model, we generated the five main topics discussed.
A chi-squared test confirmed that participants in the positive-color and the
positive-gray condition mentioned more about support for the immigrants and humanity approaches
to immigration issues, while participants in the baseline condition mentioned more about
US citizens’ financial burden for illegal immigration.
In summary, both positive-color and positive-gray background images were effective in reducing
perceived online incivility and uncivil expressions in the comments.
Given that positive-gray image was less likely to be recognized by the participants, positive-gray
images could be unobtrusive yet effective ways to reduce online incivility.
Also, uncivil expressions in the positive conditions were more often used to articulate
a controversial issue while uncivil expressions in the control condition were used to offend
other individuals.
This implies that positive emotions may have broadened the social cognition of the participants
in the positive conditions and they were less likely to leave uncivil comments toward other
participants.
Finally, the comments in the positive conditions showed more compassion toward the immigrants
than those in the control condition.
The results indicate that participants in a positive mood may have lower hostility toward
the illegal immigrants and, thus, may have seen the immigration issues from a humanitarian
perspective.
This study has some limitations.
The controlled setting of the experiment may have biased the participants’ behavior.
Another limitation could be a sampling bias.
The participants must have chosen to sign up for a crowdsourcing platform and to participate
in the study.
Thus, the results may not be the same for other populations.
Also, this study may have exposed participants to an uncomfortable experience.
And the background images could have also influenced their mood.
Despite the limitations, this study has important implications.
The study adds theoretical and empirical evidence to support the application of positive emotions
to counter online incivility.
It can also motivate social media designers to test other ways of exposing users to positive
emotions.
In addition, with the growing adoption of 360-degree content such as metaverse, the
"calm" technologies will be just as important as what is shown in the center and calm approaches
may also be explored to identify ways to reduce other forms of online risks.
Thank you for listening to my presentation.
I look forward to your questions and feedback.
Thank you.