Hello!
My name is Cassandra Kane, I am an undergraduate computer science student at Harvard University
alongside Elisa Zhao Hang and Nina Chen, and I am thrilled to present our group’s research:
Evaluating System Interventions Against Email Harassment.
According to Pew Research, in 2017, around 41% of Americans had personally experienced
online harassment, and among those victims, 10% had experienced their most recent incident
of harassment through email.
There is currently no automated way to robustly detect or flag harassment emails on popular
email platforms such as Gmail before the user views them in their inbox.
Individuals who rely on email as a central means of communication want to navigate their
email platform without being at risk of harassment.
The approach for our research was as follows.
To guide the development of our project, we explored existing work in related areas, including
the development of AI systems that can detect abusive language and interviews conducted
with victims of online harassment.
We also discovered that current approaches for email harassment management, including
services like HeartMob and SquadBox, primarily rely on community-based methods.
Our study seeks to examine automated platforms that reduce the logistical and emotional burden
of moderating abusive language in its entirety.
Our approach is a series of mixed-method user studies involving three different system intervention
strategies against harassment emails for the Gmail platform.
Our study design was as follows.
We recruited 19 participants for our study, all of whom were current or recently graduated
students from U.S. colleges.
Each of the three experiment groups included at least two participants who had prior experience
with online harassment.
For each interview, participants were first asked to complete an anonymized form that
collected information about their demographics, their email habits, and their online experiences.
Second, participants were asked to interact with the control email platform, i.e. the
standard Gmail platform.
Participants were instructed to interact with the platform as if it were their own while
describing their thought process out loud for each of the actions they executed.
Third, participants anonymously evaluated the control platform based on a number of
metrics surrounding safety, agency, and trust.
Fourth, participants were asked to follow the same procedure for one of three possible
prototype platforms.
Fifth, participants evaluated their prototype platform, and sixth, participants described
their general experiences using both platforms in a discussion-based debrief session.
This is the control platform all participants interacted with, which is the same as the
current standard Gmail platform.
This platform does not automatically detect or intercept harassment emails.
When participants first navigate to this platform, they view three simulated harassment emails
in their inbox.
Participants then interacted with one of three possible prototype platforms.
Prototype A-WARN implements a red warning label at the top of every email detected as
harassment by the AI system.
The warning label includes information as to why the email was detected as harassment
and further actions the user could take to report the sender.
Prototype B-TAG implements red warning labels next to emails detected as harassment within
the inbox, before the user opens the email.
Prototype B-TAG also includes the same red warning label from Prototype A-WARN within
the opened email.
Prototype C-FILTER implements a separate folder where emails detected as harassment are filtered
into, rather than remaining in the inbox.
Prototype C-FILTER also includes the in-email warning from Prototype A-WARN and the red
warning labels from Prototype B-TAG.
Here are our results and findings.
First, we noticed that users’ sense of safety and agency with each of the prototype platforms
increased compared to the control platform.
One participant stated that having the platform acknowledge that the received email was harassment
made them feel safer, and another appreciated how their prototype platform was more direct
about taking action.
Though system intervention was illustrated to benefit many users’ sense of agency and
trust, 37% of participants remarked that if the system were to misclassify emails often,
their sense of agency and trust with their email platform would be impacted significantly.
Across all of the prototype platforms, participants generally wanted more system intervention
against email harassment and more ways to manage the intervention.
Participants who interacted with the lower-intervention strategies often seeked features present in
the higher-intervention strategies, such as filtering.
While all of our prototype platforms provided a reporting link from the in-email warning,
21% of participants seeked more resources and protections against harassment from their
email platform.
79% of participants desired further control over how the system detected and managed harassment
emails.
Participants proposed some feature ideas for customizing email harassment management, such
as filtering emails by selected keywords.
Lastly, participants held a broad range of opinions as to whether they would prefer to
manage harassment emails themselves or to have the email platform manage harassment emails
on their behalf.
This range indicates how any system intervention designed to combat email harassment requires
some degree of flexibility and discreteness in order to avoid imposing on users’ pre-existing
routines and on their perceived relationship to their email platform.
Some areas for future development include exploring how user experiences with system
interventions against email harassment are affected by prior experiences with harassment,
system detection inaccuracies, and stronger intervention methods.
Thank you!