Hello, everyone.
Myself, Binny Matthew.
I'm a final-year PhD student at IIT Kharagpur.
I'd be presenting the paper, Hate Begets Hate--
A Temporal Study of Hate Speech.
The work is co-authored with Anurag Illendula, Punyajoy
Saha, Soumya Sarkar, Pawan Goyal, and Animesh Mukherjee
from IIT Kharagpur.
Before I start, the content warning, the presentation
has occasional examples of highly
offensive, racist, bigoted, misogynistic and awful content.
With the increased usage of social media
platform, most which intend to cause harm to the community,
have been spreading at a faster rate.
Consider the example of the post by Robert Boas,
who was the main suspect of the Pittsburgh synagogue shooting.
He posted this message on Gab just before the shooting.
While such posts should not be allowed,
there has been growing debates on the freedom
of speech versus hate speech.
The question of whether a hate speech should
be allowed in the freedom of speech is a much debated one,
and it still continues.
In our work, we aim to study the temporal effects of--
on the community if such hate speech
is allowed without any restrictions.
Hate speech is considered as a direct and a serious attack
on any protected category of the people,
based on their character groups.
These include ethnicity, national origin, religion, sex,
gender, sexual orientation, disability, or disease.
In our study, we focus on Gab.com.
Gab is a social media website, which promotes itself
as a champion of free speech.
It has already been criticized as an echo chamber
for alt-right users.
Gab promotes free speech, allowing users
to pose hateful content.
Now, what we focused and proved is those questions primarily.
First, we studied how can we characterize the growth of hate
speech in Gab.
And secondly, how have the hate speakers
affected the Gab community as a whole.
To answer these questions, we developed pipelines
based on opinion dynamics model, which
would allow us to measure the degree of hate speech usage
by each user.
Overall, our pipeline consists of three steps--
generating temporal snapshots, heat intensity, and user
profiling.
I'll be explaining each of them.
In generating temporal snapshots,
we divide the data such that a particular snapshot
represents the activity of a particular month.
Next, hate intensity calculation--
we calculate the month-wise hate intensity
score of each user, which represents the hateful activity
of a user, based on his or her posts,
reposts, network connection for that particular snapshot.
Lastly, user profiling.
We profile the users, based on his or her temporal activity
of hate speech, which is at represented
by a vector of his or her timeline of hate intensity
scores.
Finally, based on this, we'll be able to answer the research
questions that we have placed over here.
First, we look into the amount of hate speech
that is present in the community.
So we brought the possibility of hateful posts,
with respect to the growth of community.
What we observed is that the amount
of hate speech in community has an increasing trend.
Again, we look into their time that the user
takes to make his first, second, and third hateful post.
We observed that as the community is growing,
the time required for such a thing is
decreasing for each user.
Thus, new users are becoming hateful at a much higher rate.
Next, to understand how the hate speakers have affected,
we looked at two different aspects.
The first is the network, and then the language.
So for the network part, we look into the acceleration
towards the core.
Here, we do a core periphery analysis
and identify how hateful and not hateful users have behaved.
What we observed is that in the initial time frame,
the non-hateful users were occupying
the core part of the network.
However, after [?] the fraction of hateful users
have surpassed the non-hateful one,
and they have occupied the core part of the network more.
We also look into the language models.
We use the language model to understand the language usage
by the community.
We observed that the language used by the Gab community
started getting closer to the language
of the users with high hate score,
as compared to the non-hateful users.
This may indicate that the Gab community as a whole
is having an increased correlation with the language
of the hateful users.
Based on these results, we suggest that the social media
platforms could gamify an incentive mechanism,
such as hierarchical badges, which
is similar to Stack Overflow, which
are provided to the users for counter speech initiatives.
Their platform could also provide interface tools
to group moderators and users to identify till activities
and take precautionary step.
This would allow the platform to stop
the spread of hateful messages at an early stage,
as well as allow them to monitor the growth of hate speech
as well.
Thank you very much for viewing.