Abstract:
Given an online forum, how can we quantify changes in user
affect towards a person or an idea over time? We argue that
online political forums constitute an untapped opportunity
for understanding sentiment toward aspects under discussion.
However, the analysis of such forums has received little attention
from the research community. In this paper, we develop
RAFFMAN, a systematic approach to quantify the impact of
external events on the affect of forum users towards a concept,
such as a person or an entity. First, we develop an approach
to capture and quantify the observed activity: we identify related
keywords, filter threads, and establish correlations between
events and spikes in the activity. Second, we modify
and evaluate state-of-the-art NLP techniques to achieve high
accuracy (74%) in a three-class sentiment classification problem.
As a case study, we deploy our method to quantify the
effect of President Trump’s impeachment on several concepts
including: President Trump, Speaker Pelosi, and QAnon. Our
data consists of 32M posts from Reddit and 4chan over a span
of 6 months from September 2019 to February 2020. This initial
analysis hints at an increase in political polarization, especially
for people’s affect towards the President. Overall, our
work is a building block towards mining the affect of online
forum user towards a concept, which constitutes a untapped,
massive, and publicly-available source of information.
