  Understanding and mitigating political bias in online social media platforms
are crucial tasks to combat misinformation and echo chamber effects. However,
characterizing political bias temporally using computational methods presents
challenges due to the high frequency of noise in social media datasets. While
existing research has explored various approaches to political bias
characterization, the ability to forecast political bias and anticipate how
political conversations might evolve in the near future has not been
extensively studied. In this paper, we propose a heuristic approach to classify
social media posts into five distinct political leaning categories. Since there
is a lack of prior work on forecasting political bias, we conduct an in-depth
analysis of existing baseline models to identify which model best fits to
forecast political leaning time series. Our approach involves utilizing
existing time series forecasting models on two social media datasets with
different political ideologies, specifically Twitter and Gab. Through our
experiments and analyses, we seek to shed light on the challenges and
opportunities in forecasting political bias in social media platforms.
Ultimately, our work aims to pave the way for developing more effective
strategies to mitigate the negative impact of political bias in the digital
realm.
