  Politicization is a social phenomenon studied by political science
characterized by the extent to which ideas and facts are given a political
tone. A range of topics, such as climate change, religion and vaccines has been
subject to increasing politicization in the media and social media platforms.
In this work, we propose a computational method for assessing politicization in
online conversations based on topic shifts, i.e., the degree to which people
switch topics in online conversations. The intuition is that topic shifts from
a non-political topic to politics are a direct measure of politicization --
making something political, and that the more people switch conversations to
politics, the more they perceive politics as playing a vital role in their
daily lives. A fundamental challenge that must be addressed when one studies
politicization in social media is that, a priori, any topic may be politicized.
Hence, any keyword-based method or even machine learning approaches that rely
on topic labels to classify topics are expensive to run and potentially
ineffective. Instead, we learn from a seed of political keywords and use
Positive-Unlabeled (PU) Learning to detect political comments in reaction to
non-political news articles posted on Twitter, YouTube, and TikTok during the
2022 Brazilian presidential elections. Our findings indicate that all platforms
show evidence of politicization as discussion around topics adjacent to
politics such as economy, crime and drugs tend to shift to politics. Even the
least politicized topics had the rate in which their topics shift to politics
increased in the lead up to the elections and after other political events in
Brazil -- an evidence of politicization.
